Podcasts about reverse etl

«Leadership is about sowing the common vision and the common way forward, bringing the people with you.»How can a nuclear physicist transform into a data leader in the industrial sector? Kristiina Tiilas from Finland shares her fascinating journey from leading digitalization programs at Fortum to shaping data-driven organizations at companies like Outokumpu and Kemira. Kristiina provides unique insights into navigating complex data-related projects within traditional industrial environments. With a passion for skydiving and family activities, she balances a demanding career with an active lifestyle, making her an inspiring guest in this episode.We focus on the importance of data competence at the executive level and discuss how organizations can strengthen data understanding without a formal CDO role. Kristiina shares her experiences in developing innovative digitalization games that engage employees and promote a data-driven culture. Through concrete examples rather than technical jargon, she demonstrates how complex concepts can be made accessible and understandable. This approach not only provides a competitive advantage but also transforms data into an integral part of the company's decision-making processes.Here are my key takeaways:The AI hype became a wake-up moment for Data professionals in Finland taking the international stage. As a leader in dat you need to balance data domain knowledge and leadership skills. Both are important.Leadership is important to provide an arena for your data people to deliver value.As a leader you are in a position that requires you to find ways of making tacit knowledge explicit. If not you are nit able too use that knowledge to train other people or a model.CDOThe Chief Data Officer is not really present in Nordic organizations.An executive role for data is discussed much, but in reality not that widespread.Without CDO present, you need to train somebody in the top leadership group to voice data.CDO is different in every organization.Is CDO an intermediate role, to emphasis Data Literacy, or a permanent focus?You can achieve a lot through data focus of other CxOs.Make data topics tangible, this is about lingo, narratives, but also about ways of communicating - Kristiina used gamification as a method.Creating a game to explain concepts in very basic terms with clear outcomes and structure can help with Data Literacy for the entire organization.Data in OT vs. ITPredictions and views on production should be able to be vision also in Operational Settings on all levels. There should not be any restriction in utilizing analytical data in operational settings.Security and timeliness are the big differentiators between OT and IT.These are two angles of the same. They need to be connected.IoT (Internet of Things) requires more interoperability.Extracting data has been a one way process. The influence of Reverse ETL on OT data is interesting to explore further.There are possibilities to create data driven feedback loops in operations.Data TeamsIf you start, start with a team of five: One who knows the data (Data Engineering) One who knows the businessOne who understands Analytics / AIOne who understands the users / UXOne to lead the teamYou can improve your capabilities one step at a time - build focus areas that are aligned with business need an overall strategy.If you expect innovation from your data team, you need to decouple them from the operational burden.Show your value in $$$.

I had an insightful discussion at Big Data Paris with David Bentham, VP GTM, DinMo on The Ravit Show to discuss how data teams can streamline their response to marketing requests – a key topic for any data-driven organization today! Here are some of the highlights: -- What do marketing teams need from data teams to drive their strategies? – David shared a breakdown of the essential data-driven insights that fuel effective marketing strategies. --Are there tools or processes that help meet these needs? – We explored the available options that make collaboration smoother between data and marketing teams. -- What is Reverse ETL, and why does it matter? – David explained Reverse EL's role in enabling real-time, actionable data for marketing. -- Should you buy or build your Reverse ETL? – A great discussion on the pros and cons to help teams decide on the best approach! Thanks to David and DinMo for the valuable insights and to Big Data Paris for hosting this vibrant community! Looking forward to seeing how data and marketing collaboration evolve. #data #ai #dinmo #theravitshow

Highlights from this week's conversation include:Ryan's Background and Roles in Data (0:05)Data Activation and Dashboard Staleness (1:27)Profiles and Data Activation (2:54)Customer-Facing Experience and Product Management (3:40)Profiles Product Overview (5:10)Use Cases for Profiles (6:44)Challenges with Data Projects (9:19)Entity Management and Account Views (15:33)Handling Entities and Duplicates (17:55)Challenges in Entity Management (22:18)Product Management and Data Solutions (26:08)Reverse ETL and Data Movement (31:58)Accessibility of Data Warehouses (36:14)Profiles and Entity Features (37:47)Cohorts Creation and Use Cases (41:17)Customer Data and Targeting (43:09)Activations and Reverse ETL (45:57)ML and AI Use Cases (55:53)Data Activation and ML Predictions (57:02)Spicy Take and Future Product Features (59:47)ETL Evolution and Cloud Tools (1:00:50)Unbundling and Future Trends (1:02:10)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Estamos de vuelta con una puesta al día y tenemos de todo: TikTok prohibido, el Privacy Sandbox atascado en la cocina, opinión sobre “Consent or Pay”, Meta AI vs. Google, Worldcoin congelado, Sora investigada, Teams/Office bajo la lupa, Avast vendiendo datos, multa a Glovo, proyecto de ley federal de protección de datos en EEUU… y mucho más. Todo ello en el post y casi todo comentado en las secciones de siempre. Con Cris Moro y Sergio Maldonado.  ePrivacy y marco regulatorio Multas y sanciones La AEPD ordenó a Worldcoin dejar de recabar datos biométricos con objetivos de identificación en un plazo de 72 horas por la vía de urgencia que en el GDPR permite saltarse el “one stop shop”. Worldcoin está basada en Alemania y había preparado el terreno con la autoridad bávara de protección de datos, pero aún así escogió España y Portugal como campo de pruebas. El proyecto ha generado importante alarma social, aparentemente recabando datos altamente sensibles sobre menores y adolescentes sin un propósito definido (“distinguir a humanos de robots”) y con la vinculación de perfiles a la aplicación móvil que permite acceder a criptomonedas o servicios futuros.  La AEPD, a petición de Garante (DPA italiana), impuso una multa de 550.000 euros a Glovo por no observar los principios más básicos en el tratamiento de los datos de repartidores. Se ha apreciado falta de transparencia (información facilitada en el registro inicial), privacidad desde el diseño, uso de decisiones automatizadas a través de un sistema de ranking/scoring que determina la asignación de cada pedido, y la transferencia a terceros fuera de los países en los que operan. Después de sufrir una multa de 16.5 millones de euros por parte de la FTC en Estados Unidos, la agencia checa de protección de datos ha impuesto una nueva sanción de unos 15 millones de euros al antivirus Avast por vender datos de navegación de sus clientes en el mercado publicitario, destacando sus afirmaciones falsas sobre la forma en que se anonimizaban los datos, y el uso exclusivamente estadístico de los mismos.  El abogado general de California anunció un acuerdo extrajudicial con DoorDash (reparto a domicilio), después de encontrarse una infracción del CPPA y CalOPPA por la participación de la plataforma en una cooperativa de intercambio de datos (“Second Party Data”), siendo esto equivalente a una venta de datos personales -y exigiendo un “opt-in”- en el sentido de la propia CCPA.  La AEPD impuso multas de 10.000 euros tanto a La Vanguardia como a NH Hoteles por violaciones en el uso de cookies. El medio de prensa fue sancionado por no proporcionar información clara y completa sobre el uso de cookies, mientras que la cadena hotelera fue multada por usar cookies no exentas, propias y de terceros sin consentimiento, además de no permitir rechazar o gestionar las cookies de manera granular. Se ha concedido una rebaja del 20% a esta última por estar en proceso de actualización de estos aspectos en su web.  El mes pasado Garante, la DPA italiana, anunció que estaba investigando a Sora (texto a vídeo), y solicitó información sobre sus fuentes de entrenamiento (ha circulado un vídeo en el que una consejera de OpenAI confesaba hacer uso de todo el catálogo de YouTube), y el uso de datos personales en ese proceso. Se le han pedido categorías de datos personales, fuentes y bases legales. También en marzo, el EDPS le pidió a la Comisión Europea que deje de usar Microsoft365 -que viene a ser Office, Teams, y todo el kit de productividad de Microsoft- por no haber analizado bien el marco contractual que permite a esta empresa tratar datos en Estados Unidos. El EDPS ha explicado que la Comisión Europea no ha proporcionado las medidas adecuadas para garantizar que los datos personales transferidos fuera de la Unión Europea cuenten con un nivel de protección equivalente (después de Schrems II). Además, tampoco se ha detallado qué tipo de datos han sido compartidos con Microsoft y otras compañías asociadas. El EDPS ha impuesto la obligación de suspender todos los flujos de datos derivados del uso de Microsoft365 a la Comisión Europea a partir del día 9 de diciembre. El EDPB publicó finalmente su opinión sobre “consentimiento o pago” el pasado 17 de abril, como continuación a la cuestión planteada por varias agencias en el contexto de la opción ofrecida por Instagram y Facebook (Meta), análoga a la recientemente desplegada por los grandes medios de comunicación. Hemos debatido el asunto largo y tendido en varias entrevistas del canal en inglés de este podcast. Novedades legislativas Como continuación a una ley propuesta por el congreso de EEUU para prohibir TikTok en el país, y cuando parecía que no superaría la aprobación del Senado, la iniciativa terminó votándose y aprobándose de forma conjunta al paquete de ayudas a Ucrania e Israel, terminando firmada por Joe biden el 24 de abril y resultando en una venta forzosa (o su prohibición) en el plazo de nueve meses que podrían extenderse a doce.  Antes de eso, el 25 de marzo, el Gobernador de Florida (Ron de Santis) firmó la nueva House Bill 3 (“HB3”), que se une a un debate muy candente al prohibir a los menos de 14 años abrir una cuenta en Instagram, Snapchat u otros medios sociales, exigiendo además consentimiento parental para los menores de 16. Esta ley exige además que se eliminen las cuentas existentes de menores.  El 7 de abril se presentó un proyecto histórico de ley federal sobre privacidad en Estados Unidos. La American Privacy Rights Act establece derechos claros y nacionales de protección de datos para los estadounidenses, eliminando el actual mosaico de leyes estatales y estableciendo un derecho de acción privada para los individuos. MarTech y AdTech En el mercado ampliamente cubierto aquí de Data Clean Rooms (DCR), LiveRamp compró Habu y Snowflake había comprado Samooha anteriormente. Recientemente hemos entrevistado a Matthias Eigenmann, DPO de Decentriq, solución apoyada en Computación Confidencial. También hemos hablado con Damien Desfontaines, de Tumult Labs, sobre “privacidad diferencial” aplicada a DCRs en el caso de uso de análisis de datos combinados de dos responsables del tratamiento.  En paralelo sigue avanzando el concepto del Reverse ETL (Extract, Transform, Load), que ahora se rebautiza como Customer Data Platform modular, donde la nueva generación de data warehouses permite que las funcionalidades de activación de datos estén erigidas sobre éstas, en vez de exigir un repositorio completo e independiente (o redundante) como ha venido ocurriendo con los Customer Data Platforms en los últimos siete años aproximadamente. Aquí hemos entrevistado al CEO de Hightouch, Tejas Manohar, una empresa líder en esta tecnología. Esta misma semana Google ha anunciado que vuelve a retrasar el fin de las cookies de tercera parte por no darle tiempo a introducir las medidas exigidas por la autoridad de mercados y competencia del Reino Unido. El equipo del Privacy Sandbox sigue colaborando con la comunidad para solucionar algunos aspectos bastante pobres de la medición de resultados o la optimización de la publicidad bajo los nuevos estándares. IA, competencia y mercados digitales  A mediados de febrero, OpenAI presentó una “función de "memoria” en ChatGPT, lo que generó preocupaciones sobre la protección de datos de sus usuarios a pesar de los diversos controles individuales proporcionados para la eliminación de dicha memoria. Poco después, la misma empresa lanzó una herramienta "texto-a-video" llamada Sora. Para contrarrestar el aumento del riesgo de infracción de derechos de autor, desinformación y "deep fakes", OpenAI anunció que había incorporado el estándar de la Coalición para la Procedencia y Autenticidad del Contenido (C2PA), que muchos expertos consideraron insuficiente. Meta ha lanzado su nuevo modelo genérico de IA generativa, Llama 3, capaz de competir con la última generación de alternativas ofrecidas por OpenAI, Google, Anthropic o Mistral. Como gran novedad, la empresa ha integrado su propio agente, “Meta AI” en todas sus plataformas - comenzando con múltiples países angloparlantes. Los analistas comienzan a especular con que la reciente caída en bolsa de la empresa por el aumento exponencial de su inversión en IA (incluido su propio hardware) podría obtener un premio a largo plazo si consigue reemplazar a la propia Google en la búsqueda de respuestas directas desde aplicaciones de uso tan cotidiano como WhatsApp.  PETs y Zero-Party Data Signal ha introducido nombres de usuario en el canal de mensajería, permitiendo con ello ocultar números de teléfono en la popular alternativa a WhatsApp y Telegram.  La más reciente alternativa a X/Twitter, Bluesky, ha dejado atrás el requisito de invitación, reportando un crecimiento exponencial en volumen de usuarios y anunciando un sistema modular de gestión de “feeds” y filtros de contenido.  Futuro de los medios Del mismo modo que ya lo había hecho con Axel Springer (Der Spiegel) en Alemania, OpenAI ha firmado acuerdos con El País y Le Monde para facilitar el acceso a noticias en castellano y francés a través de ChatGPT. OpenAI se ha comprometido a facilitar resúmenes, atribución de fuentes y links a las fuentes originales, y estamos asumiendo que también podrán hacer uso de sus archivos históricos a efectos de entrenamiento en castellano y francés.  

Tejas Manohar is the co-founder and co-CEO of Hightouch. Prior to founding Hightouch, Tejas was an early engineer at Segment, a leading Customer Data Platform (CDP) acquired by Twilio.  The following topics have been covered in this interview: Current limitations of Customer Data Platforms (CDP) as a core building block of the marketing data stack The value of composable CDPs and Reverse ETL Privacy compliance challenges of CDPs and customer data integration as a whole Potential overlaps with Data Clean Rooms References: Tejas Manohar on LinkedIn Traditional CDP vs. Composable CDP: What is the difference? Revenge of the silos: How privacy compliance is cutting the customer journey short (Sergio Maldonado)

Summary The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. The reality was that it left data teams in the position of spending all of their engineering effort on integrating systems that weren't designed with compatible user experiences. The team at 5X understand the pain involved and the barriers to productivity and set out to solve it by pre-integrating the best tools from each layer of the stack. In this episode founder Tarush Aggarwal explains how the realities of the modern data stack are impacting data teams and the work that they are doing to accelerate time to value. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm welcoming back Tarush Aggarwal to talk about what he and his team at 5x data are building to improve the user experience of the modern data stack. Interview Introduction How did you get involved in the area of data management? Can you describe what 5x is and the story behind it? We last spoke in March of 2022. What are the notable changes in the 5x business and product? What are the notable shifts in the data ecosystem that have influenced your adoption and product direction? What trends are you most focused on tracking as you plan the continued evolution of your offerings? What are the points of friction that teams run into when trying to build their data platform? Can you describe design of the system that you have built? What are the strategies that you rely on to support adaptability and speed of onboarding for new integrations? What are some of the types of edge cases that you have to deal with while integrating and operating the platform implementations that you design for your customers? What is your process for selection of vendors to support? How would you characterize your relationships with the vendors that you rely on? For customers who have pre-existing investment in a portion of the data stack, what is your process for engaging with them to understand how best to support their goals? What are the most interesting, innovative, or unexpected ways that you have seen 5XData used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on 5XData? When is 5X the wrong choice? What do you have planned for the future of 5X? Contact Info LinkedIn (https://www.linkedin.com/in/tarushaggarwal/) @tarush (https://twitter.com/tarush) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links 5X (https://5x.co) Informatica (https://www.informatica.com/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Looker (https://cloud.google.com/looker/) Podcast Episode (https://www.dataengineeringpodcast.com/looker-with-daniel-mintz-episode-55/) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) Redshift (https://aws.amazon.com/redshift/) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) Rudderstack (https://www.rudderstack.com/) Podcast Episode (https://www.dataengineeringpodcast.com/rudderstack-open-source-customer-data-platform-episode-263/) Peak.ai (https://peak.ai/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

What's up everyone, today we're taking a deep dive into customer data and the stack that enables marketers to activate it. We'll be introducing you to packaged customer data platforms and the more flexible options of composable customer data stacks and getting different perspectives on which option is best.I've used both options at different companies and have had the pleasure of partnering with really smart data engineers and up and coming data tools and I'm excited to dive in.Here's today's main takeaway: The debate between packaged and composable CDPs boils down to a trade-off between out-of-the-box functionality and tailored flexibility, with industry opinions divided on what offers greater long-term value. Key factors to consider are company needs and data team size. But if you do decide to explore the composable route, consider tools that focus on seamless integration and adaptability rather than those who claim to replace existing CDPs.The 8 Core Components of Packaged CDPs: What the Experts SayOkay first things first, let's get some definitions out of the way. Let's start with the more common packaged CDPs.A Customer Data Platform (CDP) is software that consolidates customer data from various sources and makes it accessible for other systems. The end goal is being able to personalize customer interactions at scale. I've become a big fan of Arpit Choudhury of Data Beats, he articulates the components of a packaged CDP better than anywhere I've seen in his post Composable CDP vs. Packaged CDP: An Unbiased Guide Explaining the Two Solutions In Detail. 8 packaged CDP components: CDI (Customer Data Infrastructure): This is where you collect first party data directly from your customers, usually through your website and apps. ETL (Data Ingestion): Stands for Extract, Transform, Load. This is about pulling data from different tools you use and integrating it into your Data Warehouse (DWH). Data Storage/Warehousing: This is where the collected data resides. It's a centralized repository. Identity Resolution: This is how you connect the dots between various interactions a customer has with your brand across platforms and devices. Audience Segmentation: Usually comes with a drag-and-drop user interface for easily sorting your audience into different buckets based on behavior, demographics, or other factors. Reverse ETL: This is about taking the data from your Data Warehouse and pushing it out to other tools you use. Data Quality: This refers to ensuring the data you collect and use is valid, accurate, consistent, up-to-date, and complete. Data Governance and Privacy Compliance: Ensures you're in line with legal requirements, such as user consent for data collection or HIPAA compliance for healthcare data. So in summary: Collect first party data and important data from other tools into a central database, id resolution, quality and compliance, finally having a segmentation engine and pushing that data to other tools.I asked recent guests if they agreed with these 8 components.Collection, Source of Truth and SegmentationBoris Jabes is the Co-Founder & CEO at Census – a reverse ETL tool that allows marketers to activate customer data from their data warehouse.When asked about his definition of a packaged CDP, Boris elaborated on the role these platforms have carved for themselves in marketing tech stacks. To him, packaged CDPs are specialized tools crafted for marketers, originally in B2C settings. Their primary utility boils down to three main functions: data collection, serving as a reliable data source specifically for the marketing team, and data segmentation for targeted actions.The ability to gather data from various customer touchpoints, such as websites and apps, is crucial. These platforms act as the single source of truth for that data, ensuring that marketing teams can trust what they're seeing. Finally, they provide the capability to dissect this data into meaningful segments that can be fed into other marketing tools, whether that's advertising platforms or email marketing solutions.Though Boris mentioned the term “DMP,” it's essential to differentiate it from a CDP. Data Management Platforms (DMPs) have historically been tied to advertising and don't provide that rich, long-term profile a CDP can offer. The latter offers a more holistic view, allowing businesses to target their audience not just based on advertising metrics but on a more comprehensive understanding of consumer behavior.Key Takeaway: Packaged CDPs are functional units that collect, validate, and segment data for marketing utility. If you're considering implementing an all-in-one CDP, look for these three core features: comprehensive data collection, a single source of truth for that data, and robust segmentation capabilities.Adding Predictive Modeling to Packaged CDPsTamara Gruzbarg is the VP Customer Strategy at ActionIQ – an enterprise Customer Data Platform.When asked about her stance on 8 components of a packaged CDP, Tamara generally concurred but added nuance to each element. Starting with data collection and ending with data activation, she emphasized the critical nature of these components. Tamara also advocated for the necessity of drag-and-drop UI for audience segmentation, which paves the way for data democratization and self-service.Going beyond mere segmentation, Tamara revealed that her platform offers insights dashboards. These aren't just Business Intelligence (BI) tools; they help marketers understand segment overlaps and key performance indicators, which further empower them to design more efficient campaigns. Her approach involves offering two types of audience segmentations: rule-driven and machine learning (ML) driven. The latter is a distinct component that allows clients to construct audiences based on predictive models, and it's an option that has gained traction especially among mid-market businesses.Tamara also touched upon a salient point regarding large enterprises. Even these giants can benefit from predictive tools when dealing with new data sets they hadn't previously accessed. Collaboration with their in-house data science teams ensures the quality and reliability of this predictive modeling.Key Takeaway: A well-designed CDP should not just offer data collection and segmentation but also facilitate data activation and provide actionable insights. Whether you're a large enterprise or a mid-sized business, the predictive modeling feature in some modern CDPs offers a fast track to gain valuable insights into your audience. Keep an eye out for these extended functionalities when evaluating a CDP for your business.The Importance of Data Quality and GovernanceMichael Katz is the CEO and co-founder at mParticle, the leading packaged Customer Data Platform.When asked about his agreement with the often-cited eight components of a packaged Customer Data Platform (CDP), Michael did more than just nod in approval. He concurred that these elements are, at a minimum, the pillars of first-generation CDPs. Yet, he warned that very few platforms are strong across all these functionalities, giving his own platform as an exception for its comprehensiveness. According to Michael, a robust CDP is not just a collection of features but an integrated system where the entire value is greater than its individual parts.Diving deeper into the conversation, Michael addressed a common shortfall in the CDP landscape—data quality and data governance. Many platforms, he noted, lack robust features in these areas. The result is an unstable foundation that undermines the value proposition of a CDP. In Michael's words, the real magic happens when you can move from the data collection phase through to the data activation layer without compromising on quality and governance.Michael also highlighted a nuanced point that often gets overlooked: the speed at which you can push data out into your application layer must be balanced with maintaining data quality and consumer privacy protection. It's not just about how fast you can move; it's about how fast you can move responsibly.Key Takeaway: When evaluating a CDP, don't just look for a checklist of features. Look for an integrated system that's strong in areas often neglected by others, such as data quality and governance. Speed is important, but not at the cost of quality and consumer privacy. Your CDP should offer more than just rapid data transfer; it should provide a stable, comprehensive platform for making that data actionable.The Main Event: Harnessing and Activating DataTejas Manohar is the Co-founder and Co-CEO at Hightouch, another reverse ETL tool, that's taken a bit more of a controversial stance.When asked about the 8 components of a packaged CDP, Tejas broke ranks. He neither agreed nor disagreed with the elements but instead shifted the focus to the real question: Why do companies seek out a Customer Data Platform in the first place? According to Tejas, it's primarily about harnessing and activating customer data to personalize experiences and drive better outcomes. Everything else, in his view, is ancillary.In a field cluttered with feature lists and component breakdowns, Tejas urged companies to simplify. He distilled the CDP's core functionality into three primary aspects. First, the platform must offer a mechanism for data collection. Second, it needs to provide some form of data transformation; think identity resolution and modeling. And third, it should facilitate data activation, typically through audience building and integrations.For Tejas, the exhaustive lists of features and components often discussed in the martech space are merely a means to an end. Companies shouldn't get lost in the weeds of features or components; instead, they should focus on what a CDP is fundamentally designed to achieve. Tejas argues that it's not about ticking boxes on a feature list but about how these features contribute to the ultimate goal of using data effectively.Key Takeaway: Don't get sidetracked by a long list of features or components when evaluating a CDP. Keep your eye on the main event: harnessing and activating data to improve customer experiences and business outcomes. Simplify your approach, and focus on the core functionalities that will help you reach your objectives.So What's the Hype Around Composable CDPs?Thought of as the new kid on the block, composable CDPs promise a lot of different things compared to the packaged option. Composable CDPs take a modular approach to data management, built from separate, easily interchangeable parts. This design offers finer control over data processes and can be customized to fit particular business objectives. They provide a contrast to packaged solutions, balancing specialized benefits against workflow complexity.Example tools/setup: CDI: Snowplow ETL: Airbyte DWH: BigQuery Reverse ETL: Census Data quality: dbt But not everyone sees the composable route as an entirely new thing.Drawing Parallels: Composable CDPs and the Lessons from Headless CommerceDavid Chan is Managing Director at Deloitte Digital and leads their CDP practice.When asked about the buzz surrounding composable Customer Data Platforms (CDPs), David turned the spotlight on a parallel from his own background—headless commerce. Originating around 2013-2015, headless commerce was a game-changing moment that separated web content management from the commerce tools themselves. In this setup, the content management system functioned as the front end, while the commerce tools handled the heavy-duty logic like checkouts and product details. David observed that this shift towards modularity in the commerce space was an early sign of how composability could transform industries.David then dissected the current state of composable CDPs, comparing it to the early days of headless commerce. The crux of the issue, he said, lies in integration. While today's CDP landscape is flush with features and capabilities, it's noticeably lacking a unified framework for how these components should interact. This fragmentation echoes the initial phases of headless commerce, where disjointed systems eventually gave way to more standardized, interoperable solutions.What sets the CDP space apart right now, according to David, is the absence of those well-defined standards and partnerships that can guide the development of composable architectures. The commerce space underwent a similar period of “mashing and banging,” where different features and tools were reluctant to work in concert. Eventually, standards emerged that dictated how these composable elements should fit together. This level of structure, David argues, is still conspicuously absent in the world of CDPs.Key takeaway: Composable CDPs are still in their formative stages. But given the trajectory witnessed in headless commerce, it's only a matter of time before these platforms evolve to include more standardized, collaborative frameworks. That's what will take them from being a collection of features to a cohesive system, just like headless commerce did years ago.Tap Into Existing Data in Your WarehouseWhen asked about the shift toward composable CDPs, The Co-CEO of Hightouch explained that while the demand for CDPs is high, the satisfaction derived from most available solutions leaves something to be desired. Tejas cited a Gartner report indicating that a mere 60% of organizations find their CDPs valuable. The issue isn't with the CDP concept, but rather with its traditional execution of making a copy of your data—hence the rising interest in composable CDPs.Tejas contends that composable CDPs offer a much-needed alternative. These platforms are designed to tap into the extensive data already stored in an organization's data warehouses. This approach integrates existing data pools, breaking down data silos, and making it accessible to marketing teams. The result is a more practical and efficient way to activate personalized customer journeys.The push toward composable CDPs, then, isn't just a passing fad. It's a meaningful evolution aimed at resolving real-world dissatisfaction with older CDP models. By enabling marketers to seamlessly leverage existing organizational data, composable CDPs stand to make the concept of a CDP not just aspirational but genuinely functional.Key takeaway: The movement toward composable CDPs is rooted in the need for a different data architecture and utilization of existing data. While traditional CDPs sometimes fall short of delivering on their promise, composable CDPs aim to make existing organizational data accessible and actionable for marketers.The Need to Adapt to Complex Customer Journeys and Regulatory DemandsWhen asked about the factors motivating the industry's move towards composable Customer Data Platforms (CDPs), the Co-founder and CEO of Census explained that it wasn't merely a matter of opposing the traditional CDPs. Instead, the focus was on first principles, aiming to provide marketers with more trustworthy data. Boris emphasized that existing data storage solutions, like data warehouses from Google, Snowflake, Amazon, or DataBricks, already hold extensive and infinitely flexible data sets. The question then becomes, why duplicate these resources?Composability, Boris shared, isn't about disassembling systems but about creating components that can seamlessly work together. This approach allows businesses to customize parts of the system without disrupting its overall functionality. Traditional CDPs tend to fall short because they can't offer the level of flexibility modern businesses require, particularly as customer journeys become more complex and multi-faceted.Boris also discussed the increasing complexity in customer journeys, pointing out that marketing has evolved significantly from the times when placing a pixel on a website would solve most tracking issues. Today, especially in the B2B sector, customer relationships and touchpoints are more varied and complicated than ever before.Lastly, Boris touched on emerging regulatory demands. Marketers now have to navigate complex privacy requirements. Whether it's the EU or California, companies are expected to be more transparent about data collection, storage, and usage. This shift makes first-party data and its proper governance crucial, adding another layer of complexity to an already intricate landscape.Key takeaway: The shift towards composable CDPs isn't just a reactionary move against traditional platforms; it's an evolution driven by a need for more reliable data, increased flexibility, and the capability to adapt to complex customer journeys and regulatory demands. By focusing on composability, companies can harness their existing data infrastructure to build more agile, adaptable systems.Debating the Merits of Composable Versus Packaged CDPsOkay so we've covered the components and the definition of a packaged CDP and why there's a need for some companies to explore the more flexible route of a composable stack. Let's hear from various different industry pros about where they side when it comes to the packaged vs composable CDP battle. Choosing the Right Customer Data Platform: Flexibility vs. Cost in the CDP DebateWyatt Bales is Chief Customer Officer at Bluprintx, a global Growth-as-a-Service consultancy who provide Martech, Salestech, and Work Management solutions.When asked about his stance on the debate between packaged and composable Customer Data Platforms (CDPs), Wyatt shed light on some crucial considerations. He noted that for some companies, the ongoing licensing costs of Segment have become a long-term burden. Wyatt referenced a data integrity customer in Belgium, as an example of a company benefiting from a different approach. Instead of operating on a traditional CDP, they use a data warehouse like Snowflake to gauge the quality of their data.Wyatt emphasizes that modern data warehouses have evolved to offer a wide array of tools. These tools, which sit atop the warehouse, serve as insightful indicators of what kind of data you're dealing with. Whether it's about understanding data cleanliness or complexity, the warehouse can act as a hub for diverse data operations. This kind of flexibility makes warehouses an increasingly attractive option for companies looking beyond traditional CDPs.The discussion then veered into the realm of API calls for tasks such as email delivery and campaign execution. Wyatt is convinced that, particularly for the enterprise space, the future lies in leveraging data warehouses for these outbound tasks. The inherent adaptability of warehouses allows for easier integration of various functionalities, offering a nuanced, practical approach to handling customer data.Key takeaway: It's not about choosing one type of CDP over another but understanding your specific needs and options. Companies may find that the flexibility and scalability of modern data warehouses make them a suitable, if not superior, alternative to traditional CDPs.Why Data and Messaging Integration Matters in the Packaged vs Composable DebatePini Yakuel is the CEO of Optimove, a platform that combines a Customer Data Platform (CDP), a journey orchestration tool and an AI engine.When asked about the ongoing debate between packaged and composable CDP and martech solutions, Pini emphasized the importance of contextualizing each company's unique needs. He argued that for many businesses, the true value of data lies in its proximity to messaging channels. In this setup, data isn't just a dormant entity waiting for analytics; it actively informs real-time decisions to improve customer interactions.Pini pointed out a common pitfall: the fragmentation of data and channels. While some tools may excel at data management, they often export that data to another tool responsible for messaging. This can create a disconnection between data analytics and actionable insights. The exported data is fed into a system that remains, at its core, rule-based rather than data-driven.Diving deeper into the importance of integrating data with decision-making, Pini indicated that when data and channels share the same platform, they enable an “AI feedback loop.” This is not just about smarter segmentation; it's about making the entire system inherently smarter. An integrated platform can be adaptive, not just reactive. Such a setup cannot be easily replicated by stringing together APIs from different systems because the latter approach doesn't change the fundamental nature of those systems—they remain rule-based.Closing out his argument, Pini revealed the mindset driving his company's approach to solving this issue. To truly unlock the power of data, they maintain an “obsession” with solving this particular problem. Their unwavering focus enables them to slowly piece together a more comprehensive and optimized solution where data and channels coexist in a virtuous cycle.Key takeaway: The debate between packaged and composable CDP and martech tools isn't about one being universally better than the other. It's about understanding that the real power comes from aligning your choice with your specific needs and goals, especially when it comes to integrating data and messaging channels for actionable insights.Choosing Flexibility and Innovation: The Case for Composable over PackagedArun Thulasidharan is the CEO & Co-founder at Castled.io – A warehouse-native customer engagement platform that sits directly on top of cloud data warehouses.To him, the core difference between composable and packaged CDPs resembles the contrast between open source and closed source systems. A composable CDP, built atop a data warehouse, bestows the flexibility to innovate. If you find something lacking, you're not confined; you can add more tables or transformations to the system.Arun emphasizes that this flexibility is not just theoretical; it's practically beneficial. He brings into play real-world examples, citing tools that perform identity resolution on top of a data warehouse. These tools employ fuzzy logic, rather than deterministic methods, to identify that two rows of data might actually be related. In doing so, they enable a new kind of innovation—one that can only occur in an open system, directly on the data warehouse.In contrast, packaged CDPs often restrict this level of flexibility. They operate in a closed system, limiting your ability to introduce new functionalities or plug in external tools. To Arun, this lack of adaptability can stifle the innovations that are currently shaping the martech industry.Yet, Arun acknowledges that the discussion isn't black and white. There are compelling arguments for both sides, but his preference leans toward the composable model for its adaptability and the freedom it offers for innovation.Key takeaway: Flexibility is currency in today's martech landscape. Opting for a composable CDP over a packaged one can provide you the elbow room to innovate and adapt, positioning you at the forefront of industry advancements.Cloud Data Warehouses, Data Strategy, and the Real Value of CDPsOnce again, let's get thoughts from Michael Katz, the CEO of mParticle (packaged CDP) about how he genuinely feels about the packaged vs composable CDP debate.MK asserted that the dialogue around it is often reduced to noise—distracting from the core issue. To him, the evolution of CDPs is not a luxury but a necessity, paralleling the demands of any growing business. He cut through the chatter to highlight the critical role of Cloud Data Warehouses, noting they serve as an organization's single source of truth, at least in theory.However, MK acknowledged that simply setting up a data warehouse doesn't solve all problems. He emphasized the critical need for a robust data strategy and mechanisms to ensure data quality and integrity. The challenges don't stop at data collection; they extend to navigating an ever-changing landscape of privacy regulations. MK clarified that the value provided by legacy CDP vendors like mParticle is not merely in data storage but in the movement and activation of data.MK also argued that the real evolution in CDPs is away from basic segmentation tools toward more nuanced ‘journey tools.' These not only collect data but offer a greater understanding of that data—providing context and insights. He shared that his focus over the past year and a half has been to move beyond just verifying the data's truth to finding its meaning. Whether it's looking back to understand what happened or looking forward to predict future outcomes, the goal is an ‘infinitely optimizing loop.'Not mincing words, MK criticized the strategy of companies offering reverse ETL solutions. He labeled their approach as “garbage in, garbage out,” cautioning that a quicker path doesn't equate to better results if you have garbage data in the first place. He also tackled what he perceives as distracting tactics—myths about zero data copy, unfounded security concerns, and misleading narratives on deployment times. MK pointed out that initial value and sustained value are not the same; what is easy to initiate is often difficult to maintain in the long run.Key takeaway: Discarding the noise is the first step to understanding the real value of CDPs. It's not just about having a data strategy; it's about continuously refining it to move from data storage to data activation and insights. MK warns against the allure of quick fixes and emphasizes that true value in the data space is a long game, demanding a robust strategy and the right tools.Understanding the Distinct Roles of CDPs and Reverse ETLs in Marketing StrategyPratik Desai, is the Foudner and CEO of 1to1, a personalization agency that works with enterprise clients and has recently released a product called Ragana, a composable search and sort personalization engine built on top of your eComm platform. When asked about the tension between packaged and composable Customer Data Platforms (CDPs), Pratik identifies a core issue: the debate often stems from a misunderstanding of what Reverse ETL tools are actually doing. He explains that marketers are sometimes sold on Reverse ETLs as if they're a one-for-one substitute for CDPs. That's misleading. Reverse ETLs and CDPs are solving different problems. CDPs, for instance, excel in identity resolution, a feature Reverse ETLs don't offer.Pratik digs deeper into the structural gaps that led to the rise of CDPs. Historically, marketing teams were often sidelined when it came to data strategy. CDPs emerged as a tool to give marketers a “seat at the data table.” Reverse ETL tools have value, but they won't inherently solve this organizational disconnect. Buying a new tool won't suddenly align your marketing and data teams if those teams weren't aligned in the first place.Switching focus to enterprise-level challenges, Pratik highlights the importance of operational excellence and data structure. The adoption of a reverse ETL tool won't automatically resolve operational inefficiencies or integrate marketing into broader data strategy. It's not a silver bullet for organizational issues.Pratik ends by urging businesses to examine their unique problems before leaping into any tech solutions, whether it's a CDP or a Reverse ETL. Some organizations, particularly SMBs where marketing already has data influence, can extract enormous value from reverse ETL tools. However, Pratik warns that we're a long way from a one-size-fits-all solution, especially for enterprise-level customers.Key takeaway: Understand your organization's specific challenges before diving into CDPs or Reverse ETLs. These tools are not interchangeable; they solve distinct problems. Align your teams and clarify your data strategy first—only then can you effectively leverage these technologies.Can Reverse ETL Really Replace Packaged CDPs?So let's talk about the confusion in the market. Can reverse ETL actually replace a packaged CDP?Most of the confusion stems from one Reverse ETL vendor in particular: Hightouch. They've written plenty of controversial articles claiming that the CDP is dead and that they can replace it. I sat down with Tejas, the Co-founder and Co-CEO to get to the bottom of why they think they can replace the packaged CDP.Is the CDP Really Dead?Tejas mentioned that large Hightouch customers like Blizzard Activision and Warner already refer to their platform as a CDP (at elast internally). But what makes Tejas' perspective intriguing is that their product doesn't fit the typical mold of a CDP.The core of Tejas' viewpoint rests on the activation of marketing data. He emphasizes that the ultimate differentiator in this space isn't just the collection of data, but how effectively a company can activate and personalize that data. Tejas hints that traditional CDPs often fall short in this area. While they collect mountains of data, they lack in providing actionable insights and seamless data activation for marketing teams.Tejas went on to address a bold prediction made in their company's blog post, stating “CDPs are dead.” He argues that the CDPs of the future will either adapt to the flexible, data activation-centric model their company has pioneered or risk becoming obsolete. In Tejas' eyes, they are shaping the future of the CDP landscape by focusing on what matters the most—enabling companies to own their data, offering infinite flexibility, and allowing data activation across all channels.So some large customers do refer to Hightouch as their internal CDP. Is that enough to be able to claim that the CDP is dead and that they can replace it? I asked Michal Katz for his take on Tejas' argument.Challenging the DIY Approach to CDPsWhen asked about Tejas' claim that some customers refer to their platform as an internal CDP, MK offered a nuanced take. He argues that this viewpoint represents a narrow segment of the market. For Michael, the fragmented DIY (Do It Yourself) approach, often favored by data engineers, falls short in delivering business value, especially for enterprises. MK warns that the “day of reckoning is coming,” as sloppy habits have been formed, particularly during the pandemic. According to MK, these habits often stem from data engineers operating without proper business requirements, resulting in suboptimal digital marketing campaigns.MK points out a significant shift that has occurred over the past 9-12 months—marketers are reclaiming power from data engineers. mParticle is built with marketers in mind, focusing on low-code or no-code data activation. MK notes the importance of usability in delivering value, contrasting their approach with some of the more complex CDPs. Hes emphasize that their platform allows for easy data contextualization and activation, all through a point-and-click user interface.Beyond usability, MK makes a case for integrated platforms, pushing against the notion of using different components for an end-to-end CDP. He highlights the challenges of troubleshooting across multiple systems, especially when things go awry. According to MK, managing across different platforms introduces unnecessary complexities and slows down the ability to deliver business value.MK concludes by stressing that while there may be many paths to value creation, the quickest is usually the most straightforward. In his view, there's considerable value in using an integrated platform where seamless workflows are a given and customization is an option but not a necessity.Not All Reverse ETL Tools Aim to Replace the CDPNot all Reverse ETL platforms have taken a loud controversial approach to marketing though. I asked Boris Jabes, the CEO and Co-founder of Census if his platform replaces a CDP, he emphatically said no.He explained that many of his customers use his product, Census, alongside a traditional CDP. Far from making these platforms obsolete, Boris' goal is to give users access to trustworthy data across multiple locations. His focus is on composability—a philosophy that emphasizes building tools that seamlessly integrate with existing systems. Rather than adding another data silo, Census aims to utilize a business's existing data infrastructure.Boris took issue with the cutthroat language often seen in brand marketing—this idea that one tool “kills” or “destroys” another. According to him, composability benefits everyone; it's the unsung virtue that ensures different tools can work together without causing chaos. This isn't just a win for the marketing team. Sales, finance, privacy, and compliance—all can leverage the same cohesive data structure.Boris also noted that composability isn't just about making it easier for marketers. It's a guiding principle in software development, often discussed even at the level of programming languages. Whether you're a marketer or part of a data team, he advocates for tools that don't just serve their isolated purpose but can also integrate effectively with other components of a business's tech stack.On the topic of identity resolution, Boris argued that if it's happening in your CDP, that shouldn't be exclusive to the marketing team. Census aims to democratize access to this crucial data, ensuring it benefits the entire organization. This is not about Census trying to replace CDPs; it's about working harmoniously with them to provide a well-rounded, integrated solution.Key takeaway: The question isn't whether Reverse ETL can replace a CDP, but how these tools can coexist and complement each other. Composability is the bridge that allows for this harmonious relationship, making the data landscape more functional and less complicated for everyone involved.Why the Idea of Reverse ETL Replacing CDPs is MisleadingDavid Chan, the Managing Director at Deloitte Digital who leads their CDP practice is really close to the composable vs packaged debate. Despite wanting to move on from the debate, I asked him about the perspectives of Census and Hightouch.David pointed out his understanding of Boris' perspective, stating that Boris wasn't aiming to dethrone CDPs but rather to create robust tech solutions for data teams. In contrast, he questioned the sincerity behind Tejas' claim that a Reverse ETL tool could take the place of a CDP.David pulled apart the anatomy of a CDP to make his point. He sees Reverse ETL as just one piece of the larger CDP puzzle. Arguing that no single tool should claim the full functionality of a CDP, David raised some important questions: Is a Reverse ETL tool responsible for real-time and batch data collection? Does it handle transformations and all inbound ETL? Is it responsible for identity resolution? His answer was an emphatic no. He suggested that what Tejas and Hightouch are offering is valuable but shouldn't be inflated into something it's not.He went on to say that what Hightouch and similar platforms can do is to integrate seamlessly into native enterprise data warehouses. This enables frontend users to query data, build audiences, and connect to various downstream systems in martech and ad tech. However, this isn't the same as serving as a comprehensive CDP solution.David seemed to imply that the idea of Reverse ETL replacing a CDP might just be a marketing gimmick to generate buzz. He did acknowledge that such tools offer a convenient plug-and-play into enterprise data structures but felt it was misleading to label them as CDP substitutes.Key Takeaway: The dialogue shouldn't focus on whether Reverse ETL can replace a CDP, but rather how it functions as a component within the broader data ecosystem. Recognizing the limitations and specific utilities of each tool will lead to a more effective and truthful martech strategy.The Irony of Reverse ETL Tools Possibly Becoming the CDPs They OpposeSince we last spoke to Tejas, Hightouch added ID resolution and event collection features. While they are built on your warehouse vs copying your data, many are arguing that this is making them start to look a lot like the packaged CDP they initially claimed to be dead.Aliaksandra Lamachenka, a data and martech consultant is a big fan of the composable architecture for some companies depending on their stage and their team but she shared some of her thoughts on the evolution of CDPs.Aliaksandra candidly dismissed the notion of a one-size-fits-all solution. She believes that as businesses grow, their needs for functionalities will also evolve. Vendors then face a critical decision: either evolve alongside their customers by adding functionalities, or stay true to their core offerings. According to Aliaksandra, this fork in the road could have significant industry implications.Interestingly, she noted that adhering to core values and functionality could actually be beneficial for the industry. Such a stance supports market democratization by serving a broader range of customers at different stages of growth. However, she also points out that vendors will inevitably reach a juncture where they must decide whether to keep adding layers to their platforms to meet customer demands, or to specialize and remain focused on their core offerings.The discussion on packaged vs composable CDPs is happening in a vacuum, Aliaksandra feels. While the industry debates the merits of one over the other, companies are struggling with more immediate and foundational issues like data quality, data lineage, and system discrepancies. For her, these problems underscore the necessity of having a strong data infrastructure in place before even considering which type of CDP to adopt.Aliaksandra highlights a common pitfall: the industry's fascination with adding new tools without considering the state of the existing data. She argues for the importance of first having a “clean” data layer to build upon. Without it, no CDP—packaged or composable—can be fully leveraged. In essence, she champions the idea of data hubs that enforce ownership and documentation by design as a foundational step.Key takeaway: Before diving into the packaged vs composable CDP debate, focus on the basics. Ensure your data is in good shape and avoid adding new tools to a chaotic environment. Once your data is well-managed and reliable, you'll be in a position to make more informed choices about which type of CDP best suits your needs.Episode RecapSo there you have it folks, Hightouch believes they can replace a packaged CDP because a few of their customers refer to them as their internal CDP. But Michael Katz thinks that represents a very narrow segment of the market. He argues that a fragmented DIY approach doesn't optimize business value and isn't practical for most enterprises. On the other hand, Boris and Census have taken a less controversial approach to product marketing and opted for more honest messaging. They don't claim to replace CDPs, in fact they're happy to work alongside them. The core idea behind Census is creating tools that integrate well with existing systems without adding complexity. Census seeks to distribute trustworthy data across different departments, leveraging existing data infrastructure rather than creating another silo. David seems to side with Census on this part of the debate. He doesn't think reverse ETL tools can replace CDPs entirely. His view is that reverse ETL tools are just one component of a complete CDP system and claiming that a reverse ETL tool could serve as a CDP would be misleading. Finally, Aliaksandra argues that vendors offering composable, lightweight solutions are making data more accessible. However, these vendors face a strategic choice: either expand their feature sets to keep customers engaged, risking the credibility of their original bold statements like “the CDP is dead,” or focus on perfecting their core offerings without overhyping their capabilities.You heard it here first folks: The debate between packaged and composable CDPs boils down to a trade-off between out-of-the-box functionality and tailored flexibility, with industry opinions divided on what offers greater long-term value. But if you do decide to explore the composable route, consider tools that focus on seamless integration and adaptability rather than those who claim to replace existing CDPs.✌️ —Intro music by Wowa via UnminusCover art created with Midjourney

Oussama Ghanmi a été Chief Data Officer chez Ornikar (scaleup valorisée plus de 600 millions d'euros) et Directeur au sein du cabinet de conseil en data M13h. Aujourd'hui, il a lancé un nouvel outil de Reverse ETL à destination des équipes Métier : Dinmo. L'objectif est d'autonomiser les équipes Métier dans l'activation des données et aussi de permettre aux équipes Data de se concentrer sur les projets à forte valeur ajoutée. On aborde :

What's up folks, today we're extremely privileged to be joined by David Chan, Managing Director at Deloitte Digital.Summary: Keep a keen eye on the modular evolution of CDPs. Know that reverse ETL tools are tactical additions, not replacements. Expect to reevaluate the roles of older platforms in your martech stack as CDPs get smarter. And if your organization's data strategy resembles more of a herding cats scenario than a well-oiled machine, maybe it's time to look into that dual-zone approach. It's a way to make sure everyone from your IT folks to your marketing creatives are playing from the same strategic playbook.About David David started his journey with PepsiCo as a Data Strategy Analyst and progressed to a Senior Associate role at Accenture Interactive He then joined Deloitte Digital as a Senior Consultant where he worked his way up to Managing Director, leading their CDP practice and focusing on Marketing Transformation and Operations He possesses extensive knowledge in crafting real-time personalization strategies, blending Identity Resolution, Customer Data Platforms (CDP), AI/Machine Learning, Dynamic Content, and their interplay within the broader martech ecosystem At Deloitte, David also works with product engineering teams to develop assets using tech platforms like AWS, Snowflake, Adobe, Salesforce and many others. From Web Analytics to CDPs: David's Evolution in MartechWhen asked about his journey into the world of Customer Data Platforms (CDPs) and martech, David candidly revealed that CDPs were nowhere on his radar back in 2010. Those were the days when conversations in the marketing tech space revolved around web analytics, content management, and commerce systems. No one was losing sleep over data management; instead, the questions on everyone's lips were about the promise of mobile apps. Is mobile going to be a big deal? Will people actually shop on a tiny screen? David noted that his professional background was solidly rooted in digital marketing, with a focus on areas like web analytics and content management. He didn't venture into the data-centric world of CDPs until about five years ago. The pivot happened when Deloitte, where David was employed, made a strategic acquisition. For the first time, they brought a company into the fold that specialized in data and analytics, a capability entirely new to Deloitte's existing services. It was this event that nudged David to start integrating this newfound expertise into Deloitte's broader service portfolio. He shared that this acquisition was a sort of aha moment for him, leading him to delve deeper into the CDP arena. Before this, the martech issues that were top of mind for him and the industry were focused elsewhere. Now, with this new role, David began to consider how to marry data and analytics capabilities with existing digital marketing services. His career took a turn, opening up new avenues and challenges.At this point, David's journey becomes a testament to how quickly martech can pivot and evolve, but also a case study on the necessity of adaptability in one's career. David's path shows that sometimes, the most significant career shifts happen when you're willing to integrate new, emerging components into your existing skill set. Key Takeaway: David's shift from web analytics to CDPs didn't happen overnight but was catalyzed by a crucial acquisition at Deloitte. His career trajectory illustrates that being open to new opportunities, especially those that are tangential to your existing expertise, can make all the difference.The Future Isn't Unbundled, It's Composable: David's Take on CDPs When asked about his unique take on the future of CDPs, particularly as articulated in his article responding to claims that "CDPs are dead," David drew parallels to his earlier experiences in the world of commerce. According to him, before one could even talk about a composable CDP, it's crucial to understand headless commerce. Back around 2013-2015, headless commerce decoupled web content management from the more intricate logic of commerce tools. In simpler terms, the pretty face of the website was one tool; the behind-the-scenes grunt work of product listings and checkouts was another. David noted that this breaking apart into components wasn't just a neat trick—it was an evolution seen in various technologies, especially commerce. Fast-forward to CDPs today, and there's a glaring issue. While commerce tools have achieved a kind of "modular maturity," CDPs lag behind. The tools exist, but how they should integrate is still a murky question. There's no established standard or framework yet that guides how these diverse tools and features should come together in a seamless, unified way. He went on to clarify that this lack of standardization in the CDP landscape is what makes an unbundled approach impractical, for now. In contrast, the commerce space went through a period of consolidation and standardization that made headless systems not just possible but effective. The CDP space is in its infancy when it comes to this level of system integration. It's like trying to assemble a puzzle when half the pieces aren't just missing; they haven't even been made yet. David's outlook? We're not there yet, and no, his stance hasn't changed. There's a future where CDPs are as composable as lego sets, but the industry needs more time to harden its thoughts, develop standards, and agree on frameworks.Key Takeaway: The future of CDPs isn't about scrapping the concept, but refining it. The industry needs time to mature and adopt standards for how disparate CDP components should integrate seamlessly, much like what has happened in the commerce space. Until then, declaring CDPs dead is a bit premature.Why Composability Wins: The Power of Choice in CDPsWhen asked about the top benefit of choosing a composable route for CDPs, David cut to the chase: it's all about choice. Imagine subscribing to a cable package with hundreds of channels when you only watch a handful. You get stuck with a bunch of extras you don't really need. David likens this to package CDPs, which come pre-bundled with an array of features that may not align with individual business needs. Sure, the package deal may look appealing on the surface, but dig deeper and you find yourself with components that are more noise than signal. The compelling thing about composability is it allows companies to choose only what they actually need. This isn't just about being picky; it's about optimizing performance and cutting down on excess baggage. Yet, David also added a word of caution: composability isn't some magic elixir. As the industry is still in the early stages of this journey, composability can offer either the "best of both worlds" or the "worst of both worlds." In other words, you can cherry-pick the best elements, but if you're not cautious, you might end up with a mishmash of incompatible parts. Companies should keep in mind that the CDP landscape is still maturing. For now, the fully composable CDP is like a chef's tasting menu that's still in development. Sure, some dishes are ready to serve, but others need more time to cook to perfection. So where does this leave us? David's take is clear: the path to fully composable CDPs is an ongoing journey, one that requires both caution and a bit of adventurous spirit. And, crucially, more time.Key Takeaway: Composability in CDPs offers the critical advantage of choice. Yet, with the industry still in its infancy, that choice comes with a responsibility to be cautious. Your tech stack can be a well-curated collection of best-in-class tools or a disorganized jumble. The next few years will be key in shaping how this unfolds.Who's the Ideal Fit for Composable CDPs? The Tale of Two ExtremesWhen David was asked about the ideal type of company for implementing a composable Customer Data Platform (CDP), his answer highlighted two extreme cases: large enterprises and small digital-native startups. David emphasized that the real crux of any CDP project lies in how a company constructs identity. In the U.S., for example, identity often hinges on where you live. In China, a WhatsApp ID can serve as a robust identifier for all kinds of transactions.Large enterprises have often sunk years and millions into getting their data in order. They've typically built up a wealth of data in different domains, from customer and product to financial and supply chain. They've got data for days, but the problem? Making it easily accessible for the business user without relying on a constant back-and-forth with IT. In this case, the last 5%—making data accessible and actionable—is where they're most likely to stumble.Contrast that with small, digital-native startups. These companies usually don't have the historical tech debt that can bog down larger organizations. They have fewer requirements and a more straightforward path to achieving a composable CDP. They don't have to unify data across multiple physical and digital channels, making their overall project complexity much lower.David also offered insights into which industries might find composable CDPs most beneficial. Companies that are less omni-channel—think streaming services like Netflix or Hulu—have it easier. These platforms require user login and payment details upfront, practically solving the identity question from the get-go. They don't need to unify data across various channels, simplifying the process.Key Takeaway: Large enterprises and small startups occupy opposite ends of the spectrum but both can effectively leverage composable CDPs. The secret sauce lies in mastering the intricacies of identity data, whether you're a big fish in a big pond or a small fish in a fast stream. Know your advantages and tackle that 'last mile' with eyes wide open.The Realities of Unbundling CDPs Across Company SizesWhen asked about the new wave of unbundling Customer Data Platforms (CDPs) and the role of resources in both small and large companies, David brings up an interesting analogy. Think of your martech stack as a collection of rocks and pebbles. The bigger the company, the more rocks and pebbles you'll have to manage. In this world, rocks symbolize the larger, more complex tasks and pebbles are the smaller, nimbler operations.The scale isn't just about data; it's also about the entire ecosystem. Larger companies have to wade through the labyrinth of vendor management, procurement, and InfoSec reviews for each tool they add to their stack. Smaller shops, while nimble, are often resource-starved. They may find it challenging to handle even five to eight additional tools, which could be the entry-level requirement for a low-end, unbundled CDP.What's worth noting is the rise of specialized data teams in both small and large organizations. Unlike a decade ago, engineers focusing on product development are now complemented by a whole squad of data-focused folks. This shift isn't just a passing fad; it's a fundamental change. Organizations that embrace this development are more likely to effectively manage their CDPs and, by extension, their customer data.So, the question isn't whether CDPs are dead or not. It's about understanding the resources, both human and technological, that you need to manage them effectively. The complexity of managing these platforms should be considered from a total cost of ownership (TCO) standpoint. After all, implementing a CDP isn't a one-off project; it's an ongoing, living, breathing initiative.Key Takeaway: Whether you're a startup or a large enterprise, unbundling a CDP requires you to weigh resources against complexity. Before jumping into any CDP project, consider the TCO, not just the upfront costs. It's an ongoing commitment, not a one-time task.The Real Scope of Reverse ETL Tools in the CDP EcosystemWhen asked about the heated debate between Census and Hightouch over whether reverse ETL tools could replace traditional Customer Data Platforms (CDPs), David had a unique perspective. While Census believes in complementing existing CDPs and focuses on seamless integration, Hightouch claims it can completely replace legacy CDPs. David's articles generally align more with Census' viewpoint and he's actually moderated a panel discussion sponsored by Census and had the opportunity to connect with Boris, Census' founder. In David's view, Boris was not aiming to overthrow CDPs but focused on building solid technology that empowers data teams.David expressed skepticism about Tejas's bold claims that Hightouch could fully replace legacy CDPs. For David, such statements are a bit of a red herring. "What exactly does Tejas think a CDP is?" David questioned. Reverse ETL tools, he argued, represent just one component of a CDP. The notion that they could replace CDPs in totality is misleading, especially when these tools aren't responsible for a host of crucial functionalities like customer data infrastructure, data ingestion in real-time or batch and data transformations.David makes it clear that reverse ETL tools can integrate well with an existing data infrastructure, but they shouldn't claim to replace an entire CDP. He points out that even if Tejas would accept this line of reasoning, Hightouch's primary function is to plug into enterprise data warehouses. Their purpose is to simplify data queries for front-end users and facilitate connections to downstream martech, ad tech, and CRM systems, but that's far from a complete CDP solution.Key Takeaway: David debunks the notion that reverse ETL tools can fully replace CDPs. These tools might be powerful in what they do, but they're not an all-in-one solution for customer data management. Calling them a CDP replacement is not just an oversimplification; it's misleading.The Complexity and Potential of Reverse ETL in CDP EcosystemsIn our interview with Tejas, we asked him about how Hightouch compares to traditional CDPs, focusing on its functionalities and whether it could eventually take on the role of a full-fledged CDP. Tejas agreed that Hightouch doesn't currently offer all these features but didn't rule out adding them in the future. He mentioned that they've already started incorporating features like ID resolution and have a partnership for first-party data tracking with Snowplow and hinted at data ingestion features coming soon (which has since been released). While Hightouch is primarily a reverse ETL tool, Tejas argued that some enterprises have ditched their packaged CDPs in favor of Hightouch.David acknowledged that for enterprises that have already sorted their data collection and ETL needs, a reverse ETL tool like Hightouch could easily take on the role of a CDP in their minds. However, he stressed that Hightouch explicitly outlines on their website where they fit into the CDP landscape, making it clear they're not trying to play in every space.Diving deeper into the evolving CDP market, David reflected on how different tools are considering expansions into adjacent areas. For example, tag management systems have begun to offer data storage solutions, while others are contemplating adding web SDKs. According to David, this trend is likely to continue as companies strive to increase the value proposition of their solutions. Product teams are left with the strategic question of where to invest their time and resources for maximum impact.What caught David's attention was the concept of journey orchestration, an area still not fully grasped or well-executed in the industry. David argued that reverse ETL vendors, already proficient in data segmentation, could logically advance into the journey orchestration space, thereby increasing their value and utility.Key Takeaway: David believes the role of reverse ETL tools like Hightouch in the CDP ecosystem isn't set in stone. While they can serve as a CDP for some businesses, their real strength lies in the opportunity to expand into adjacent, high-impact functionalities, like journey orchestration. This ability to adapt and grow may well shape the future of CDPs and reverse ETL tools alike.The Changing Role of Segmentation and Journey Orchestration in the Martech StackWhen David was asked about the current transition in martech—where reverse ETL tools are challenging traditional customer engagement platforms on tasks like segmentation and journey orchestration—he framed it as a shift between "dumb hubs and smart spokes" versus "smart hubs and dumb spokes." According to David, there's a move toward centralizing the intelligence in the stack. In this scenario, the "hub," often a Customer Data Platform (CDP), would handle the heavy lifting of segmentation and data management. As a result, the other tools (the "spokes") would just execute the tasks they're fed, possibly becoming simpler and more specialized in their functions.David explained that legacy marketing platforms like Marketo and Braze have limitations when it comes to segmentation and data processing. Often, they run into performance issues due to the sheer volume of data, forcing users to simplify their segmentation logic or even causing timeouts. These older platforms were conceived before the rise of the modern CDP, which can handle complex segmentation and data management tasks more efficiently.The shift towards smart hubs is leading marketing cloud companies to invest in CDPs as a central nervous system for their various tools. David implies that as CDPs become increasingly capable, the need for complex segmentation logic in secondary tools may diminish. This will alter not just the technology but the entire structure and strategy of martech stacks across industries.However, this raises questions about the future role of traditional customer engagement platforms. If a lot of the complexity they were designed to handle is now managed earlier in the tech stack, what function will they serve moving forward? David suggests that we could see an industry-wide reevaluation of these platforms' roles and functionalities.Key Takeaway: As CDPs potentially evolve to become the "smart hubs" of the martech stack, handling additional features on top of complex segmentation and data management tasks, the importance of other tools like Customer Engagement Platforms may start to diminish. This has implications for the entire industry and could redefine what we expect from our marketing technology.Dual Zone Versus Hybrid in CDP: A Nuanced Take on Customer Data StrategyWhen asked about his unique "dual zone" concept for CDPs and how it contrasts with the more traditional hybrid approach, David agreed that companies like Acquia and ActionIQ are onto something by offering a range of configurations for their CDP products. The marketplace has shifted towards customization—nobody wants to buy a one-size-fits-all solution. While some companies call this a "hybrid" approach, David's take on dual-zone addresses a deeper, more nuanced issue: the rift between data producers (IT and data teams) and data consumers (marketing, sales, and service ops teams). The fundamental problem, he pointed out, isn't just about storage or composition. It's about breaking down the barriers between data producers and data consumers. Marketing, sales, and service teams often work in silos, unaware of the kind of data being generated or how to harness it. This lack of synergy creates a disconnect that the dual zone strategy aims to address.On the technical front, David stresses that a dual zone approach gives both sides—cloud-native data stores and SaaS platforms—specific roles to play.  Zone One focuses on ID resolution, data management, and heavy computational tasks.  Zone Two, on the other hand, takes care of customer engagement elements like journey orchestration and campaign activation.  Unlike a mere "hybrid" system, which might evolve into a "Frankenstack" of mismatched parts, dual zone is intentional. Every tool, every piece of tech, has its designated space and function.What really sets David's dual zone model apart is its purposefulness. It's not an amalgamation of technologies thrown together over time. It's a well-defined, clear-cut strategy that aims to balance workload efficiently between different zones. The end game? To ensure that the right data is available to the right teams at the right time, boosting operational efficiency and customer engagement in one fell swoop.Unlike the 'hybrid' systems that try to do a bit of everything, dual-zone forces you to be deliberate about what you're doing and why. For instance, identity resolution doesn't occur in just one zone. Zone one might provide a baseline identity, while zone two enhances it with device IDs, cookies, or IP addresses. This distinction, while subtle, is crucial. It prevents a mishmash of functionalities and ensures that you're not overbuying or overextending in either zone.Key Takeaway: David's dual-zone model is not just a conceptual pivot; it's a tactical framework designed to align your data strategy with business goals. By dissecting the data journey into two specialized zones, companies can achieve a rare balance—efficiency and effectiveness, without unnecessary complexities.Championing Asian American Awareness in Corporate AmericaWhen asked about his role as an executive sponsor and co-founder of the SES and East Asian Leadership Network, David candidly shared his upbringing and motivations. Born in Brooklyn to immigrant parents, David faced his share of challenges, including racism, while navigating the complexities of life in a metropolitan city. Despite achieving career success, he's also experienced a changing perception of safety amidst rising hate crimes against Asian Americans—a stark contrast to his previous comfort and confidence in his own city.David's work at Deloitte brought to light a glaring issue. Though the firm focuses on underrepresented minorities, Asians had often been left out of that categorization. David's push for the creation of the SES and East Asian Leadership Network stems from the understanding that Asians, while possibly not underrepresented in certain corporate environments, face unique challenges tied to cultural values and norms. The clash between Western and Eastern cultures, he noted, can sometimes hinder career advancement for Asians, putting them at a disadvantage.The primary objective of David's group is not merely representation, but genuine empowerment. He emphasized the importance of mentoring younger professionals in the firm to help them succeed authentically, without losing their true selves in the process. David clarified that success doesn't just mean climbing the corporate ladder within the company; he celebrates even if they find their path elsewhere.Key Takeaway: David's advocacy for Asian American awareness is far from a corporate checkbox; it's a mission rooted in personal experience and an intimate understanding of the unique struggles faced by Asians in America. His approach redefines the term 'underrepresented' to capture not just numbers, but the nuances of cultural collisions and barriers to true equality.The Realities of Balancing Success and HappinessWhen asked about how he balances his busy life—juggling roles as a managing director, executive sponsor, co-founder, and husband—David offered a candid perspective. He doesn't claim to have found a magical formula for balance. Instead, he talks about making trade-offs. It's a conscious decision to allocate time and energy, whether it's toward family, work, or friendships. The trick, according to David, is to make these choices without harboring regret.A critical element that keeps David motivated is his love for his job, a passion rooted in his early career aspirations and influenced by his parents, who were small business owners. David saw consulting as a learning opportunity, a means to understand the intricacies of running a business before launching his own. Over time, he found that Deloitte offered the kind of autonomy that scratched his entrepreneurial itch. He relished the freedom to build his own teams while aligning with broader corporate mandates.This enthusiasm for his work isn't just an isolated experience. David revels in the discussions and dialogues that occur in his professional life, especially the learning aspect. His thirst for knowledge isn't merely for personal growth; it feeds into his approach with clients. David frequently tells his clients, "My pain is your gain," underscoring that his own experiences, both successful and otherwise, serve as valuable lessons for others.Key Takeaway: David's approach to the often-asked question of work-life balance shatters the illusion of perfection. Instead, he emphasizes the reality of trade-offs and the importance of living without regret. His zest for learning and the job at hand serves as a pillar that helps him navigate through these trade-offs, enriching not just himself but those around him.Episode recapIn a rapidly evolving martech landscape, CDPs are shifting from the fringes to become the central nervous system of data strategy. But let's get one thing straight: CDPs are at a teenage stage. They're moody, hard to predict, and just like headless commerce a few years ago, they haven't reached their full potential. It's a space where caution and choice co-exist. You can choose your own adventure, selecting modules like you're shopping a la carte, but make no mistake—without standards, that liberty could get messy.As CDPs step up, reverse ETL tools like Hightouch that claimed to be a "next best thing" need to be put in perspective. They're good, but not the CDP-killer some might think. They fill gaps but aren't equipped to manage the panorama of tasks a full-fledged CDP can. While they integrate neatly into an existing architecture, they're not an architecture unto themselves.Now, while CDPs are getting sharper and more intelligent, it doesn't mean every tool in your stack should or will. Platforms like Marketo are being forced to simplify; they're turning into role players rather than MVPs. The CDP is emerging as the LeBron James on your martech team, orchestrating plays and setting up others to score.Here's where David's concept of a "dual-zone" CDP strategy adds another layer of finesse. It's not just about intelligent data collection and customer engagement. It's a targeted approach to breaking down the silos that often keep data producers and consumers in an organization from truly collaborating. This isn't your average 'hybrid' model that clumsily tries to be everything. It's a fine-tuned system that ensures the right hand knows what the left hand is doing, enhancing operational efficiency and customer engagement in one coherent, strategic sweep.Keep a keen eye on the modular evolution of CDPs. Know that reverse ETL tools are tactical additions, not replacements. Expect to reevaluate the roles of older platforms in your martech stack as CDPs get smarter. And if your organization's data strategy resembles more of a herding cats scenario than a well-oiled machine, maybe it's time to look into that dual-zone approach. It's a way to make sure everyone from your IT geeks to your marketing creatives are playing from the same strategic playbook.✌️--Intro music by Wowa via UnminusCover art created with Midjourney

Allan Wille, CEO at Klipfolio, sat down with Brian Kotlyar, VP of Marketing & Growth at Hightouch, a reverse ETL solution that makes data manipulation, extraction, and moving smooth, easy, and fun for data engineers. Their conversation explored the potential of reverse ETL, the pressing challenges of modern business intelligence (BI), and the exciting prospects that are emerging as AI meets data analytics.

What's up folks, today we'll be joined by various martech pros sharing their opinions on the topic of warehouse-native martech.The landscape of marketing technology architecture has been undergoing – what you might call – a seismic shift and many don't even realize it. In this transformation, there's a remarkable development - warehouse-native marketing technology, an innovative breakthrough that promises to reshape the entire industry for the better, but comes with plenty of questions and skepticism. Here's today's main takeaway: As we navigate the potential transformation to warehouse-native martech, the single most critical action is to prioritize achieving high-quality, well-structured data; it's the golden key to unlocking the full potential of these emerging tools and strategies.This episode explores the various facets of warehouse-native martech and its viability, pulling in insights from industry experts, piecing together a comprehensive view of this groundbreaking shift.What are warehouse native martech or connected apps?In Dec 2021, Snowflake introduced a new term, 'connected applications'.  Unlike traditional managed SaaS applications, connected applications process data on the customers' data warehouse, giving customers control over their data  Benefits include  preventing data silos,  removing API integration backlog,  enabling custom analytics,  upholding data governance policies,  improving SaaS performance,  and facilitating actionable reporting In other words, instead of making a copy of your DWH like most CDPs ad MAPs do today, everything lives on top of the DWH and you don't have to pay for copying your db.Some companies solving this for product analytics are Rakam, Indicative, and Kubit. Census and Hightouch are also doing this, being warehouse-native activation tools sitting on top of a DWH and don't store any of your data. Some Messaging companies solving this use case natively on the cloud warehouse are Vero, Messagears, and Castled.Revolutionized Data Handling in Customer Engagement PlatformsIndia Waters currently leads growth and technology partnerships at MessageGears. She explains how her company's differentiation comes from its unique handling of customer data.Unlike competitors such as Salesforce Marketing Cloud or Oracle, which require a copy of customer data to live within their tool, MessageGears directly taps into modern data warehouses like Snowflake or Google BigQuery. This unique approach is born out of the inefficiency and high costs of older platforms that necessitate copying and moving data into multiple marketing tools.India vividly portrayed the challenge this old approach creates, imagining the confusion and resource consumption of working with out-of-date data across numerous tools. By not having to have a copy of customer data, MessageGears solves this problem for big companies, eliminating waste and creating a more coherent understanding of the customer's journey. Clients like OpenTable, T-Mobile, and Party City can now work with the most up-to-date data, using it as a source of truth for better analytics and customer experiences.Reflecting on how MessageGears had to become thought leaders in this approach, India acknowledged that it took time for the industry to understand and accept this innovative method. But as awareness has grown, the approach is now seen as a logical and necessary step in the evolution of customer data handling.Takeaway: MessageGears' refusal to follow the traditional path of copying customer data into its tools is a game-changer in the world of customer engagement platforms. By plugging directly into modern data warehouses, they've solved a problem that has plagued big companies, enabling them to use the most up-to-date data for insights and experiences. The industry has evolved, and MessageGears is leading the way with an approach that makes sense for today's data-driven world.Rethinking User Database Size Pricing in MartechWhile MG has been around since 2011, more and more startups are waking up to the idea of directly accessing brands' first-party data instead of relying on cloud data syncs. We also chatted with Arun Thulasidarhan, CEO & Co-founder at Castled.io. They're a warehouse-native customer engagement platform that sits directly on top of cloud data warehouses. Arun and his team set out to disrupt traditional martech to fix some of the fundamental problems as it relates to the significant disconnect between the number of users a company pays to store in their database and the actual value derived from them.He emphasized that having millions of users doesn't necessarily translate to substantial revenue or value, especially for smaller B2C companies. He critically questioned whether traditional pricing models based on user database size were really delivering value for businesses. Arun then went on to explain how Castled.io approaches this differently, choosing a more logical and direct connection between cost and benefit. Unlike other martech firms that charge based on customer numbers, Castled.io bases its pricing on the number of team members using the tool. Arun argues that this is a more accurate reflection of the value a company gets from the service, as more marketers using the tool likely means a more substantial investment in the platform. He also touched on how they handle data look-back periods and the importance of data retention for retargeting and reengagement. With traditional systems, data engineers might have to wait for months, while with Castled.io, the data is readily available in the data warehouse. The integration of data warehousing and marketing tools, according to Arun, is the future of martech pricing – something he sees as a "no-brainer." Takeaway: Traditional martech pricing models have significant inconsistencies, often failing to align the number of customers with the real value obtained. Castled.io challenges this paradigm by pricing their services based on the number of team members using the tool and ensuring that data retention aligns with business needs. This more logical and direct approach may be an essential step forward for the martech industry, promoting fairness and value over mere numbers.Aligning Pricing Metrics with Customer NeedsMessageGears and Castled.io's groundbreaking approach in martech isn't merely an isolated occurrence. It's part of a broader trend that calls for a deliberate rethinking of pricing metrics within the industry. This movement emphasizes the alignment of price with real value and accessibility. It's worth highlighting the intricacies of selecting the right pricing metric. We spoke with Dan Balcauski, a SaaS pricing expert who highlights that it's not just about being innovative; it's about making choices that truly resonate with customer needs and market demands. Dan delved into the complexities of pricing metrics and how they can be used to either aid or hinder competitive differentiation. Though he admitted that his knowledge of the specific market wasn't extensive, he was able to break down the various facets of pricing strategy, sharing an intriguing case study to illustrate his point.Dan emphasized the importance of choosing a pricing metric that aligns with customers' business requirements and the perceived value of the product. This metric, according to him, must balance fairness, predictability, and operational ease for both the buyer and the seller.He highlighted the example of Rolls Royce's innovative approach to jet engine pricing, where they chose to charge "power by the hour" instead of selling the engines outright. This usage-based model aligned the interests of the buyer and seller, streamlining many ancillary aspects such as maintenance and replacement.However, Dan also warned against unnecessarily complex or "cute" pricing metrics. He stated that success in implementing innovative pricing strategies likely comes easier to industry leaders or highly innovative products. Trying to be different just for the sake of it can lead to confusion and additional costs in educating the market.Takeaway: In the world of martech, warehouse-native pricing changes are a nuanced subject. As Dan's analysis reveals, the successful implementation of a pricing strategy requires a careful balance of alignment with customer needs, perceived value, predictability, and operational efficiency. Innovative approaches can bring success, but they must be implemented thoughtfully and with a true understanding of the market. Being different for its own sake may lead to complexity without adding real value.The Undeniable Movement Towards a Universal Data LayerBefore getting into the weeds of the viability of this shift, let's get the lowdown from one of the most respected voices in martech. You guessed, we're talking about Scott Brinker. The Martech Landscape creator, the VP of Platform ecosystem at Hubspot and the acclaimed force behind chiefmartec.com, hailed universally as the martech world's ultimate wellspring of knowledge and insight.Scott sees a clear trend in martech towards consolidating data across the company into the warehouse, and making that data accessible across various applications. He doesn't hesitate to point out that this is a bit different from being truly warehouse-native, which raises questions about the architecture layers and the way data interacts operationally with the warehouse.On the exciting side, Scott highlights the robust experimentation in the field. However, he's keen to identify the challenges too, such as the need to rationalize data that is inherently messy when consolidated into data lakes and warehouses. The sheer volume and complexity of data require layers of interpretation and structuring, something that individual martech products often provide.Scott also highlights the performance dimension, noting that while technological advances have improved the read/write performance of data in a warehouse, there are still cases where millisecond differences in performance can have critical impacts on user experience or search engine rankings. He sees the need for operational databases fine-tuned to specific engagements as a continued necessity in the martech architecture.In the end, Scott recognizes the undeniable movement towards a universal data layer where martech companies are being driven to contribute and leverage data from the warehouse. However, he doesn't see it as something that will entirely replace all localized and context-specific databases in the immediate future.Takeaway: Scott provides a balanced and insightful perspective on the warehouse-native approach in martech, seeing it as an interesting and evolving aspect but not a complete solution. He emphasizes that while consolidation and accessibility of data are crucial, the complex nature of data, performance considerations, and the need for specific databases mean that the warehouse-native concept is still more of a developing direction rather than an established end point in the martech landscape.The Necessity of Cloning Data in Warehouse Native MartechAs we talk about shifts in the data management landscape, Pini Yakuel, the CEO of Optimove (Marketing Automation Platform) provides a practical example of these changes, discussing how they're CDP component is built on top of Snowflake.Pini dived into the subject of warehouse native martech with a keen eye for architectural details. He spoke candidly about the convenience of copying data from one place to another and the efficiency of Snowflake, allowing for a seamless client experience. A clear advocate for this technology, Pini mentioned how companies can leverage Snowflake to have data easily accessible without having to move it around. The snowflake-to-snowflake data mirroring, for instance, eliminates the need for ETL, providing a significant advantage.However, Pini didn't shy away from the challenges either. The same technology that enables quick data processing doesn't necessarily translate into fast response times for user experience. For instance, Snowflake, being an analytical data warehouse in the cloud, may not respond quickly enough for UX requirements.Pini concluded with an optimistic note about the future, mentioning that Snowflake and BigQuery are emerging as significant players. But, he also acknowledged that the need to have copies of data close for certain operations still exists, leaving room for technology to evolve further.Takeaway: While warehouse native martech, especially through platforms like Snowflake, offers incredible convenience and has been a game-changer, it's essential to recognize the need for closer data positioning in some cases. The current landscape is promising, but the future might hold even better ways to copy and utilize data without hindrance.The Misguided Myth of Zero Data CopyWhether it's technically possible or not, not everyone is on board with the notion of zero copy data, using martech without ever needing to copy data in any of your tools. Enter Michael Katz, CEO and co-founder at mParticle, the leading independent and packaged customer data platform.When asked about the concept of zero data copy and why he considers it misguided, Michael passionately dove into the core of the argument. He began by highlighting that copying data's implication of creating inefficiency, particularly in terms of access cost, is fundamentally flawed. In his view, the cost of storage is negligible compared to the cost of computation, a fact well understood in the industry. Hence, creating duplicate copies of data doesn't significantly change the overall cost structure.Michael then went on to emphasize that it's been demonstrated time and time again that replicating data brings tremendous efficiency for various uses and applications. He further expanded on his argument by noting that the belief in zero data copy not only misleads but also directs individuals and companies down a path of solving non-existent problems. He remarked that the focus should be on minimizing costs to maximize resources for growth, not chasing an illusion of efficiency.Adding another layer to his argument, Michael revealed the dirty secret behind many reverse ETL companies, citing a persistent churn problem. These companies, he pointed out, offer what appears to be an "easy button" solution, but when the button is pressed, things turn out to be far from easy.Takeaway: Michael's debunking of the zero data copy concept is a compelling reminder that chasing illusions can lead to more harm than good. The true focus should be on understanding the problem at hand and allocating resources wisely, rather than getting lost in the allure of simplified solutions that often prove ineffective. This insight urges us to be more discerning in evaluating the effectiveness and underlying motives of the tools and strategies we adopt in the world of martech.Solving the Puzzle of Compute Charges in the Cloud Data WarehouseMany industry experts agree with Michael that one of the biggest hurdles for warehouse native martech is computing charges and creating a load on your DWH/Snowflake that can add up quickly. Here's what Arun from Castled.io had to say about his solution for this compute challenge.When asked about how to tackle the prevalent problem of compute charges in existing cloud data warehouses, Arun clearly outlined the importance of addressing this issue. In his view, it's more than just a concern about expenses; it's an integral part of deciding to have a data warehouse, which still holds great value to many.Arun dove into the core of the problem, explaining that once a data warehouse has been implemented, businesses often aim to not only enable data analytics but also marketing, where significant investments are made. This decision leads to one of the major reasons behind the compute charges: hiring bulk analytic engineers, many lacking the necessary experience to write optimal SQL queries.Arun's perspective on the solution is straightforward and rooted in his experience. For him, once the data is collected in the data warehouse, the most scalable model involves using warehouse-native applications like Castled.io. These applications reduce the charges by running all kinds of load tests to ensure minimal and optimal expenditure. Arun emphasized the care taken to ensure that even a minor filter change doesn't lead to unnecessary extra charges.Takeaway: Arun's insights highlight a common yet overlooked aspect of cloud data warehouse management: compute charges. By understanding the root causes and adopting warehouse-native applications, companies can not only minimize these charges but also maximize the value and efficiency of their data warehouses. His approach illustrates a thoughtful and scalable way to ensure that technology investments align with financial considerations.Is Warehouse Martech More Beneficial for Cloud Providers Than Customers?Despite hearing this solution on compute charges and the benefits of zero copy data, Michael Katz, CEO of mParticle held firm on his stance going back to the value to customers.Michael began by laying out a common structure of the marketing tech stack, mentioning different components such as analytics, customer engagement platforms, experimentation tools, and customer support services like Zendesk. In this context, he highlighted that between five and ten different categories could be observed across most martech stacks.Michael then questioned the real beneficiaries of building everything natively on a Cloud Data Warehouse. He argued that such an approach seems to favor the data warehouse provider rather than delivering genuine value to the customer. Moreover, he expressed skepticism about the notion that having all vendors run their own compute cycles on the data warehouse would necessarily lead to cost savings. He pointed out that while theoretically possible, no one has conducted a side-by-side comparison to prove that assumption.Further, Michael emphasized that whether dealing with providers like Snowflake or mParticle, everyone is in essence reselling cloud compute, either with a markup or bundled into services. The assumption of inherent cost savings, he asserted, doesn't stand up to scrutiny, and the claim that avoiding the creation of multiple copies of data will automatically save money is not necessarily true.Takeaway: Michael's examination of the warehouse native approach reveals that what might seem like a cost-saving strategy on the surface might not deliver real benefits to the customers. This insight warns against blindly accepting theoretical advantages without concrete evidence, encouraging a more nuanced understanding of how value is truly generated in the martech world.Why Zero Data Copy in Martech is Not a Black-and-White IssueMichael's scrutiny of the warehouse native approach invites a broader conversation about adaptability and tailored solutions in martech. It challenges the standard view, paving the way for alternative methods that don't cling to conventional wisdom. Recognizing that one approach doesn't fit every scenario, some companies are proposing a hybrid approach and shaping the conversation around customization and efficiency.In this camp is Tamara Gruzbarg, VP Customer Strategy at ActionIQ – an enterprise Customer Data Platform. When asked about the widespread arguments dismissing zero data copy as a flawed concept, Tamara offered a thoughtful perspective. She didn't outright reject the notion, but rather emphasized the importance of not viewing it in black and white terms. In her view, the concept of zero data copy isn't necessarily something that will work for everyone in the immediate future, but that doesn't mean the industry shouldn't be moving in this direction.Tamara continued to explain that once sufficient work has been done to create a robust data environment within a client's internal structure, there's a real opportunity to leverage that investment. It's about using the data from its original location to minimize costs, rather than insisting on either 0% or 100% adherence to a zero copy or fully composable CDP model.Speaking from her experience at ActionIQ, she emphasized the value of creating a "future-proof" environment where different components from the best vendors or internal solutions can be utilized. This approach allows for adaptability, not locking into a rigid framework, and instead opting for a path that works for the individual needs of a company, with the capacity to optimize over time.Takeaway: Tamara's insight sheds light on the nuanced reality of the zero data copy debate. Rather than clinging to absolutes, she encourages a more flexible approach that aligns with the individual needs and future directions of a company. Her focus on creating a future-proof environment underscores the importance of adaptability and optimization in the ever-changing martech landscape, without falling prey to rigid ideologies.Warehouse Native Martech Impacting Enterprise More Than SMBsThe push for flexibility and optimization in data handling hints at a wider trend affecting large enterprises. This focus on warehouse native solutions aligns more closely with the complex needs of large organizations than with SMBs, setting the stage for a broader industry shift that some experts continue to explore.One of these experts is  Wyatt Bales a senior exec at BlueprintX, an enterprise focused-martech and growth agency. When asked about the potential future of martech being warehouse native, Wyatt presented a comprehensive view on the subject. He emphasized that this path is indeed the way forward for enterprises, defining these as organizations with 10,000 employees or more. Wyatt agreed that traditional tools, such as duplicated databases and interfaces for marketing automation, are being replaced by more sophisticated and flexible solutions.He shared insights from current projects, where customers are rethinking their approach and moving towards more direct communication through APIs and delivery services. This transition, according to Wyatt, is not only efficient but also resonates with the changing needs of enterprise clients.However, he didn't see this trend affecting the Small and Medium Business (SMB) sector in the same way. The traditional path of migrating from simpler tools like MailChimp to more advanced platforms like Marketo still holds relevance for SMBs. Wyatt predicts an emerging trend where SMB markets might see the integration of work management tools, such as Asana, with marketing automation platforms. This would provide an end-to-end solution that meets the specific needs of smaller businesses.Wyatt also highlighted the importance of adaptability in skillsets, particularly within the context of warehouse-native solutions. Emphasizing the value of SQL knowledge, he discussed how organizational decisions and structures are changing, affecting even hiring and staff positioning. The future, according to Wyatt, is not only about mastering specific tools but also having the ability to talk about cloud storage, integrations, and other technological advancements. He stressed the importance of versatility in skillsets, particularly in a landscape that is rapidly shifting towards warehouse native solutions.Takeaway: The future of martech is clearly leaning towards warehouse native solutions for enterprises, reflecting a desire for flexibility, efficiency, and direct control. However, this shift is not universal, and Wyatt points out that SMBs will continue to have different needs and paths. The landscape is evolving, and success will depend on adaptability, both in technology and in the skillsets of those navigating this complex ecosystem.API Connections Versus Warehouse Native ApproachThis being more impactful for enterprise is an argument that's echoed by MessageGears when talking about the difference between APIs integrations and the warehouse native approach. Here's India Waters from MessageGears (again).India described the contrasting experiences of these two models by focusing on the real-world implications. She broke down the seemingly straightforward task of setting up individual APIs for real-time data access, especially in small to medium-sized businesses.The problem, India explained, lies in the constantly changing environment. Whether it's adding new fields or updating existing ones, the complexity of these tasks grows exponentially. When businesses try to synchronize tools like SalesLoft, Salesforce Pardot, or even something as specific as demand-based sales tools, the complexity doesn't just double; it becomes an almost unmanageable challenge. Imagine a company like Best Buy or Home Depot, with countless customers and enormous volumes of first-party data. The complexity becomes a daunting puzzle.India's solution through MessageGears provides a refreshing perspective. By allowing businesses to view their modern data warehouse without the burden of storing data, the approach untangles the web of syncing, matching, and complying with new data privacy laws. India expressed a frustration with those who still don't get this new approach, highlighting how the warehouse native model renders concerns like HIPAA compliance almost irrelevant.Takeaway: India's insights shed light on the intricacies of API connections versus the warehouse native approach. Her detailed explanation helps us understand how even simple tasks can become a tangled web as business grows. By adopting innovative solutions like MessageGears, businesses can bypass these complexities, align with modern data privacy laws, and efficiently manage their data, demonstrating a forward-thinking approach to the technological future.Does Warehouse Native Martech Replace Reverse ETL Tools?Some of the emerging tools to replace API integrations are called reverse ETL, basically pushing data from your warehouse to your business tools. Some of the startups solving this are Hightouch and Census. The question though is, does warehouse native martech (sitting on top of the warehouse) also replace the need for reverse ETL solutions. Just like you might prefer using a bridge to cross a river rather than paying a ferry. Here's Arun again from Castled:When asked about whether warehouse native can replace reverse ETL tools, Arun provided a perspective that goes beyond a simple yes or no. His insights highlight the intricate balance between technology and purpose.Arun explained that while warehouse native solutions can indeed eliminate the need for reverse ETL pipelines, it's essential to understand why a business would want to do so. The motivation to adopt warehouse native shouldn't be solely to eliminate reverse ETL; otherwise, the solution may fall short. With companies like Customer.io actively incorporating reverse ETL into their systems, a mere desire to remove reverse ETL isn't enough.Arun's approach emphasizes the problem-solving capabilities of the warehouse native approach. If there are tangible limitations in existing tools, and if a warehouse native solution can solve those problems, then the path becomes clear. But starting on this path just to eliminate reverse ETL, without considering the broader issues, would be a mistake.Takeaway: Arun's insights underscore the importance of aligning technology with genuine needs. Warehouse native solutions offer the ability to bypass reverse ETL, but this shouldn't be the sole driver. Businesses need to identify real challenges that can be addressed by warehouse native solutions, creating a synergy between technological innovation and problem-solving. Anything less is a fleeting pursuit that's likely to fall short.Established Platforms vs Warehouse Native Marketing Automation Obviously reverse ETL platforms are going to have some hot takes about this question. One of them is Tejas Monahar, the co-founder and co-CEO of Hightouch, a reverse ETL tool that's taken a controversial stance against the packaged CDP, claiming that it's dead and that they can replace it.Tejas noted that while ease of warehouse native tools are on the rise, he doesn't envision them taking over established platforms like Salesforce Marketing Cloud or Iterable. To Tejas, these tools can't replicate the variety of channels and functions available in existing martech solutions.Tejas explained that marketers need to utilize their data across all channels, and solutions like Hightouch make this process simple. He was unafraid to share that he's not bullish on the trend of warehouse native marketing tools dominating the space, as they do not address the unique needs of marketers. This includes all sorts of concerns that a Customer Engagement or ESP platform handles, not related to the data warehouse, such as data quality, governance, privacy, and identity risk.However, Tejas clarified that his stance does not mean there's no room for new businesses in martech like warehouse native. On the contrary, he sees a wealth of opportunities to build in this field, especially with localization and integration. What he doesn't foresee is a platform shift that replaces giants like Salesforce and Adobe. The focus should be on integrating the data and marketing sides of the business, and Hightouch is positioned as an ideal solution for this.Key Takeaway: Warehouse native tools and CDPs are growing, but Tejas argues that they will not replace the multifaceted capabilities of existing martech providers. While they may add some new functionality, their integration with traditional platforms seems more likely. The focus, he believes, should be on how marketers can use data effectively across all channels, and he sees Hightouch as the perfect solution to bridge the gap between data and marketing needs.Effortless Data Movement and Apps as Lightweight Caches on Core WarehousesNot all reverse ETL vendors have a negative view on warehouse native approach though. Boris Jabes, the co-founder and CEO at Census, another Reverse ETL tool, has a different perspective.When Boris was asked about the future of warehouse native martech and its potential to replace reverse ETL, his response not only highlighted a promising vision but also revealed Census's pioneering role in the field.Boris acknowledged the attraction of a world where warehouse native martech diminishes fragmentation and promotes consistency in written data. He was quick to point out that Census has been a trailblazer in this domain, adopting a warehouse native solution even before the term was coined. This, he said, is a testament to the company's innovation and leadership in the space.He detailed Census's offerings, such as the Audience Hub, a segmentation experience native to the warehouse. These solutions not only reflect Census's deep understanding of warehouse native systems but also underscore the company's commitment to letting marketers activate data without hassle.However, Boris also emphasized the challenges and necessities of this path. Perfect data in the warehouse is key. Understanding the relationships between different sets of data, customizing relationships, and validating data before use are all integral to making warehouse native martech work seamlessly.Boris's vision culminated in the anticipation of a world where data movement systems are no longer a concern, and every application becomes a lightweight cache on the core data warehouse. Though he believes in this future, he cautioned that it may take time to come to fruition and urged companies to focus on transforming and modeling user data.Key Takeaway: Boris's insights cast a spotlight on the potential of warehouse native martech, with Census leading the way before it was even a recognized term. His vision of applications as lightweight caches on core data warehouses paints a compelling future. Yet, it's grounded in the reality that clean, well-structured data and a deep understanding of relationships between data sets are crucial to making this dream a reality. The path is laid out; the journey, according to Boris, requires focus, innovation, and a commitment to quality.Closing Thought on Warehouse Native MartechThe shifting tides of warehouse-native technology are promising but they come with a fair share of skepticism. This shift is not just a simple tool swap, but a nuanced evolution requiring careful understanding and strategic decision-making, shaped by a company's unique needs and data maturity. Is zero data copy really achievable?Does it save costs for the customer? Or does it benefit the cloud warehouse companies?How long will local database copies be a requirement?Can compute charges be solved with higher quality queries?Will warehouse native martech affect more enterprise or startup companies?Does warehouse native martech replace the need for reverse ETL pipelines?Yet, amid the complexity, and all the questions, a promise shines through - a future of reduced data pipelines, seamless integration, and more efficient, direct data access. The challenge, as well as the opportunity, lies in the journey towards that future, a journey fueled by the symbiosis of pioneering tools and clean data.You heard it here first folks: As we navigate the transformation to warehouse-native martech, the single most critical action is to prioritize achieving high-quality, well-structured data; it's the golden key to unlocking the full potential of these emerging tools and strategies.✌️--Intro music by Wowa via UnminusCover art created with MidjourneyMusic generated by Mubert https://mubert.com/render 

What's folks, today I'm pumped to be joined by Michael Katz, CEO and co-founder at mParticle, the leading independent customer data platform.Summary: In the contentious debate over Packaged and Composable CDPs, Michael delivers a clear-eyed perspective that cuts through the hype. Rejecting the idea that Pacakged CDPs are becoming obsolete, he emphasizes the continued importance of data quality, integrity, and privacy, and he warns against becoming entangled in marketing illusions. He also highlights the need for adaptability, dismissing some of the more pervasive myths in the martech landscape, such as the magic of zero copy data. With strategic acquisitions, mParticle is focusing on intelligence and automation, aiming to be more than just “simple pipes” in data management. Michael's insights provide a grounded roadmap, focusing on genuine value creation and thoughtful navigation of the complex industry that is Customer Data Platforms.About Michael Michael got his start as an analyst at Accenture and later focused on customer acquisition and marketing strategy for a mobile content company He entered the entrepreneurial world founding interclick in 2005, a data-valuation platform for advertisers He ran the company as President and took the company public in 2009 and sold to Yahoo in 2011 for $270M  He's been on the Board of Directors for several companies including Adaptly and BrightLine He's a volunteer at Southampton Animal Shelter He's also a Mentor at Techstars After a year as VP of Optimization and Analytics at Yahoo after his company's acquisition, Michael took on his second venture, co-founding mParticle in 2013 mParticle is a global, remote-first company that provides a real-time AI customer data platform.  They help get the highest quality customer data to any system that marketers or product managers use – ultimately improving customer experiences.  They work with big players and small, fueling the customer success of brands like Paypal, Seatgeek, Venmo, Headspace, Lyft, McDonalds, and Airbnb. Unpacking the 8 Components of Customer Data PlatformsWhen asked about Arpit Choudhury's enumeration of the eight essential components of Customer Data Platforms (CDPs), Michael's response was swift and assertive. With an appreciative shoutout to Arpit for articulating the complex aspects of CDPs, he aligned himself with the eight facets laid out in the question.These eight components, according to Michael, indeed compose an end-to-end solution for the first generation of CDPs.  They include: CDI, customer data infra, collect 1st party event data from customers from website and apps ETL, data ingestion, extract data from other tools and load it into DWH Data Storage/warehousing, store a copy of data collected Identity resolution, a solution for tying together a customer's various interactions with you across multiple platforms and devices Audience segmentation, drag and drop UI Reverse ETL, extract/activate from DWH to other tools Data quality, validity, accuracy, consistency, freshness, completeness…  Data governance and privacy compliance, user consent, HIPAA compliance Emphasizing the integrated nature of these components, Michael asserts that the value of the whole system is greater than the sum of the individual parts. He proudly reflects on mParticle's reputation as a complete CDP and emphasizes that many existing CDPs lack strong stories around data quality and governance.The conversation with Michael reveals his confidence in the synergy that arises when these parts function together. He cautions against skipping any of these steps, underscoring that a weak foundation will undermine the entire system. Speed in data processing should not compromise quality and privacy protection, and mParticle's holistic approach ensures this balance is maintained.Takeaway: Michael's insights into the eight essential components of CDPs not only align with industry experts but also highlight the importance of a unified approach. By valuing integration, quality, and consumer privacy, mParticle positions itself as a leading player in the CDP landscape. The wisdom shared by Michael emphasizes that genuine value is derived not merely from the individual elements but from the careful orchestration of all parts into a coherent and resilient system.Debunking the Myths Around Reverse ETL and Composable CDPsReverse ETL and composable CDP proponents assert that the traditional CDP is becoming obsolete and that the future lies in Composable CDPs that leverage modern data warehouses and processes like Reverse ETL. Claiming that existing CDP vendors will have to adapt to this shift or risk becoming irrelevant.Michael's written extensively about this debate over the years. He argued that product marketing around the composable CDP is just modern day sleight of hand tricks…designed to dupe the buyer. To be fair, mParticle has adapted to the rise of the modern data stack by offering services like data warehouse sync and value-based pricing. Michael highlighted the rise of the Cloud Data Warehouse as an essential system within organizations, but he was quick to emphasize that the real challenges lie in maintaining data quality, integrity, and privacy. As he elaborated, legacy CDP vendors like mParticle deliver value not in the storage of data, but in the movement and activation of it. Michael stressed the importance of going beyond mere data collection to understanding the context and the “why” behind customer behavior.According to Michael, the true value in the CDP space has shifted towards enhancing context, improving understanding, and introducing an insights layer. For mParticle, this has translated into a focus on finding truth and meaning in their data, creating an infinitely optimizing loop. He vehemently argued against reverse ETL, characterizing it as “garbage in, garbage out,” and took aim at what he described as “sleight of hand” tricks in product marketing designed to distract from the real issues.Michael challenged several narratives in the debate, dismissing the importance of zero data copy, the vulnerability of CDPs to security threats, and the notion of faster deployment times leading to sustained value. He warned against getting enticed by aggressive product marketing, stressing that what might appear easy to implement could be hard to maintain.Takeaway: The transformation of CDPs isn't just about new technologies or marketing tactics but lies in understanding the true needs of customers. With a focus on integrity, context, and sustained value, Michael exposes the fallacies in current debates, emphasizing that real success comes from creating genuine value, not just noise.The Realities of Replacing Traditional CDPs with Reverse ETL ToolsWhen asked about the growing trend where some reverse ETL customers have found ways to replace their traditional Customer Data Platforms (CDP) with reverse ETL tools, Michael acknowledged that this represents only a very narrow subsegment of the market. He expressed a concern that the fragmented “Do It Yourself” approach isn't always a practical solution, particularly for most businesses within the enterprise sector.Michael pointed out that during the pandemic, certain habits had developed, often driven by data engineers working with limited perspectives and without a comprehensive understanding of the complexities of running successful digital marketing campaigns. This lack of integration and understanding has led to an increasing need for a return of the decision-making power to the marketers.Highlighting the importance of usability, Michael described how mParticle is designed to make it easy for marketers to contextualize and activate data in a low code, no code manner. This approach stands in contrast to other CDPs and modern data stack tools that require intricate knowledge of SQL scripts and schema. A significant portion of his argument revolved around the practical challenges of troubleshooting across multiple different systems. He explained that when a business relies on eight or more different systems to serve the purpose of an end-to-end CDP, it introduces a unique set of complexities. If something goes wrong, troubleshooting becomes an intricate web of challenges involving different account managers. In Michael's words, “the whole thing becomes a bit of a mess.”Takeaway: Michael's insight sheds light on the realities of replacing a traditional CDP with reverse ETL tools. The fragmented approach may work for some but presents complexities and challenges that might be impractical for the broader market. Usability, integration, and streamlined workflows are highlighted as essential elements for optimizing business value, suggesting that while there are different paths to success, a straight line is often the fastest and most efficient route. The emphasis on integration over “hobbyist” solutions presents a compelling argument for businesses looking to evolve in the ever-changing landscape of martech.Debunking the Myth of Zero Copy Data in MartechWhen Michael was asked about the notion of zero copy data, he didn't mince words, immediately cutting through the hype to lay bare the underlying realities. He expressed skepticism about the idea that zero copy data is a magical solution, pointing to the assumption that copying data creates inefficiency and additional access cost.Michael argued that the cost of storage isn't the main driver of expenses; it's the cost of compute. He believes that creating duplicate copies of data doesn't drastically change costs and, moreover, that there's considerable efficiency to be gained by replicating data for different uses and use cases.He also emphasized the importance of focusing on the value side of the equation. Minimizing costs is essential to maximizing investable resources for growth, but it shouldn't overshadow the primary goal of driving customer value. Michael expressed concern that focusing on zero copy data might lead businesses down the wrong path, solving for a non-existent problem.His perspective on the issue extended to a critique of some reverse ETL companies. He noted that they often face a churn problem, luring customers in with the promise of an “easy button” only to disappoint when reality doesn't meet expectations.Takeaway: Michael's dismantling of the zero copy data concept offers a vital reminder that not all that glitters is gold in the world of martech. By focusing on the practicalities of costs and the importance of efficiency and value, he encourages businesses to ask the right questions and prioritize what truly matters. His argument against zero copy data serves as a caution against getting swept up in appealing but potentially misguided solutions, emphasizing instead a thoughtful approach to data management that delivers real value.Examining the Warehouse Native Approach to MartechWhen Michael was asked about the increasing trend of warehouse native approaches in martech and its potential impact on companies with large volumes of non-revenue-generating users, his response was insightful. He broke down the question into specific elements, focusing on both the technological and practical aspects of this approach.He acknowledged the structure of a typical marketing tech stack, with various components like analytics, customer engagement platforms, experimentation tools, and customer support services. However, he questioned the real beneficiaries of having all these tools built natively on the Cloud Data Warehouse. He emphasized that the benefit might lie more with the data warehouse provider than with the customer.Michael also pointed out that as different vendors leverage multiple datasets and run their own compute cycles on the data warehouse, it's not necessarily clear if that would result in cost savings. He challenged the assumption that avoiding multiple copies of data would inherently save money, stating that there hasn't been enough side-by-side comparison to substantiate this belief.He concluded that whether it's through a company like Snowflake or mParticle, they are, in essence, reselling cloud compute in different forms. Simply assuming cost savings because of a lack of data duplication might not hold true in practical terms.Takeaway: Michael's analysis of the warehouse native approach in martech opens a nuanced conversation about the real-world implications of this trend. By examining who benefits from this strategy and challenging the common assumption that it leads to cost savings, he encourages a more critical evaluation. The discussion underscores that what might appear as an intuitive solution needs more robust evidence and careful consideration to understand its true value and impact.The Insights Layer of mParticle's Approach to Customer DataIt's getting harder and harder to track the packaged vs composable battle these days, there's a ton of overlap with so many tools: ETL tools adding rETL features while rETL tools and CDIs becoming composable CDPs CDPs adding product analytics and AI features while product analytic tools adding CDP and AI features CDPs adding marketing automation features while MAPs adding CDP features CDPs also adding “warehouse connectors” or “warehouse sync”  Adding an interesting layer to the debate here is extending the capabilities of the CDP into new areas. mParticle made some interesting acquisitions over the last few years: Aug 2022 Vidora, AI personalization platform for customer data Jan 2022 Indicative, a customer journey analytics platform to address data entropy With these capabilities, mParticle is adding an intelligence layer that not many CDPs have. Not only are they capturing and helping customers move data around, they're helping them make sense of the data, look back to see what happened and also make predictions on what will happen.Initially, mParticle's efforts were directed at solving mobile data collection challenges, aiming to set up organizations on a durable and scalable API-based system. By addressing unique mobile data challenges that no one else was confronting, they sought to position themselves at the center of mass for many consumer brands.According to Michael, the solution to these challenges led to mParticle's focus on multi-channel data challenges, revolving around vital components like data quality, governance, and identity resolution. Identity resolution, Michael believes, remains one of the most misunderstood aspects of the whole process.But the vision didn't stop there. The evolution went beyond these challenges, aiming at what would come next: intelligence and automation. The acquisitions of Vidora and Indicative, as Michael revealed, probably accelerated mParticle's roadmap by four or five years.Michael brought to light mParticle's ambitious strategy to move beyond mere segmentation tools and “simple pipes.” As Michael argued, many existing tools are like “simple pipes” that do exactly what you tell them to do. However, mParticle's approach aims to be an intelligent force that moves the industry forward.Michael's discourse paints a picture of a company that's not just satisfied with optimizing first-generation capabilities. It's a story of looking ahead, focusing on intelligent pipes and striving to put customers in the best possible position to extract value from their first-party customer data.Takeaway: By focusing on next-generation capabilities and accelerating their roadmap through strategic acquisitions, mParticle is positioning itself as a leading force in the evolving landscape of martech. The compelling insight is their move towards intelligent pipes that can make sense of the data, not just move it around, guiding the industry into a new era of customer data understanding and utilization.The Vidora Acquisition: Empowering Marketers with Machine LearningWhen asked about the acquisition of Vidora and its integration into mParticle's CDP offering, Michael dove into the compelling dynamics behind this strategic move. The conversation revolved around AI tools like IBM's Watson Studio, Amazon SageMaker, and Google's AutoML, which are generally built for data scientists. What set Vidora apart, however, was its design to be accessible to knowledge workers and marketers, aligning with the founders' vision to democratize machine learning.Michael was keen to clarify that many tools in the market offer a single type of machine learning, often centered around propensity scores. But Vidora went beyond, impressing him with the building of diverse ML pipelines. The suite enabled regression testing, propensity scoring, uplift analysis, and more, without constraining the types of intelligence or automation that customers could access.According to Michael, the uniqueness of customer data demands tailored solutions, as no two customers' data look, shape, or behave the same way. With Vidora, now branded as Cortex, mParticle has extended a full suite to users that align with various channels. The seamless integration of models within mParticle allows marketers to create, sync, and activate models effortlessly, accommodating different channels from paid advertising to customer support.But what really resonated with Michael's view was how this acquisition tackled a common industry problem: the gap between the creation of cool models and their actual implementation into production. Most in-house models never see the light of day, and those that do are often channel-specific, failing to transcend their original context. Cortex, on the other hand, offers flexibility without channel dependency, backed by mParticle's robust and diverse set of connectors.Takeaway: mParticle's acquisition of Vidora, rebranded as Cortex, has redefined the machine learning landscape for marketers. It provides a versatile and accessible set of tools that break down conventional barriers and facilitate the practical application of models across diverse channels. By doing so, it empowers marketers to extract greater value from data and paves the way for a more intelligent and integrated approach to customer engagement.Innovating Martech Pricing: A Fresh Approach to Value-Based PricingWhen asked about the recent shift in mParticle's pricing structure, Michael delves into the exciting philosophy behind this change. He emphatically expresses that the change isn't merely superficial, but rather a product of innovation, something that's more than just a re-packaging of their pricing model. Michael explains the need for de-averaging or de-aggregating pricing, acknowledging that the traditional charging based on users or events is fairly straightforward, but it doesn't capture the full picture. According to Michael, not all events, users, or use cases hold equal value, and treating them as such creates a logjam through the system. This one-size-fits-all approach undermines the ability to provide marketers with appropriate solutions.The heart of the problem is that this logjam prevents Customer Data Platforms (CDP) from having access to all necessary data, typically due to how they are priced. Michael highlights that when they analyzed how customers were using mParticle, they discovered three distinct use cases: real-time event federation, data maintenance for historical lookup and redundancy, and targeting and personalization.With this fresh approach, mParticle managed to “unclog the pipes” of data, allowing it to flow where needed and at the right pace. This shift allowed for acceleration in audience calculation and refresh, and extended the look-back window on real-time audiences from a mere 90 days to perpetuity without sacrificing performance.Takeaway: Michael's insights into mParticle's new pricing structure reveal an innovative and necessary departure from traditional user or event-based pricing. By recognizing the unique value in different data points and use cases, mParticle has managed to not only create a more effective pricing model but also to enhance the functionality and efficiency of their platform. It's a lesson in understanding the complex dynamics of the martech space and the importance of aligning pricing models with actual value and functionality.Empowering Black Founders with TechnologyOne of the coolest discoveries when digging through Michael's socials is that he actually created Tech for Black Founders. He got together with a list of data vendors to provide free software to early-stage startups led by Black founders, as part of an initiative to support Black technologists and entrepreneurs, who currently make up only 1% of founders backed by venture capital in the US. In the midst of 2020, during a peak of social unrest, he found himself pondering how his company, mParticle, could serve the community better. It was more than a fleeting thought; it was a shower epiphany that would soon spark a wave of empowerment for black technologists and entrepreneurs.Michael's initiative, which might seem simple, was profound. Recognizing that black founders made up less than 1% of those backed by venture capital in the U.S., he set out to make a difference. The idea was to provide free software from leading tech companies to early-stage, black-led start-ups. The aim was to bridge the equity gap, offering services usually costing six to seven figures to those underrepresented.He texted friends and fellow founders from braze, amplitude, branch, and more. His proposal was met with instant approval, and a simple application page was launched. What happened next was nothing short of extraordinary. The initiative went viral, with 50 to 100 companies reaching out, eager to contribute, and the movement continues to grow, now encompassing hundreds of companies offering their services to black and other minority tech founders.Takeaway: Michael's leadership in rallying tech companies to offer free software to black and minority tech founders is a powerful example of how one person's idea can ignite a movement. It underscores the importance of community and collaboration, and showcases a tangible effort to close the equity gap in the tech industry. Simple, immediate, and impactful, it's a testament to what can be achieved when passion meets purpose.Finding Balance and Joy in a Multifaceted LifeWhen asked about how he remains happy and successful amidst his diverse roles as a founder, writer, sports fanatic, dad, animal shelter volunteer, mentor, and board member, Michael's response is a reflection of self-awareness, clarity, and wisdom. His take on balancing a life filled with various passions and responsibilities is both refreshing and deeply inspiring.First and foremost, Michael's priority is being a dad, a role he deems his most important job. Everything else, whether it's being a CEO or a board member, follows in sequence. He admits that although he doesn't always follow his own advice, the goal isn't merely about becoming proficient at navigating the ups and downs of company building and the entrepreneurial journey. Instead, it's about transcending these fluctuations and reaching a state of equanimity.Michael stresses that the pursuit isn't happiness itself; rather, the pursuit is happiness. Finding joy, meaning, and growth in whatever he's doing is what keeps him motivated and content. He measures his alignment with his work by his excitement every Monday morning and his anxiety every Friday for not getting enough done. If those feelings begin to reverse, that's his cue to reassess his path.Takeaway: Michael's philosophy on balance and happiness is a profound lesson in understanding one's priorities and embracing the journey itself as the source of joy. His words are a reminder to find contentment in the pursuit, to align passions with purpose, and to recognize the importance of self-awareness in living a fulfilling life. His perspective turns the conventional wisdom of “work-life balance” on its head, offering a unique insight into living a life filled with meaning and happiness.Michael Teases Exciting Announcements from mParticleWhen asked if there was anything he wanted to share with the audience or any exciting things launching soon, Michael's response was filled with enthusiasm and intrigue. He hinted at some compelling announcements coming from mParticle in September. Without divulging specific details, he provided a glimpse into what the company is focusing on.Michael mentioned that these new developments would continue to expand on their mission of creating value. They are looking to transpose their services and add value not just in their own data store but across any data store, including the data warehouse ecosystem. Though he kept the specifics under wraps, the anticipation in his voice was clear. The audience was left eagerly awaiting the “cool stuff” that mParticle has in store.Episode RecapThe martech industry is no stranger to bold claims and sweeping predictions, and the recent debate around Reverse ETL and Composable CDPs is no exception. The air is thick with assertions that traditional CDPs are going the way of the dinosaur, set to be replaced by sleek, modern solutions. Michael, however, has a more grounded take.For starters, he considers the buzz around Composable CDPs to be a well-executed marketing illusion, a sleight of hand rather than a genuine revolution. Sure, modern data warehouses and Reverse ETL processes are capturing attention, but at the core, the need for data quality, integrity, and privacy still reigns supreme. Michael doesn't view this shift as a death blow to existing CDP vendors like mParticle, but rather a call to adapt, focusing on the movement and activation of data.Adaptation is a theme that resonates throughout Michael's insights. While acknowledging that some Reverse ETL customers are indeed replacing traditional Customer Data Platforms, he emphasizes that this trend represents a narrow slice of the market. The fragmented “Do It Yourself” approach has its limitations, especially when applied to the complex landscape of enterprise-level marketing. Here, mParticle's approach stands out, prioritizing usability and enabling marketers to contextualize and activate data without becoming entangled in intricate coding.Michael doesn't shy away from debunking popular narratives in the debate, including the myth of zero copy data. Cutting through the hype, he directs attention to the real drivers of expenses and underscores the importance of focusing on customer value over cost-cutting.Perhaps the most intriguing aspect of Michael's perspective lies in the strategic evolution of mParticle. The company's recent acquisitions, including Vidora, an AI personalization platform, signal a commitment to intelligence and automation. Moving beyond simple data collection and segmentation, mParticle aims to become an intelligent force that drives the industry forward. Their tools aren't mere “simple pipes”; they're designed to meet the unique needs of customers and provide tailored solutions that enhance understanding and value extraction.All and all, Michael offers a refreshingly realistic and actionable perspective on the current CDP landscape. Rather than getting caught up in marketing tricks or chasing after the latest shiny object, he encourages a return to core principles and a commitment to intelligent, adaptable solutions. It's an approach that recognizes the complexity of the industry while providing clear pathways for growth, innovation, and genuine value creation.Whether you're a marketer, data engineer, or business leader, listen below for insights that offer a solid foundation for navigating the ever-complex world of martech and data platforms, without falling prey to illusions or unnecessary complexity.✌️--Intro music by Wowa via UnminusCover art created with Midjourney

Summary: Arun clarifies 'warehouse-native' and 'connected' concepts, positioning Castled.io as a flexible solution that caters to specific customer needs. He addresses challenges in traditional martech, such as the disparity between customer base size and value derived, and presents Castled.io's unique solutions like an alternative pricing model and immediate data access. Arun navigates the issues of a warehouse-native approach, providing strategies for handling real-time data and minimizing compute charges. He cautions against seeing warehouse-native adoption as merely an escape from reverse ETL, emphasizing its potential to resolve existing martech problems and enhance functionalities. Arun encourages a positive attitude towards new, complex technologies, recognizing their transformative potential.About Arun Arun is a data engineer by trade with over a decade of experience building and scaling systems in the startup ecosystem He started his career in software engineering roles at Applied Materials, an enterprise semiconductor manufacturer and later MiQ, a programmatic advertising media partner Arun then joined Flipkart, known today as India's largest e-commerce marketplace with a whopping 150 million customers  He then moved to the startup world joining Hevo Data as one of the first tech hires, a No-code ETL Data Pipeline platform that enables companies to consolidate data from multiple software In 2021, Arun moved to San Francisco to co-found his first startup, Castled Data - A warehouse-native customer engagement platform that sits directly on top of cloud data warehouses Along with his team of founders Arun was selected by YC in the Winter 22 batch From Open Source Reverse ETL Tool to Warehouse Native CEPWhen asked about the transformational journey of Castled.io, Arun shed light on the genesis of the company's vision. It was a time when businesses wanted to move their data from warehouses to various tools, yet the market lacked the means to do this efficiently. Recognizing this gap, Arun embarked on the mission to develop an open source, reverse ETL solution. His concept was founded on the idea that no one-size-fits-all tool could cater to the wide range of companies' diverse requirements.This venture brought Castled.io a fair amount of traction, with many companies employing their open source solution in-house, and a growing clientele availing of their cloud-based offering. However, around this time, a critical analysis of the martech landscape provoked a pivot. Arun realized the long-term sustainability of reverse ETL solutions was questionable, especially with the burgeoning concept of warehouse-native apps. Other companies were beginning to develop their own reverse ETL tools.Arun observed that these ETL solutions were not truly designed for data teams but rather marketing growth teams, signaling a limitation in their scope. The need to constantly shift data to different platforms like Intercom was dwindling, given alternative and more efficient methods emerging in the martech ecosystem. In fact, he believed that the popularity of these reverse ETL solutions might begin to wane within a year.The most crucial feedback that inspired the transformation of Castled.io came directly from its target audience – the marketers. They indicated that a reverse ETL solution did not fully resolve their challenges, especially in scenarios where handling large amounts of data became a bottleneck for their existing tools. It became clear that simply copying data from warehouses to another tool wasn't an effective solution.Prompted by these revelations and the rising acceptance of the warehouse-native concept, Arun and his team decided to pivot. They transitioned from being an open-source reverse ETL tool provider to building Castled.io as a solution directly layered on top of data warehouses. This move allowed them to bypass data migration issues and directly cater to the marketers' needs.Takeaway: The journey of Castled.io highlights the importance of remaining adaptable and receptive to market changes and customer feedback. This awareness allowed the company to evolve from being an open-source reverse ETL tool to a robust, warehouse-native solution, directly addressing marketers' challenges. The company's pivot is a testament to strategic foresight and innovation in the martech space.The Similarities of Open vs Closed and Composable vs Packaged CDPsIn the fiery debate around composed versus packaged CDPs, Arun weighed in with his unique viewpoint. He likened the contrast between these two approaches to the difference between open source and closed source systems.From Arun's perspective, the appeal of composable CDPs lies in the flexibility they offer. This format enables innovation on top of the data warehouse, unlike the constraints potentially imposed by a packaged system. If something isn't quite right, with a composable CDP, you're able to add more tables, create more transformations, and even integrate external tools. Arun cited examples like Mutuality and Thing, tools that perform identity resolution on top of the data warehouse. These systems, instead of operating deterministically, utilize fuzzy resolution. They identify rows that may be the same and join them together - an innovative process executed directly within the data warehouse. Such possibilities underscore the value of composable CDPs. Being locked into a closed system inhibits the ability to incorporate these innovations into one's data warehouse, a limitation he finds less appealing. Though there are countless other arguments surrounding this topic, Arun emphasizes this angle as one often overlooked in the broader conversation.Takeaway: In the composable vs. packaged CDP debate, Arun highlights the flexibility and potential for innovation offered by composable CDPs. By likening them to open-source systems, he underscores the opportunities to customize and integrate additional tools directly on top of the data warehouse, an often overlooked yet crucial consideration in the martech space.Unpacking the Definition of Warehouse-Native MartechWhen asked about the varying definitions in the martech space, particularly 'warehouse-native' and 'connected', Arun addressed these terms with a refreshingly pragmatic viewpoint. He observed that while the industry is caught up in different terminologies, often what doesn't fit into these boxes is what the customer actually wants.Arun described his understanding of warehouse-native as akin to the framework offered by Snowflake, where everything runs atop the data. A connected app, in his view, is one that separates compute and data - the data resides in a warehouse, not in the SaaS app, providing the flexibility we've discussed before. The actual computations happen on internal clusters, streamlining operations by removing the need for API integrations, enhancing consistency and security, and reducing data movement.Yet, for Arun, the appeal of warehouse-native martech extends beyond these definitions. The true advantage lies in its potential to transform data into a goldmine of information that can fuel powerful reporting and analytics. The ability to write data back to the data warehouse creates a wealth of opportunities for customers, a feature he deems as a significant boon of connected apps and warehouse-native tech.Despite these perspectives, Arun chooses not to classify Castled.io strictly as a warehouse-native or connected app. Instead, he emphasizes meeting customer needs. For some enterprise customers, the security of not moving data to an external system like Breeze or Iterable is paramount. Here, he sees value in solving problems directly atop the data warehouse, utilizing its compute capabilities. However, he recognizes that this isn't a universal requirement, with many mid-market companies comfortable with moving data to different tools.Emphasizing flexibility, Arun noted that focusing solely on solving problems within the data warehouse could limit the potential for real-time or transactional use cases. To balance this, Castled.io allows users to create segments directly atop the data warehouse. Once a segment is created, it's cached to a segment architecture designed to power real-time use cases.Takeaway: While the martech industry grapples with terms like warehouse-native and connected apps, Arun emphasizes the need to focus on customer requirements. He sees the benefits of these concepts but underlines the importance of flexibility, using warehouse-native capabilities where they add value and enabling other options where they serve specific use cases better. This customer-centric perspective holds the potential to influence the way we think about the future of martech solutions.The Problems with Traditional MartechWhen asked about the challenges plaguing traditional martech and how his company, Castled.io, presents solutions, Arun had a wealth of insights to share. There's a significant disconnect, he explained, between the number of customers a company maintains and the actual value derived from them. This discrepancy was particularly noticeable among smaller B2C companies with millions of subscribers but limited income. Traditional martech solutions claim their pricing aligns with the value the customer extracts, but in Arun's view, this assertion doesn't hold water. Castled.io, however, he says, offers a more sensible alternative. Its pricing model doesn't center on the volume of data or compute capacity utilized. Instead, it correlates with the number of team members leveraging the tool, an arrangement Arun believes more accurately reflects the true value provided to the customer.He further unpacked the issue of data look-back periods, pointing out that especially for certain B2C companies, like travel agencies planning vacations, having only a six-month data retention limit makes re-engagement efforts nearly impossible. Traditional ecommerce companies could similarly benefit from having access to data from past holiday seasons, enabling them to strategize more effectively.Arun also criticized the inefficiency of marketers waiting for data engineers to fulfil their requests, a process that can take months. In contrast, Castled.io offers immediate access to data stored in the warehouse, overcoming this time-consuming hurdle.Takeaway: Traditional martech has its shortcomings, notably the disconnection between customer numbers and value derived, and restrictive data look-back periods. However, Arun emphasizes that Castled.io is leading the charge in overcoming these hurdles, with its unique pricing model based on team member usage and its efficient, immediate access to data.The Role of Clean Data Warehouses in Warehouse Native Martech ToolsWhen Arun was asked about the significance of clean data warehouses for operating any warehouse native martech tools, he agreed with the concept, but offered an intriguing observation. According to Arun, the common notion that only enterprises and mid-market companies maintain clean data warehouses doesn't hold water.He revealed that surprisingly, many small to medium-sized businesses (SMBs) also maintain high-quality data warehouses. Arun explained that this trend could be attributed to the increasing awareness among businesses about the importance of data quality. They understand that shoddy data quality could pose issues down the line, and therefore, they focus on data quality from day one.Arun mentioned an interesting phenomenon among SMBs. These companies, although smaller in size, often have a higher data quality than mid-market and enterprise businesses. Arun attributes this to the hands-on approach of the CTOs at these smaller companies who directly oversee these matters, implementing best practices such as data contracts.Arun also highlighted that warehouse-native tech isn't just for large B2C companies with loads of customers. Castled.io, while it primarily targets B2C customers, is also used by B2B customers for tasks like drip and email marketing.Arun concluded by emphasizing that a clean data warehouse is indeed a prerequisite for any warehouse native tech. This, he suggested, is evidenced by the popularity of reverse ETL tools, which send data from the data warehouse back to these tools.Takeaway: Clean data warehouses are critical for the effective operation of warehouse native martech tools. Interestingly, SMBs are often leading the pack in maintaining clean data warehouses due to a proactive approach towards data quality right from the get-go. It highlights the broader applicability of warehouse native tech beyond just large B2C companies, as businesses of all sizes strive to achieve better marketing results.Navigating the Challenges of the Warehouse-Native ApproachAccording to other industry experts, the key challenges faced by companies in adopting a warehouse-native approach include:  Real time data: There's often a delay in getting data in the warehouse. Many marketers are faced with a nightly sync in their warehouse which makes real-time campaigns tricky Compute charges: For teams that are lucky enough to have real time data, there's the added cost of compute charges / creating a load on your DWH/Snowflake that can add up quickly  Schema ownership: The data team usually owns the schema and it might not always be in a format that marketing can do something with it. Example: creating a table for every product event vs creating a product event table and having the events be columns  DWH access: A lot of companies will block access to the data warehouse because it has sensitive info in it... even if you aren't reading it, they just don't want the access point Arun offered his in-depth insight and experiences to provide a well-rounded view of these complex issues.Real time data challengesArun explained that the real-time data challenges within a warehouse-native approach can be addressed by leveraging APIs and strategic data management. He split it up into two distinct use-cases of real-time data:  reacting to events in real-time  handling real-time data itself. The first scenario, reacting to real-time events, is already addressed by Castled.io. Arun provided an example of an e-commerce company sending a push notification or email when an order is shipped. By integrating with the Castled.io API, they're able to send these notifications within milliseconds, utilizing data directly from the data warehouse. This speed is enabled by caching user segment data on their end, catering to a majority of real-time use cases they encounter.The second scenario, real-time data management, involves cases like newsletter sign-ups, where data may not instantly exist in the data warehouse. Here, Arun shared how customers resolve this: when making the API call, they pass all the contextual data of the event. Since the data associated with a new user sign-up isn't scattered across multiple sources, this method provides a timely and efficient solution. The collected data can then be used for personalization and other activities.Computing Charges, The Hidden Pain Point of the Data WarehouseArun acknowledged this as a common problem in existing cloud data warehouses. He emphasized the potential adverse impacts these charges could have on the data warehousing ecosystem if not properly managed. But with strategic use of optimized SQL queries and warehouse-native applications, these costs can be minimized and optimized. Despite the associated costs, Arun maintained that many would still favor a data warehouse due to the immense value it brings. Particularly when it comes to marketing, where substantial budgets are often deployed, utilizing a data warehouse to its fullest extent becomes crucial.Arun explained that once a company decides to leverage its warehouse for marketing, it must choose the most efficient way to operate. Either data engineers can write queries to create segments and handle the data, or applications running atop the data warehouse can execute these queries. Arun pointed out that a key reason for the escalating compute charges is the hiring of inexperienced analytics engineers, whose lack of optimized SQL queries knowledge adds to these costs.Arun explained how his team prides itself on paying careful attention to even minor filter additions in their audience builder query tool. He noted that a thorough load testing process is carried out to ensure customers don't have to pay any unnecessary costs. For him, this is way more scalable. Despite the inevitability of compute charges, utilizing a warehouse-native application ensures these costs are minimized and optimized.Understanding the Intricacies of Schema Ownership and Data Warehouse Access Arun was frank about the limitations in addressing the challenges of schema ownership and data warehouse access. He acknowledged that these hurdles are more about facilitating an understanding among the people involved. He emphasized the need in educating the relevant parties about the potential value that's being left on the table. However, if a company insists on restricting marketer access to their data warehouse, the solution may be out of reach.Arun indicated that the number of companies unwilling to grant marketers access to their data warehouse is relatively small. He stated that even enterprise customers are not frequently faced with such restrictions.He admitted that beyond educating the individuals involved about the benefits of allowing marketers access to their data warehouses, there isn't much else they can do. He elaborated that Castled.io can show how they write optimized queries on a data warehouse compared to data engineers, but if a company is still hesitant to give marketers access to their data warehouse, the situation is beyond their control.Takeaway: Addressing these complex issues requires a deep understanding of the technical landscape and the value that real-time data, well-managed compute charges, and sensible schema ownership can bring to an organization. Arun's perspective illuminates the path to navigating these challenges, ensuring data is used optimally and efficiently.Playing the Fast-Paced Game of Overlapping Martech Data ToolsNavigating the current martech landscape can feel like a high-stakes game of chess, with tools shifting and overlapping in functions almost as quickly as one can keep track.  ETL tools adding rETL features while rETL tools and CDIs becoming composable CDPs CDPs adding product analytics and AI features while product analytic tools adding CDP and AI features CDPs adding marketing automation features while MAPs adding CDP features CDPs also adding "warehouse connectors" or "warehouse sync" - basically acknowledging that the warehouse is essential, and they need to catch up on ETL and reverse ETL Arun presented a comprehensive view of the current state and predicted trajectory of the industry considering the labyrinthine tangle of martech data tools. Recognizing that consolidation is a looming certainty, he detailed the implications of such a shift in the landscape.Arun highlighted the inherent overlap between various data technologies, and spoke about how this redundancy is fueling an inevitable unification of tools. He recalled a conversation with a colleague who, after a detailed analysis of their tech stack, could potentially cut their toolset in half due to existing redundancies.Moving onto the complex interplay of transformations and Extract, Load, Transform (ELT) tools, Arun shed light on their evolution. He noted that transformation tools, such as DBT, are now included in data warehouse offerings like Snowflake, Google's data form, and many others. Simultaneously, ELT solutions are expanding their repertoire to include reverse ELT capabilities.In Arun's view, this overlap is driving a constant pruning of tools from the data stack. Every tool needs to adapt, evolve and carve its own unique position in the modern data stack to stay relevant. He noted that survival in this space might not always be a given.Expanding on the idea of consolidation, Arun brought up the evolution of tools that collect events like CDPs. He sees the possibility of these tools offering product analytics, thereby diversifying their functionalities. This continuous absorption of capabilities will lead to the shrinking of data stacks and trigger a battle between composable and single solution platforms.In Arun's vision of the future, the role of data warehouses could undergo a fundamental transformation. They could metamorphose into powerhouses of databases with the capability to store vast amounts of data. As this happens, vendors may pivot to building packaged CDPs around these data warehouses, heralding a shift in the industry's architecture.Takeaway: Martech is in a state of continuous flux, with data tool overlap and feature consolidation driving a relentless contraction of the data stack. This evolution may culminate in data warehouses morphing into mega-databases, around which packaged CDPs are built. In this scenario, every tool will need to adapt and find its unique niche within the modern data stack to remain relevant. The competitive landscape promises to be intriguing, with potentially transformative effects on the industry.Martech's Shift to Warehouse Native: A Threat to Reverse ETL?One question looms large: will the move to a warehouse native approach render reverse ETL and data pipelines redundant? Arun was asked about this potential change and how it could reshape the landscape for data-driven marketers. In a scenario where marketing and customer engagement tools sit atop the data warehouse, absorbing information directly from the source, the need for a third-party intermediary like reverse ETL might seem diminished. Arun's answer to this is as complex as it is insightful.While he acknowledged that a shift to warehouse-native solutions could, in theory, eliminate the need for reverse ETL, he urged caution. The impetus behind adopting a warehouse-native solution should not solely be to sidestep reverse ETL, he argued. Instead, the focus should be on solving existing issues in the martech stack, enhancing functionalities, and driving more value for the end-users.Arun referenced companies like Customer.io that have already started incorporating reverse ETL into their platforms. If the motivation behind warehouse-native tools is solely to eliminate reverse ETL, he believes their lifespan in the industry may be short. The way forward, according to Arun, lies in leveraging the power of warehouse-native approaches to overcome limitations of existing tools and to drive innovation in the martech space.Takeaway: Warehouse-native solutions might theoretically replace the need for reverse ETL, but a successful pivot relies on problem-solving and enhancing existing functionalities. In the evolving martech space, the focus should be on creating value and meeting user needs, rather than simply seeking to disrupt existing processes.Optimism, Apprehension, and The Pace of AI in MarketingArun was asked about one of the hottest topics in marketing today: artificial intelligence (AI). We inquired specifically about the challenges AI and machine learning (ML) pose in potentially transforming or even replacing current marketing roles. This is a topic that resonates with many early-stage marketers who find themselves caught between fear and optimism.Arun's viewpoint on this topic provides clarity, drawing from his understanding of the field. He referenced a past podcast episode where the host mentioned the threat of rapid AI innovation. Indeed, one significant worry is the potential obsolescence of innovations built on older AI models like GPT-2 as newer versions, such as GPT-5 or GPT-6, are developed.From Arun's perspective, this highlights a fundamental limitation of AI. While current AI models, like transformers, can solve problems related to natural language processing, the road to artificial general intelligence (AGI) is longer and more complex. AGI refers to a type of AI that can understand, learn, and apply knowledge across a wide range of tasks, closely mirroring human intelligence.When it comes to significant innovation in AI, Arun believes it's largely confined to big tech giants like OpenAI, Google, and Facebook. These entities have enormous resources at their disposal, making it challenging for smaller players to compete. The journey to disruptive AI innovations is arduous, and in Arun's view, it's likely to take more time than most people anticipate.While acknowledging ongoing AI innovations within many companies' internal systems, including marketing tools, Arun expressed skepticism about their sustainability. He pointed out that once the tech giants develop AGI, smaller innovations might be overshadowed or rendered obsolete. Therein lies a key issue: who should be innovating in AI, and why, given the rapid pace of change and the dominance of major players?Arun concluded by reflecting on the future of AI, considering it entirely possible that many roles, even podcast hosts, could be replaced by AI. But the crux of the matter, he stressed, is determining who will lead these innovations and when.Takeaway: While the advent of AI brings with it both challenges and opportunities, its path to replacing current marketing roles isn't straightforward. The question isn't merely what AI can achieve but who's driving its evolution and at what pace. Future changes will depend on advances in AGI and the balancing act between smaller innovations and the dominance of tech giants in the field.Striking Balance in Success and Happiness: Insights from a Founder's JourneyWhen asked about how he strikes a balance between all his roles - a CEO, co-founder, cricket fan, movie buff, and dad - and still manages to stay happy and successful in his career, Arun had some insightful thoughts to share.Arun recognized early on that a significant portion of the entrepreneurial journey involves learning from failures. He recalled a moment when a series of potential customers did not convert, sending him into a state of disappointment. However, he learned to adjust his mindset, understanding that things won't always go as planned, particularly for first-time founders.Today, he approaches his work with a different attitude, seeing each customer as an opportunity. If one doesn't convert, he simply moves on to the next, a sign of maturity and growth that has evolved over time.Yet, the life of an entrepreneur can be stressful and hectic. Arun has found ways to alleviate the stress and maintain balance. He spends quality time with his young daughter and friends. He recognizes that while his work is an important part of his life and career, it's crucial to separate personal life from professional pursuits. This, he believes, helps him to stay productive and manage his stress better.Takeaway: Success in one's career, especially in the high-stakes world of startups, isn't just about achieving business milestones. It's also about maintaining a healthy balance between work and personal life, learning from failures, and evolving as an individual. Arun's story underlines the importance of these facets in fostering both happiness and success.Embracing new Tech Even if It's ComplicatedWhen asked if there was anything he wanted to share with the audience, Arun urged everyone to be more open to the world around them, implying that not everything is as intimidating as it might seem at first glance.Arun acknowledged the common misconceptions about tech, specifically around emerging fields like warehouse native technology. However, he strongly encourages a more open-minded exploration of these advancements. He believes that understanding their potential and pushing past the initial fear and doubt is key.Highlighting his own faith in these technologies, Arun mentioned how he would not have left his high-paying job to pursue this venture if he did not believe in its feasibility. His faith in the technology's potential isn't based on blind optimism but a firm belief in its transformative possibilities.Takeaway: The world of tech, particularly emerging fields, can seem daunting. But Arun's experience underscores the importance of staying open-minded and pushing past initial misconceptions. Embracing change, after all, is often the first step to leveraging the opportunities that accompany it.Episode RecapArun provides a straightforward understanding of 'warehouse-native' and 'connected' in the context of martech. He explains 'warehouse-native' as akin to Snowflake's model, where all processes run on the data, highlighting its potential to turn data into valuable insights for powerful reporting and analytics. The 'connected' approach is where data resides in a warehouse, with computations happening on internal clusters, enhancing security and reducing data movement. However, Arun doesn't pigeonhole Castled.io as either; instead, he underlines the importance of catering to specific customer needs and balancing flexibility to allow for real-time and transactional use cases.Addressing traditional martech's challenges, Arun speaks of a significant mismatch between a company's customer base and the value derived. Castled.io proposes a solution through its unique pricing model that correlates with the number of team members using the tool rather than data volume or compute capacity. Additionally, he tackles the issue of data look-back periods, emphasizing the benefits of having access to past data for strategic planning. In contrast to traditional martech that makes marketers dependent on data engineers, Castled.io offers immediate access to data stored in the warehouse.Acknowledging the challenges of a warehouse-native approach, Arun discusses real-time data, compute charges, schema ownership, and DWH access. He offers solutions like leveraging APIs and strategic data management to address real-time data challenges, and optimized SQL queries and warehouse-native applications to minimize and manage compute charges. Arun believes that despite the costs, the value a data warehouse brings, especially for marketing, is immense.The shift towards warehouse-native martech raises questions about the future of reverse ETL and data pipelines. While acknowledging the potential redundancy of reverse ETL in a warehouse-native scenario, Arun stresses that the adoption of warehouse-native solutions should focus on solving existing martech issues, enhancing functionalities, and driving value for end-users. The key to the future, according to Arun, lies in leveraging warehouse-native approaches to overcome existing tool limitations and drive innovation in the martech space.Finally, Arun encourages openness towards new technology, despite its complexity. He dispels misconceptions about emerging tech like warehouse-native solutions and encourages pushing past initial fears and doubts to understand their potential. His faith in these technologies is based not on blind optimism but a conviction in their transformative possibilities.Listen for a comprehensive, nuanced, and accessible journey through the world of warehouse native martech, composable CDPs and reverse ETL.

Summary Data pipelines are the core of every data product, ML model, and business intelligence dashboard. If you're not careful you will end up spending all of your time on maintenance and fire-fighting. The folks at Rivery distilled the seven principles of modern data pipelines that will help you stay out of trouble and be productive with your data. In this episode Ariel Pohoryles explains what they are and how they work together to increase your chances of success. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Your host is Tobias Macey and today I'm interviewing Ariel Pohoryles about the seven principles of modern data pipelines Interview Introduction How did you get involved in the area of data management? Can you start by defining what you mean by a "modern" data pipeline? At Rivery you published a white paper identifying seven principles of modern data pipelines: Zero infrastructure management ELT-first mindset Speaks SQL and Python Dynamic multi-storage layers Reverse ETL & operational analytics Full transparency Faster time to value What are the applications of data that you focused on while identifying these principles? How do the application of these principles influence the ability of organizations and their data teams to encourage and keep pace with the use of data in the business? What are the technical components of a pipeline infrastructure that are necessary to support a "modern" workflow? How do the technologies involved impact the organizational involvement with how data is applied throughout the business? When using managed services, what are the ways that the pricing model acts to encourage/discourage experimentation/exploration with data? What are the most interesting, innovative, or unexpected ways that you have seen these seven principles implemented/applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working with customers to adapt to these principles? What are the cases where some/all of these principles are undesirable/impractical to implement? What are the opportunities for further advancement/sophistication in the ways that teams work with and gain value from data? Contact Info LinkedIn (https://www.linkedin.com/in/ariel-pohoryles-88695622/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Rivery (https://rivery.io/) 7 Principles Of The Modern Data Pipeline (https://rivery.io/downloads/7-principles-modern-data-pipeline-lp/) ELT (https://en.wikipedia.org/wiki/Extract,_load,_transform) Reverse ETL (https://rivery.io/blog/what-is-reverse-etl-guide-for-data-teams/) Martech Landscape (https://chiefmartec.com/2023/05/2023-marketing-technology-landscape-supergraphic-11038-solutions-searchable-on-martechmap-com/) Data Lakehouse (https://www.forbes.com/sites/bernardmarr/2022/01/18/what-is-a-data-lakehouse-a-super-simple-explanation-for-anyone/?sh=54d5c4916088) Databricks (https://www.databricks.com/) Snowflake (https://www.snowflake.com/en/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

In this bonus episode, Eric and Kostas preview their upcoming conversation with Chris Sell of GrowthLoop.

Brought to you by OneSchema—Import CSV data 10x faster | Mixpanel—Event analytics that everyone can trust, use, and afford | Brave Search API—An independent, global search index you can use to power your search or AI app—Austin Hay is currently Head of Marketing Technology at Ramp and was previously the VP of Business Operations at Runway, the VP of Growth at mParticle, and the fourth employee at the unicorn Branch Metrics. In 2022 he sold his online course, the Marketing Technology Academy, to Reforge, where he now teaches Martech and has a program launching in the fall. He's consulted on Martech and growth for companies including Notion, Airbnb, Robinhood, Postmates, Walmart, JPMorgan Chase, and many others. In today's podcast, we discuss:• What exactly marketing technology is• What a Martech person can do for your business• When to hire a Martech person and what to look for• Austin's favorite tools• Advice for doing attribution• Frameworks on tooling, systems, and building vs. buying• How to apply the concept of “thinking gray” to make better decisions in work and life—Find the full transcript at: https://www.lennyspodcast.com/the-ultimate-guide-to-martech-austin-hay-reforge-ramp-runway/#transcript—Where to find Austin Hay:• LinkedIn: https://www.linkedin.com/in/austinahay/• Threads: @austinahay—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• Twitter: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Austin's background(03:58) What marketing technology is(06:17) The difference between typical growth roles and Martech(10:23) Signs you need a Martech person on your team(14:03) Hiring and placing a Martech person in B2B, B2C, and B2B2C businesses(21:15) A day in the life of a Martech professional (25:05) Marketing technology vs. marketing operations (31:14) Tooling recommendations(41:49) The never-ending struggle of how to do attribution well(50:47) Emerging tools and platforms to keep an eye on(55:26) MMM modeling(57:47) What to look for when hiring a Martech professional, and Austin's favorite interview questions(1:02:45) His red flags for companies and “false flags” for potential hires(1:04:51) His favorite frameworks(1:13:37) Lightning round—Referenced:• Siqi Chen on LinkedIn: https://www.linkedin.com/in/siqic/• Austin's marketing technology course on Reforge: https://www.reforge.com/courses/marketing-technology• Notion: https://www.notion.so/• Sri Batchu on Lenny's Podcast: https://www.lennyspodcast.com/lessons-from-scaling-ramp-sri-batchu-ramp-instacart-opendoor/• Cody Morgan on LinkedIn: https://www.linkedin.com/in/cody-morgan/• Braze: https://www.braze.com• Marketo: https://business.adobe.com/products/marketo/adobe-marketo.html• Mparticle: https://www.mparticle.com/• Segment: https://segment.com/• Snowflake: https://www.snowflake.com/• Reverse ETL: a primer: https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb• RudderStack: https://www.rudderstack.com/• Hightouch: https://hightouch.com/• Mike Molinet on LinkedIn: https://www.linkedin.com/in/mikemolinet/• Thena: https://www.thena.ai/• Salesforce: https://www.salesforce.com/• Gong: https://www.gong.io/• How today's top consumer brands measure marketing's impact: https://www.lennysnewsletter.com/p/how-todays-top-consumer-brands-measure• About MMM: https://www.marketingevolution.com/marketing-essentials/media-mix-modeling• Recast: https://getrecast.com/• The Contrarian's Guide to Leadership: https://www.amazon.com/Contrarians-Guide-Leadership-Steven-Sample/dp/0787967076• The Art and Adventure of Leadership: https://www.amazon.com/Art-Adventure-Leadership-Understanding-Resilience/dp/1119090318/• Suits on Netflix: https://www.netflix.com/title/70195800• Our Flag Means Death on Prime: https://www.amazon.com/Our-Flag-Means-Death-Season/dp/B0B8N4R4X1• What We Do in the Shadows on Hulu: https://www.hulu.com/series/what-we-do-in-the-shadows-0b10c46a-12f0-4357-8a00-547057b49bac• Silo on AppleTV+: https://tv.apple.com/us/show/silo• Cal.com: https://cal.com/• Brian Balfour on the Startup Dad podcast: https://www.startupdadpod.com/coping-with-the-loss-of-a-child-and-protecting-your-time-brian-balfour-father-of-2-ceo-and-found/• Amplitude: https://amplitude.com/• AppsFlyer: https://www.appsflyer.com/• Customer.io: https://customer.io/• Branch: https://www.branch.io/• HubSpot: https://www.hubspot.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

What's up folks, today we're extremely privileged to be joined by Boris Jabes, the Co-Founder & CEO at Census. Boris is originally from Ottawa, Canada where he went on to study Computer Science at the University of Waterloo  He got his start at Microsoft where he spent 7 years in various Program Manager roles leading C++ and 3D graphics for Visual Studio He then moved to SanFrancisco to co-found a password manager tool called Meldium, backed by Y Combinator  In 2014 he sold the startup to LogMeIn where he became a Senior Director for a year and a half – before jumping into Angel Investing in which he took part in startups like Canvas, Endgame, Lambda and Reflect In 2018, Boris Co-founded Census where he's also CEO today. Census is a reverse ETL tool that allows marketers to activate customer data from their data warehouse  Boris is also a podcaster, in 2021 he and his team launched The Sequel Show which counts over 30 episodes with some of the smartest minds in data and is one of the greatest resources to help marketers bridge the gap with data teams The Thread Connecting Password Management and Reverse ETLBoris's first venture was into password management, but it wasn't out of love for passwords. It stemmed from a frustration with the scattered nature of employee identities across numerous apps. Each login seemed to represent a different version of oneself. The solution? A first-of-its-kind enterprise-grade password management tool designed for teams, aimed at streamlining the login process for any office application.Boris describes this as a quest to create a federated version of oneself - a concept known as Single Sign-On (SSO). Behind the tech jargon, the aim was simple: to make people's lives easier by reducing the friction caused by hundreds of passwords.His journey then led to Census, a reverse ETL venture. Again, the core issue was fragmented identities, but this time, it was the customers' identities in question. Why were these identities inconsistent across different divisions within a company?Just as with the password management venture, Boris saw the need for a central place from which customer identities could be federated. He was addressing the same problem but from a different angle.Boris's focus has always been on alleviating the pain points created by disparate data. From password management to reverse ETL, he continually seeks to resolve identity disparities, a testament to the power of innovation that lies at the intersection of distinct yet interconnected problems.Experiencing the Pain Point Firsthand: The Genesis of CensusIt was when Boris's first startup was acquired that he truly felt the problem Census would later solve. Joining forces with LogMeIn, a larger company with a keen interest in their software and user base, illuminated a stark issue. The marketers and salespeople at LogMeIn wanted to engage with the users and cross-sell the software, but they struggled. The key issue? They didn't seem to have a clear understanding of what the users were doing.Despite the availability of tools to connect data, none seemed to coalesce the company around a single version of reality. The tools used by marketers were different from those used by salespeople. These fragmented solutions failed to bring everyone onto the same page, especially considering that product behavior was becoming an increasingly important driver.The seed for Census was thus sown. Boris and his team envisioned a solution that would work at scale, bridging the gap between different divisions and providing a unified view of customer data. The challenge was technical, but the ultimate goal went beyond that. They aimed to empower various stakeholders – marketers, salespeople, product teams, finance – to take action based on reliable, trustworthy data.Census was born out of the need to solve a real problem – to provide a single version of truth that would allow different divisions within a company to understand and act upon user behavior efficiently and effectively. This venture underlines Boris's ability to observe, understand, and respond to the intricate problems arising from fragmented data, paving the way for more streamlined operations and decision-making within organizations.Unraveling the Concept of the Packaged Customer Data PlatformAs we delve deeper into the realm of martech, there's no escaping the maze of terminology and definitions, especially when it comes to the concept of the Customer Data Platform (CDP). From a distance, it might seem like yet another acronym tossed into the complex landscape, but understanding it is essential.In Boris's view, the packaged CDP (or pack CDP) is a lineage of software specifically designed with marketers in mind, most often serving B2C companies (at least initially). But what does it really do? It performs three critical functions: It helps collect events from your website and applications. It serves as a source of truth for that data specifically for the marketing team. It enables the segmentation and personalization of targets based on this data into other marketing tools. Whether it's channeling information into advertising platforms or feeding into an email or direct mail tool, a packaged CDP is designed to facilitate these processes. But remember, it's not just about the technical definition. It's about building tools that people find genuinely useful, solving real problems and creating value.A packaged CDP is more than just another piece of software. It's a testament to the evolving world of marketing technology and the efforts to streamline data management and utilization in the B2C sector. It stands as a symbol of how understanding data can help us build more efficient, more effective systems for connecting with customers.Building a Composable Stack: The Shift in the Martech LandscapeBoris highlights the major shift in the martech landscape, specifically focusing on the evolution of customer data platforms (CDPs). When examining the traditional packaged CDP model, it's clear that these platforms often duplicate data from existing company databases, such as a data warehouse. However, this model assumes that all companies have a data warehouse, which is not always the case, especially for smaller startups.In recent years, there's been a considerable increase in companies investing in data warehouses or other forms of data platforms. These platforms, such as Google, Snowflake, Amazon, or Databricks, can store a massive amount of data and are used to answer a wide array of questions. Therefore, duplicating this data to solve problems seems counterproductive.With this in mind, Census was built differently. It was designed from first principles, focusing on giving marketers more trustworthy data without contributing to unnecessary data duplication. The tool connects directly to a company's existing data warehouse, eliminating the need to recreate a separate customer database. This, in turn, saves both the data and marketing teams a significant amount of time.This shift is part of a broader trend towards composable solutions. Composability, in this context, refers to a philosophy where components of a system are designed to work together seamlessly, fostering flexibility. Each piece of the system can be customized and interacts fluidly with the others.Today's customer journey is more complex than ever, spanning across multiple touchpoints and channels. Add in the added complexity of privacy regulations and it's clear that marketers require more flexible, adaptable tools to handle their customer data effectively. This evolving landscape necessitates a shift towards a more composable stack of tools centered around the data warehouse.Defining the Composable CDP: Flexibility, Scalability and CustomizabilityWhen we dive into the world of Composable Customer Data Platforms (CDPs), it can feel like stepping into an intricate labyrinth. Navigating this complex terrain involves understanding the key facets that differentiate it from traditional packaged CDPs.A Composable CDP can be best defined as a marketing system that integrates diverse, best-in-class software components. Each component caters to a specific function or data task, resulting in a highly tailored and flexible solution designed to meet unique business needs. This modularity gives marketers the agility to switch components as and when their business requirements evolve.The core advantage of the Composable CDP lies in its flexibility. As our fellow marketer pointed out, traditional CDPs often enforce limitations on the number of custom attributes or on the complexity of data manipulations, creating a 'one-size-fits-all' solution. In contrast, Composable CDPs empower marketers with unlimited attributes and advanced customization possibilities, even accommodating nuanced SQL operations.This leads us to a secondary, yet vital, benefit – the ease of integration. Composable CDPs are built to work seamlessly with existing systems, such as data warehouses, forming the backbone of marketing operations. The fear factor associated with these complex systems is mitigated through intuitive user interfaces, rendering them accessible to a wider marketing audience.Now, to the economic aspect of it. With Composable CDPs, marketers are no longer bound by the constraints of a user or event-based payment structure. Given that these platforms leverage existing storage resources, costs are dramatically reduced.Finally, Composable CDPs serve as a holistic source of truth, capable of assimilating data from a wide range of systems like point-of-sale, billing systems, data science models, and even offline data. This approach results in faster implementation timescales, from months to mere days or even hours, depending on the state of your data warehouse. The Composable CDP is scalable, adjustable, and most importantly, built to work for you, not the other way around.As the era of large internal martech teams building in-house solutions fades into the annals of marketing history, the rise of the Composable CDP opens the doors to a world of infinite possibilities, ready to be harnessed by marketers of today and tomorrow.Bridging the Gap: Collaboration Between Marketing and Data TeamsThere's a prevalent notion within the industry that data warehouses and marketing teams operate on different spectrums, leading to friction when attempting to implement tools such as Customer Data Platforms (CDPs). Yet, it's crucial to reassess this standpoint as data increasingly becomes an integral part of marketing strategies.The idea of data teams and marketing teams operating in silos is gradually fading. Forward-thinking businesses encourage their data teams to construct the stack and equip the marketing team with the necessary data for use cases. This collaborative approach paves the way for smoother communication and facilitates a more holistic view of the data.Furthermore, this cooperative model propels a change in teaching and learning dynamics within the organization. For instance, data analysts are educated about the real-world applications of data in marketing strategies, such as segment creation, email targeting, and personalizations. On the other hand, marketers get insights into the intricacies of data tools, transformations, and models. These cross-functional insights lead to a synergistic relationship that enhances the overall functioning of the business.While packaged CDPs are marketed as tools that don't necessitate a data engineer, the reality can be quite different. Even technically adept marketers often find themselves needing the assistance of engineers to navigate the complexities of tools like Segment. Thus, acknowledging the interdependence of these roles can facilitate a more efficient and cost-effective implementation of CDPs.Ultimately, fostering better relationships between marketing and data teams is a win-win scenario for all parties involved. Not only does it mitigate potential conflicts, but it also streamlines regulatory and compliance procedures, ensures consistency in metrics, and increases the flexibility of the business's data operations. As such, bridging this gap offers a more seamless and efficient approach to utilizing data in marketing strategies.Embracing the Composable Stack and reverse ETLThe journey towards a cohesive and integrated work environment between data and marketing teams is undoubtedly a gradual process. However, the strategic advantage of aligning everyone around the shared objective of growing the company significantly outweighs the time investment.Reflecting on the evolution of startups and marketing technology over the past decade, there was a time when these departments operated without a dedicated data team. Instead, marketing and engineering teams manually built individual integrations with each tool through APIs, a process that was time-consuming and often complex.Fast forward to today, and marketers can now readily partner with data teams, thereby streamlining the process and maximizing data utility. This evolution highlights the emerging trend of the composable stack, which involves unbundling tools and functionalities to create a more tailored and efficient workflow.The shift from conventional packaged CDPs to a more composable stack approach is gaining momentum. Tools like Census, for instance, offer a compelling alternative to traditional CDPs by interfacing directly with data warehouses like Redshift. These tools allow data teams to build or leverage existing transformations seamlessly and without the need for managing individual APIs or worrying about bi-directional syncing.In essence, tools like Census handle the heavy lifting behind the scenes by fitting data into what downstream tools like Iterable or Salesforce require. This includes navigating challenges such as adhering to API quotas and managing system failover, which are notoriously complex to build internally.The concept of 'reverse ETL' encapsulates this process, allowing marketers to get more data at their fingertips and enabling them to personalize the customer experience more effectively. However, it's not just about better data access; it's about reducing the grunt work for data teams, work that often goes unnoticed and unrewarded.In conclusion, the future of data and marketing integration lies in embracing the composable stack and reverse ETL. These innovations not only enhance operational efficiency but also pave the way for more personalized and data-driven marketing strategies.The Emergence of 'Reverse ETL': Bridging Data and Marketing for Enhanced PersonalizationThe term 'Reverse ETL' first started to gain traction nearly five years ago, amidst the complexities of explaining a new and unfamiliar product. Its emergence was less a strategic branding decision and more a byproduct of early-stage product pitches, wherein the technical intricacies of data extraction and loading could seem somewhat disconnected from the more compelling value proposition – personalizing marketing campaigns.During early customer interactions, when the product's relevance to a marketing context was presented too abstractly, it often resulted in confusion. Therefore, shifting the focus towards describing what the product did, rather than its implications, seemed more effective. That's when users from the data world started comparing the tool to existing ones, like FiveTran and Stitch, but in reverse.Though this comparison seemed overly simplistic, it did help customers understand the flow of data. Soon, the term 'Reverse ETL' began circulating among data teams, first appearing in a Notion document someone had shared about intriguing tools.The term's widespread use, however, didn't truly take off until it was picked up and propagated by venture capitalists via industry thought pieces. Given the excitement for the product was primarily born out of data teams, the term 'Reverse ETL' quickly became synonymous with the new category of tools designed to empower marketing teams with better data.Regardless of its etymology, the term 'Reverse ETL' is less about who coined it and more about what it represents - the technical means for providing marketers with a higher degree of customer targeting and personalization. It has helped establish a clear and efficient route to manage data across multiple platforms.Despite its widespread acceptance, it's important to remember that the primary aim of 'Reverse ETL' is not the technology itself, but its capacity to facilitate marketers to tailor their campaigns more effectively. As the number of data destinations increases, maintaining the efficiency of these operations can be a challenge. By successfully bridging this gap, 'Reverse ETL' helps marketers personalize their approach, leading to improved customer experiences and less 'Dear unknown' type of communications.The Role of Reverse ETL Tools in the CDP Landscape: Enhancing Data Activation, Not Replacing Legacy SystemsThe terms 'Reverse ETL' and 'Data Activation' often go hand in hand, especially when it comes to technical marketers and DevOps teams. In the world of data flow, these teams often use ETL workflows to understand and map out how data moves around. Therefore, when they require a tool that helps them get data from the warehouse to other areas, the phrase 'Reverse ETL' resonates quite effectively.There are critics of the composable route for Customer Data Platforms (CDPs) who argue that the discussion is rooted in an attempt to repackage Reverse ETL tools in a way that appeals to marketers but ends up causing confusion. Some vendors of Reverse ETL tools claim their solutions can replace a legacy or packaged CDP, which can potentially mislead marketers.At Census, we neither replace a traditional CDP nor claim to do so. In fact, many of our customers have been using Census in combination with a CDP for years. We adopt a philosophy of composability – building tools that integrate seamlessly with others, providing users with more trustworthy data in more places without adding complexity or creating new data silos.In the discussion around composability, a crucial point is that it allows the user, whether that's a marketer or a data team, to work with the data seamlessly. In the software world, composability is a well-accepted principle – savvy engineers design their software not to break when combined with other software.If your organization has a tool that does identity resolution, whether that's a CDP or another tool, then we aim to make it seamless for you to use that in tandem with Census. Our goal is to benefit everyone, not just the marketing team.The sales team, finance team, privacy and compliance team – they all need access to that identity resolution too. Therefore, our philosophy of composability is not about Census versus CDPs but about creating a more connected, efficient and user-friendly data environment.Could Warehouse-Native Tools Eliminate the Need for Data Pipelines?Let's step into the fascinating world of warehouse-native tools, where conversations often teeter on the edge of existential questions like "Do we even need data pipelines?" This isn't about futurology. This is about the data landscape we're navigating today, and the possible turns it could take tomorrow.The term 'warehouse-native' may have recently entered the tech vernacular, but Boris has been living it through his work with Census, an enterprise that prides itself on its warehouse-native DNA.The proposition Boris put forth is as audacious as it is appealing. In an ideal world, customer engagement platforms would sit natively on top of data warehouses, rendering separate data pipeline solutions redundant."Wouldn't that be fantastic?" he pondered, adding that it would essentially mean less data duplication and greater consistency - the holy grail for data practitioners. But the pragmatic part of him warned against prematurely packing away your data pipeline toolkits. Census, which began its life as a warehouse-native solution before it was cool, is a testament to Boris's extensive experience in this arena. The product natively connects to data warehouses and activates your data, enabling marketers to employ it in customer engagement platforms. In essence, Census is what you might call a 'translator', morphing complex datasets into comprehensible, actionable insights for marketers.But the challenge doesn't end at merely connecting. Boris highlighted the fundamental prerequisite for this system to operate seamlessly - "you need perfect data in your warehouse to begin with."Here's where things get tricky. Warehouses are optimized for storing tables of data. Yet, platforms like Marketo don't operate on tables of data; they work with users, contacts or similar entities. So, a considerable part of Census's work revolves around shaping the data into a usable form for these platforms.Boris candidly admitted, "I think the need for data pipelines will reduce over time, and that will be a great day. I don't care about data pipelines. But it's like the same way Apple doesn't really try to talk about the megahertz or whatever, it's like, what is the thing you can do?"Looking to the future, Boris envisions an ecosystem where every application acts as a "lightweight cache on the core data warehouse." While that day may not be here yet, there's no denying we're steadily moving towards it.So, next time you find yourself pondering the relevance of your data pipeline solution, remember Boris's words. We may not be ready to discard them just yet, but the tide is certainly turning.Navigating the AI Labyrinth: The Path to Replacing MarketersArtificial Intelligence (AI) is a game changer for every industry, including marketing. While some sectors have been quick to adopt and reap its benefits, there are challenges that need to be overcome for AI to fully replace human marketers. We've been deep down the rabbit hole on AI, we recently did a 4 part series that covered a few AI topics including how to parse out the gimmicky AI tools from the valuable tools marketers should be trying. We talked about things like predictive analytics and propensity models. And we also talked about How fast could AI change or replace marketing jobs. AI has long been a silent partner, behind the scenes, helping marketers place their advertisements strategically on Google and Facebook. By augmenting content generation and fueling these platforms with an increased quantity of options, AI has significantly enhanced efficiency. Those who aren't utilizing AI in this aspect of marketing are falling behind in the race.However, despite its numerous advantages, AI's journey towards replacing human marketers isn't a smooth sail. Boris's take is that the critical challenge lies in our ability to trust AI. The data AI uses and generates can be a double-edged sword. It's hard enough to trust raw data, and AI adds another layer of complexity by making its results subject to its interpretation or, in some cases, its "hallucination."For instance, when addressing critical customer queries or dealing with sensitive issues such as ADA compliance, you wouldn't want AI to provide "hallucinated" answers. It's crucial to restrict and guide these systems, ensuring they deliver correct and safe responses.There's no doubt that smaller companies, driven by the thrill of innovation, might dive headfirst into using AI. Yet, larger enterprises may proceed with caution, driven by the need to maintain data trust and correctness. This presents a golden opportunity for solutions that can help marketers harness the power of AI without compromising on data integrity and reliability.While the notion of AI replacing marketers seems harsh, the future of marketing lies in the balance between automation and human expertise. Marketers who aren't engaging with AI in one form or another are on a risky path. Therefore, embracing AI, yet maintaining a keen eye on data trustworthiness, is a delicate but necessary dance in today's marketing world.The Secret to Balancing Success and Happiness: People FirstIn the fast-paced world of entrepreneurship, it's easy to lose sight of what truly matters. But not for Boris Jabes, co-founder, CEO, investor, speaker, podcaster, and sports enthusiast. He's a man who wears many hats, and yet, he seems to have found the secret to maintaining happiness and success amidst a sea of responsibilities.The answer, it turns out, is quite simple and deeply human - it's all about the people you surround yourself with. According to Boris, the people in your life, whether they're coworkers, friends, or your spouse, have the capacity to uplift you. The power of a supportive network should not be underestimated; it's these connections that energize him and push him to achieve more, all while keeping him grounded.When he walks into his office, it isn't a sense of duty that keeps him motivated, but the anticipation of being around those he respects and admires. These relationships offer more than just professional advantages - they fuel his drive, provide emotional support, and ultimately, make his career journey enjoyable and fulfilling.In Boris's perspective, success doesn't exist in a vacuum. It's cultivated through meaningful relationships, camaraderie, and shared experiences. So, while many are still seeking work-life balance, Boris has found his equilibrium in the people he surrounds himself with. It's a thought-provoking approach, one that truly highlights the importance of nurturing relationships in the pursuit of a happy and successful career.Strengthening Bonds: Bridging Marketers and Data TeamsIn the vast, intricate world of marketing, Census is constantly innovating. They are on a mission to redefine the relationships between data teams and marketers, to create a more harmonious, efficient, and fruitful collaboration. In an ever-evolving ecosystem, their primary focus remains on understanding the nuances of data teams, their aspirations, their challenges, and how we can bridge the gap for better synergy. One of the unique aspects of Census is its dedication to improving collaboration. To make this possible, they are on the brink of launching a set of features aimed at reducing the burden on the engineering teams, making it less of a colossal task to manage. Their goal? To help those who have experienced frustration and feel cornered by the overwhelming demand of data management. On a personal note, as the CEO, Boris is always open for discussion, and eagerly awaits your queries, suggestions, and insights. Despite the company's growth, the communication lines remain wide open. So, reach out, connect, and unravel the complexities of data management and marketing together.Final note, Census is more than excited about the imminent release of their new audience hub and their ongoing community survey for data professionals. So stay tuned! At Census, they believe in the strength of connections, not just between data and marketing teams, but also with their users. Because ultimately, we're all in this together, shaping a more dynamic, responsive, and effective marketing landscape.Links: Data community survey Audience Hub  LinkedIn: https://www.linkedin.com/in/borisjabes/  Twitter: https://twitter.com/borisjabes  Census: https://www.getcensus.com/ 

In this Marketing Over Coffee: Learn about Looker, Reverse ETL, The Flash and more! Direct Link to File Brought to you by our sponsor: The Mailworks and Miro According to BuiltWith over 375k of the top 1M sites are still on UA Looker Updates – 175 new metrics for your GA data The Analytics for […] The post Now With More Illegal Fireworks! appeared first on Marketing Over Coffee Marketing Podcast.

Hightouch co-Founder and co-CEO Tejas Manohar returns as special guest to talk with Mark Rittman about the reverse ETL market today, the evolution of the composable customer data platform and new featured in Hightouch to enrich customer profiles and drive personalization across marketing campaigns.Reverse ETL is Dead (Ethan Aaron LinkedIn Post)Customer 360 Data Warehousing and Sync to HubspotYou don't need the Modern Data Stack to get sh*t doneHightouch Customer StudioHightouch Personalization APIHightouch Match BoosterWhat's in Store for Data Teams in 2023?

Ari Paparo, Eric Franchi from Aperium Ventures are joined by mParticle CEO Mike Katz who explains CDPs, Reverse ETL, and a bunch of other buzzwords. Also Ari learns how to pronounce Nas.Twitter going programmatic: https://digiday.com/marketing/with-advertising-in-flux-twitter-is-outsourcing-ad-monetization-to-ad-tech/ Nvidia and WPP: https://www.wpp.com/en/news/2023/05/wpp-partners-with-nvidia-to-build-generative-ai-enabled-content-engine-for-digital-advertising Visit Marketecture.tv to join our community and get access to full-length in-depth interviews. Marketecture is a new way to get smart about technology. Our team of real industry practitioners helps you understand the complex world of technology and make better vendor decisions through in-depth interviews with CEOs and product leaders at dozens of platforms. We are launching with extensive coverage of the marketing and advertising verticals with plans to expand into many other technology sectors.Copyright (C) 2023 Marketecture Media, Inc.

Drinking from the data firehose? Welcome to the party! Luckily, a whole host of solutions have been rolled out to address the size, scale and scope of data movement projects. Yes, the cloud data warehouse is a common target, but what about all those other systems? Reverse ETL is now taking hold, as companies look for ways to hydrate their systems with the enriched data. Another alternative is data virtualization, a discipline that seeks to dynamically provision data access where and when it's needed. Though not new, there are some new tricks of the trade that make it more viable than ever. Check out this episode of DM Radio to learn more! Host @eric_kavanagh will interview several experts, including Rick Sherman of Athena IT Solutions, Nick Golovin of Data Virtuality, and Taylor McGrath of Rivery.io.

Summary Every business has customers, and a critical element of success is understanding who they are and how they are using the companies products or services. The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it. In this episode Kevin Niparko and Hanhan Wang share the details of how it is implemented and how you can use it to build and maintain rich customer profiles. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Kevin Niparko and Hanhan Wang about Segment's new Unify product for building and syncing comprehensive customer profiles across your data systems Interview Introduction How did you get involved in the area of data management? Can you describe what Segment Unify is and the story behind it? What are the net-new capabilities that it brings to the Segment product suite? What are some of the categories of attributes that need to be managed in a prototypical customer profile? What are the different use cases that are enabled/simplified by the availability of a comprehensive customer profile? What is the potential impact of more detailed customer profiles on LTV? How do you manage permissions/auditability of updating or amending profile data? Can you describe how the Unify product is implemented? What are the technical challenges that you had to address while developing/launching this product? What is the workflow for a team who is adopting the Unify product? What are the other Segment products that need to be in use to take advantage of Unify? What are some of the most complex edge cases to address in identity resolution? How does reverse ETL factor into the enrichment process for profile data? What are some of the issues that you have to account for in synchronizing profiles across platforms/products? How do you mititgate the impact of "regression to the mean" for systems that don't support all of the attributes that you want to maintain in a profile record? What are some of the data modeling considerations that you have had to account for to support e.g. historical changes (e.g. slowly changing dimensions)? What are the most interesting, innovative, or unexpected ways that you have seen Segment Unify used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Segment Unify? When is Segment Unify the wrong choice? What do you have planned for the future of Segment Unify? Contact Info Kevin LinkedIn (https://www.linkedin.com/in/kevin-niparko-5ab86b54/) Blog (https://n2parko.com/) Hanhan LinkedIn (https://www.linkedin.com/in/hansquared/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Segment Unify (https://segment.com/product/unify/) Segment (https://segment.com/) Podcast Episode (https://www.dataengineeringpodcast.com/segment-customer-analytics-episode-72/) Customer Data Platform (CDP) (https://blog.hubspot.com/service/customer-data-platform-guide) Golden Profile (https://www.uniserv.com/en/business-cases/customer-data-management/golden-record-golden-profile/) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) MarTech Landscape (https://chiefmartec.com/2023/05/2023-marketing-technology-landscape-supergraphic-11038-solutions-searchable-on-martechmap-com/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Mark Rittman is joined by Twilio Segment Head of Product Kevin Niparko to talk about trends in the customer data platform market, Reverse ETL and Profiles Sync, the impact of LLMs (Large Language Models) on digital customer experience and Segment Unify, a consumer scale real-time identity resolution solution that provides complete, real-time, portable customer profiles.Segment Unify is here: complete, real-time, portable customer profilesActivate warehouse data in all of your destination toolsCustomer profiles made portableDrill to Detail Ep.94 'Rudderstack and the Warehouse-First Customer Data Platform' with Special Guest Eric DoddsDrill to Detail Ep. 86 'Reverse ETL, Hightouch and CDW as CDP' with Special Guest Tejas Manohar

Summary The customer data platform is a category of services that was developed early in the evolution of the current era of cloud services for data processing. When it was difficult to wire together the event collection, data modeling, reporting, and activation it made sense to buy monolithic products that handled every stage of the customer data lifecycle. Now that the data warehouse has taken center stage a new approach of composable customer data platforms is emerging. In this episode Darren Haken is joined by Tejas Manohar to discuss how Autotrader UK is addressing their customer data needs by building on top of their existing data stack. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Darren Haken and Tejas Manohar about building a composable CDP and how you can start adopting it incrementally Interview Introduction How did you get involved in the area of data management? Can you describe what you mean by a "composable CDP"? What are some of the key ways that it differs from the ways that we think of a CDP today? What are the problems that you were focused on addressing at Autotrader that are solved by a CDP? One of the promises of the first generation CDP was an opinionated way to model your data so that non-technical teams could own this responsibility. What do you see as the risks/tradeoffs of moving CDP functionality into the same data stack as the rest of the organization? What about companies that don't have the capacity to run a full data infrastructure? Beyond the core technology of the data warehouse, what are the other evolutions/innovations that allow for a CDP experience to be built on top of the core data stack? added burden on core data teams to generate event-driven data models When iterating toward a CDP on top of the core investment of the infrastructure to feed and manage a data warehouse, what are the typical first steps? What are some of the components in the ecosystem that help to speed up the time to adoption? (e.g. pre-built dbt packages for common transformations, etc.) What are the most interesting, innovative, or unexpected ways that you have seen CDPs implemented? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDP related functionality? When is a CDP (composable or monolithic) the wrong choice? What do you have planned for the future of the CDP stack? Contact Info Darren LinkedIn (https://www.linkedin.com/in/darrenhaken/?originalSubdomain=uk) @DarrenHaken (https://twitter.com/darrenhaken) on Twitter Tejas LinkedIn (https://www.linkedin.com/in/tejasmanohar) @tejasmanohar (https://twitter.com/tejasmanohar) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Autotrader (https://www.autotrader.co.uk/) Hightouch (https://hightouch.com/) Customer Studio (https://hightouch.com/platform/customer-studio) CDP == Customer Data Platform (https://blog.hubspot.com/service/customer-data-platform-guide) Segment (https://segment.com/) Podcast Episode (https://www.dataengineeringpodcast.com/segment-customer-analytics-episode-72/) mParticle (https://www.mparticle.com/) Salesforce (https://www.salesforce.com/) Amplitude (https://amplitude.com/) Snowplow (https://snowplow.io/) Podcast Episode (https://www.dataengineeringpodcast.com/snowplow-with-alexander-dean-episode-48/) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) BigQuery (https://cloud.google.com/bigquery) Databricks (https://www.databricks.com/) ELT (https://en.wikipedia.org/wiki/Extract,_load,_transform) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) DataHub (https://datahubproject.io/) Podcast Episode (https://www.dataengineeringpodcast.com/acryl-data-datahub-metadata-graph-episode-230/) Amundsen (https://www.amundsen.io/) Podcast Episode (https://www.dataengineeringpodcast.com/amundsen-data-discovery-episode-92/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process Interview Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it? At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape? As the data landscape matures, how have you seen that influence the types of projects/companies that are founded? What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive? The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve? Is there a different architectural paradigm that you see as growing to take its place? How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape? Contact Info Website (https://mattturck.com/) @mattturck (https://twitter.com/mattturck) on Twitter MAD Landscape Comments Email (mailto:mad2023@firstmarkcap.com) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links MAD Landscape (https://mad.firstmarkcap.com) First Mark Capital (https://firstmark.com/) Bayesian Learning (https://en.wikipedia.org/wiki/Bayesian_inference) AI Winter (https://en.wikipedia.org/wiki/AI_winter) Databricks (https://www.databricks.com/) Cloud Native Landscape (https://landscape.cncf.io/) LUMA Scape (https://lumapartners.com/lumascapes/) Hadoop Ecosystem (https://www.analyticsvidhya.com/blog/2020/10/introduction-hadoop-ecosystem/) Modern Data Stack (https://www.fivetran.com/blog/what-is-the-modern-data-stack) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) Generative AI (https://generativeai.net/) dbt (https://www.getdbt.com/) Transform (https://transform.co/) Podcast Episode (https://www.dataengineeringpodcast.com/transform-co-metrics-layer-episode-206/) Snowflake IPO (https://www.cnn.com/2020/09/16/investing/snowflake-ipo/index.html) Dataiku (https://www.dataiku.com/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209/) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) Trino (https://trino.io/) Y42 (https://www.y42.com/) Podcast Episode (https://www.dataengineeringpodcast.com/y42-full-stack-data-platform-episode-295) Mozart Data (https://www.mozartdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/mozart-data-modern-data-stack-episode-242/) Keboola (https://www.keboola.com/) MPP Database (https://www.techtarget.com/searchdatamanagement/definition/MPP-database-massively-parallel-processing-database) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Sylvain Giuliani, Head of Growth and Operations at Census, talks about why CDPs fail. While CDPs have not lived up to their data activation promises, reverse ETL solutions have become a popular alternative for companies looking to centralize and manage their customer data effectively. Reverse ETL solutions allow organizations to efficiently collect, process, and transfer customer data from various sources into a centralized repository. Today, Sylvain discusses activating data using reverse ETL. Show NotesConnect With: Sylvain Giuliani: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Sylvain Giuliani, Head of Growth and Operations at Census, talks about why CDPs fail. While CDPs have not lived up to their data activation promises, reverse ETL solutions have become a popular alternative for companies looking to centralize and manage their customer data effectively. Reverse ETL solutions allow organizations to efficiently collect, process, and transfer customer data from various sources into a centralized repository. Today, Sylvain discusses activating data using reverse ETL. Show NotesConnect With: Sylvain Giuliani: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hilary recovers from COVID while Roger throws various difficult discussion topics at her. They talk about Charlie Javice, Data Day Texas, Posit / Palantir, and classified documents.   Show Notes: JP Morgan Chase lawsuit against Frank Palantir news release Support us on Patreon Roger on Twitter Hilary on Twitter List of NSSD Fellows Get the Not So Standard Deviations book Subscribe to the podcast on Apple Podcasts Subscribe to the podcast on Google Play Find past episodes Contact us at nssdeviations @ gmail.com Podcast art by Jessica Crowell

For the last episode of 2022, I am joined by Martech Expert: Niklas Nikolaidis. Niklas accepted the challenge to explain what he does in words our mom would understand. Niklas is Partner at Arise Consulting Group (ARC) and Curamando where he works with business development around marketing technology ("Martech”) for clients and the 16 companies in ARC's consulting group. During the conversation, we have talked about the exciting world of ‘Martech' and all the changes we have seen over the past year · The differences with Adtech. · The rise of CDP and Reverse ETLs · The Retail Media Opportunity · The rise of composability About Niklas: Besides his role at ARC, he's also a Limited Partner (LP) at FirstPartyCapital, an investment fund investing in adtech and martech startups in Europe and Asia. On the side, Niklas is a board member at student marketing company House of Education and a jury member of Association of National Advertiser's ECHO Awards, an international competition listing the best, data-driven campaigns globally. You can get in touch with Niklas via LinkedIn Digital Explained to my Mom is a production of No-Stress Media. Please check our other shows such as Let's Talk About CX, where we discuss all things Customer Experience.

Det femte och avslutade avsnittet av poddserien som görs i samarbete med Conversionista där vi utforskar fem områden inom CRO. Både nuläget och vart vi är på väg. I det här avsnittet pratar jag med Daniel Lundkvist som är head of analytics om kvantitativ analys. Allt från vad det är och olika typer av kvantitativ analys till hur man arbetar smartare genom att tänka tvärtom. Och hur man ser till att aktivera de insikter man får fram. För kvantitativ analys handlar i grunden om beslutsfattande och att bygga förståelse kring något. Så att vi kan ta bra besult som baseras på data och inte på gissningar. Det här är ett bra avsnitt för dig som vill få en bättre förståelse för vad kvantitativ analys innebär i praktiken för såväl marknadsföring som konverteringsoptimering. Och varför det inte är samma sak som rapportering. Om gästen Daniel Lundkvist är head of analytics och leder analytics-teamet på Conversionista. Ett team som består av ca 20 personer i Stockholm, Göteborg och Malmö. De hjälper företag med att ta data och förvandla den till insikter och saker som går att ta action på. Daniel har lång erfarenhet som konsult och har arbetat med allt från implementation och datakvalitet till analys som är hans kärnkompetens. Han föreläser även såväl internt som externt på olika skolor och utbildningar. Och har bland annat hållit i analytics-delarna i Conversionistas utbildningsprogram, Conversion Manager. Om avsnittet Daniel och jag pratar i avsnittet om vad kvantitativ analys är och dess roll inom både marknadsföring och konverteringsoptimering. Och reder ut skillnaden mellan kvantitativ analys och rapportering. Vi pratar bland annat om det växande ekosystemet av analysverktyg och varför det är så viktigt att bryta ner silos mellan datakällor. Samt hur man kan göra det även på enkla sätt. Daniel ger också inblick i hans process för den kvantitativa analysen. Och varför han tänker tvärtom och börjar med målet. Du får dessutom höra om: De tre typerna av kvantitativ analys Vanliga misstag många gör i sitt analysarbete Hur man undviker dashboards-kyrkogården Varför datakvalitet är så viktig att tänka på Ramverk Daniel använder i sin arbetsprocess Triangulering och dess roll i kvantitativ analys Mängder med konkreta exempel Plus en massa mer… Daniel förklarar också varför det är så avgörande att ha ett actionorienterat förhållningssätt till sin analys och vad det innebär i praktiken. Han delar även vad han anser är de främsta trenderna inom kvantitativ analys framöver. Samt vad han ser är på väg från USA. Du hittar precis som vanligt länkar till allt vi nämnde här i poddinlägget. Plus några extra länkar med bra läsning. Efter länkarna hittar du även tidsstämplar till olika sektioner i avsnittet. Länkar Daniel Lundkvist på LinkedIn Conversionista (webbsida) Conversionista (LinkedIn) Conversion Manager (utbildning) Google Analytics (verktyg) Adobe Analytics (verktyg) Mixpanel (verktyg) Amplitude (verktyg) Power BI (verktyg) Tableau (verktyg) BigQuery (verktyg) Snowflake (verktyg) Jupyter (verktyg) R Studio (verktyg) Hex (verktyg) Census (verktyg) Hightouch (verktyg) Unbundling the CDP - Hightouch (artikel) What is Reverse ETL? The Definitive Guid - Hightouch (artikel) 5 Whys: The Ultimate Root Cause Analysis Tool - Kanbanize (artikel) So what technique från boken Think Smarter av Michael Kallet (artikel) Turning Numbers into Knowledge av Jonathan Garo Koomey (bok) Adlibris / Amazon Storytelling with Data av Cole Nussbaumer Knaflic (bok) Adlibris / Amazon Web Analytics 2.0 av Avinash Kaushik (bok) Adlibris / Amazon Tidsstämplar [4:00] Inleder med att prata om vad kvantitativ analys är och varför det är så viktigt idag. Samt de tre typerna av kvantitativ analys och skillnaden mellan analys och rapportering. [8:03] Om hur Daniel ser på den kvantitativa analysens roll inom marknadsföring.

A conversation with Tejas Manohar & Michael Katz. In this episode I am joined by Tejas Manohar, the CEO of Hightouch Data and Michael Katz the CEO of mParticle. Over the past few years, a new category of customer data platforms has emerged called reverse ETL - data activation, enrichment, and analytics that runs on data on top of the data warehouse. Championed by Hightouch data and numerous other startups, there has been a growing shift in how tech leaders are thinking about the role of the CDP in their business in conjunction with existing data warehouses like Snowflake, GCP, and AWS. We have two of the leading voices in this discussion to talk about the changing role of the CDP category, if reverse ETL makes sense as a category of tech, the modern data stack, the unbundling of CDPs, the value of data in a company, the problem of data waste, and many other topics. Go here for show notes, links, and resources. Subscribe to The Martech Weekly here. Follow Juan Mendoza on LinkedIn and Twitter. Listen on Apple, Spotify, Google, and everywhere else. You can find Tejas on LinkedIn. You can find Mike on LinkedIn.

Over the past two weeks major CDP companies like Segment Twilio and mParticle have announced warehouse native data activation features, commonly known as "Reverse ETL." This week, Scott and Juan break down this growing category in data orchestration and figure out if there's a serious trend happening or if it's all hype. We also touch on the problem of customer identity management, the challenges with breaking down data silos in companies and new ways to think about data management in the enterprise. Watch this show on Youtube Get the transcript and resources mentioned on the show Subscribe to get Big Martech updates Follow Big Martech on LinkedIn and Twitter

Joining us this week is Kashish Gupta, co-founder and CEO of Hightouch. Hightouch is a software for your data stack that syncs any data warehouse to the SaaS tools that your business runs on, making internal usage and sharing easier for everyone. Kashish talks to us about the current state of the modern data stack community and how the industry is constantly pushing forward. He describes how this plays into their sales tactic of “evangelizing” larger corporations by teaching them about “Reverse ETL” and how Hightouch works without pushing the sale. We cover themes such as starting a company with two of his good friends and the procedures they take when it comes to decision-making, the perfect modern data stack, and how to sell your business when the product is something that no one has heard of yet. For The Room Podcast in your inbox every week, subscribe to our newsletter. 6:00 - Where did Kashish grow up and how did that shape his view of the world?8:08 - Did Kashish always want to be a founder?9:27 - How did Kashish's education impact his professional goals?13:08 - What is the story behind Mama’s Cooking?15:01 - What was the “aha” moment that got Kashish thinking about Hightouch?18:10 - How do businesses take advantage of Hightouch?19:56 - How is Kashish's relationship with his partners, Tejas Manohar and Josh Curl?21:28 - How do Kashish's and his partners split up responsibilities and tasks?22:24 - How do Kashish and his partners handle things when there is a disagreement?25:03 - What part of the go-to-market is Hightouch going to continue investing in?27:58 - Who was the first person to say yes to investing in Hightouch? 29:51 - When is the right time for a company to embrace its data warehouse?32:29 - What is Kashish's stance on the semantic layer?34:38 - What are some tools in the modern data stack?35:39 - What tools does Kashish recommend for a company building their modern data stack?38:15 - What advice would Kashish give to an entrepreneur building in this space?40:44 - What’s next for Kashish and Hightouch?44:00 - Who is a woman that has had a profound impact on Kashish and his career? WX Productions

In Data & Impact episode 11, Martin Fiser, Head of Professional Services at Keboola, joins us to dive into the platform as a premiere all-in-one data engineering platform and its role in data management, data governance and reverse ETL. Data & Impact is available on Apple Podcasts, Google Podcasts, Spotify, Stitcher, Pandora Radio & iHeart Radio Follow Martin on LinkedIn: https://www.linkedin.com/in/fisermartin/ Follow Keboola on LinkedIn: https://www.linkedin.com/company/keboola/ Check out their website: https://www.keboola.com/

Operational Analytics. Reverse ETL was ist das? Software wie Census, Rudderstack etc bieten dies nun als Software Lösung an

Ведущие подкаста "Data Coffee" обсуждают новости и делятся своими мыслями! Shownotes: 4:06 Пожар на складе озона 9:50 Python-клиент для airflow api 11:16 Dagster 1.0 11:58 Reverse etl 16:42 'лучший' браузер для винды 23:40 Стриминг экселя 32:47 Мнения разработчиков: проблемы cloud providers 37:16 Gitlab собирается удалять проекты на бесплатных... 38:50 Superset 2.0.0 44:50 Аналитика загрузок одного пакета с npmjs.com 47:57 Китайцы силой мысли управляют домом 51:49 Робот или человек по ту сторону экрана 55:34 Японские учёные обнаружили червей-паразитов, сп... 59:31 Flipper zero — ксерокс радиосигналов или «тамаг... Обложка - Unknown authorUnknown author, CC0, via Wikimedia Commons Сайт: https://datacoffee.link, канал в Telegram: https://t.me/datacoffee, профиль в Twitter: https://twitter.com/_DataCoffee_ Чат подкаста, где можно предложить темы для будущих выпусков, а также обсудить эпизоды: https://t.me/datacoffee_chat

Kashish is a co-founder of Hightouch, which syncs data from warehouses into something that business teams can use and rely on, through a unique process called Reverse ETL. They've raised over $50 million in funding and are growing faster than ever in the data activation space! Today, I talked to Kashish about his origin story, how the company pivoted 6 or 7 times before landing on data activation and where he sees the space moving in the future! FOLLOW Arnav: https://twitter.com/arnavvgarg FOLLOW Kashish: https://twitter.com/kashgupta_ To stay up to date with the podcast: Subscribe on YouTube: https://www.youtube.com/channel/UC9m1vhdkW7tA8NM8r6F9rOA/?sub_confirmation=1. Follow us on Spotify: https://open.spotify.com/show/1GXOT2uv64WEXjXTnMfeOb Follow us on Apple: https://podcasts.apple.com/us/podcast/investigating-pathways/id1554195882 Follow us on Google: https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy80NTJiMWY3MC9wb2RjYXN0L3Jzcw== #investing #startups #entrepreneurship #data

In this episode, you'll hear from Henrik Warfvinge, the Technical Leader of Artificial Intelligence at Google, Kashish Gupta the Co-CEO & Co-Founder at Hightouch, and Evan Kaeding, Lead Sales Engineer at Supermetrics. They'll discuss how to put marketing data into action through reverse-ETL. The limitations of the current data stack that block data teams from making the most of their data Important factors to consider when building an operational marketing data warehouse How reverse-ETL powers data activation What companies can achieve with data activation

Was ist Reverse ETL?

Reverse ETL is a bit of a buzzword these days. But why? In this video Gwen Shapira discusses the new capabilities that reverse ETL introduces to businesses, and especially B2B SaaS with product-lead growth strategy. She also discusses why reverse ETL is more challenging to implement than you may imagine and why stream processing platforms may be a key part of solving these challenges. Reverse ETL is not for everyone - but after watching this video, you'll know why everyone is talking about it.

Show Notes(00:43) Kashish shared briefly about his upbringing in Atlanta and his early interest in STEM subjects.(02:38) Kashish described his overall academic experience studying Economics, Management, and Computer Science at the University of Pennsylvania.(05:53) Kashish walked over the Machine Learning classes and projects throughout his MSE degree in Robotics.(09:02) Kashish shared valuable lessons learned from multiple internships throughout his undergraduate: data science at Implantable Provider Group, investment analysis at Tree Line, and product management at LYNK.(13:14) Kashish told the anecdotes that enabled him to realize his passion for building startups.(17:14) Kashish recapped his learning about venture capital from spending a summer as an analyst in early-stage deep-tech companies at Bessemer Venture Partners in New York.(22:09) Kashish shared learnings from his entrepreneurial stints at an early age.(26:12) Kashish talked through his decision to move to San Francisco after college (Read his blog post explaining how he moved here without a job and a home).(29:04) Kashish recalled his experience working on a project called Carry (an executive assistant for travel on Slack) with his friend Tejas Manohar and going through Y Combinator.(36:40) Kashish shared the founding story of Hightouch, a data platform that syncs customer data from the data warehouse to CRM, marketing, and support tools.(44:15) Kashish emphasized the importance of speed and execution around different pivots that led to Hightouch.(46:35) Kashish unpacked the notion of Operational Analytics, an approach to analytics that shifts the focus from simply understanding data to putting that data to work in the tools that run your business.(49:46) Kashish dissected Hightouch's market-leading Reverse ETL, which is the process of copying data from a data warehouse to operational systems of record.(54:51) Kashish discussed Hightouch Audiences, used primarily by larger B2C customers, that allows marketing teams to build audiences and filters on top of existing data models.(58:09) Kashish explained how the “Reverse ETL” concept fits into the quickly evolving modern data stack.(01:00:26) Kashish shared how the Hightouch team prioritizes their product roadmap, given the high number of customer requests.(01:02:47) Kashish shared valuable hiring lessons to attract the right people who are excited about Hightouch's mission.(01:05:13) Kashish shared the hurdles to find the early design partners and lighthouse customers of Hightouch.(01:08:06) Kashish explained how Hightouch prices by destinations, reflecting the value customers get from using the product and helping them predict costs over time.(01:10:32) Kashish shared upcoming go-to-market initiatives that he is most excited about for Hightouch.(01:14:36) Kashish shared fundraising advice for founders currently seeking the right investors for their startups.(01:17:47) Kashish emphasized the industry recognition of the Reverse ETL market.(01:19:47) Closing segment.Kashish's Contact InfoLinkedInTwitterGitHubWebsiteMediumHightouch's ResourcesWebsite | Twitter | LinkedInData Features | Hightouch Audiences | Hightouch NotifyDocs | BlogCustomers | Careers | PricingMentioned ContentArticles“On Moving to SF Jobless and Homeless” (Aug 2018)“Hightouch Ushers In The Era of Operational Analytics” (March 2021)“The State of Reverse ETL” (May 2021)“What is Operational Analytics?” (July 2021)“Hightouch Has Raised a Series A!” (July 2021)“Hightouch Raises $12M to Empower Business Teams With Operational Analytics” (July 2021)“The Cloud 100 Rising Stars 2021” (Aug 2021)“What is Reverse ETL?” (Nov 2021)Companiesdbt LabsShipyardBig Time DataBook“The Hard Things About Hard Things” (by Ben Horowitz)NotesMy conversation with Kashish was recorded back in August 2021. Since then, many things have happened at Hightouch. I'd recommend looking at:Kashish's piece about Hightouch's transition from Reverse ETL to becoming a Data Activation companyKashish's recent talk at Data Council Austin about the current state of Data Apps built on top of the warehouse and the future as warehouses become even faster.The release of Hightouch Notify that sends notifications on top of the data warehouseHightouch's Series B funding of $40M back in November 2021Finally, Kashish lets me know that back in August, Hightouch were only 25 people. Now, the company is 70-person strong!About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

Pedram Navid is the Head of Data at Hightouch where he leads advocacy for data practitioners, community-building, and the internal data stack. Prior to Hightouch, he has worked as a data engineer building up a modern data stack from the ground up and as a data scientist where he helped scale analytics at a major bank. He contributes to open-source packages and creates data memes on Twitter.In this episode, we cover a range of topics including:- How he entered the world of data science- Reverse ETL- How can new data science professionals evaluate various roles in data science- How can interviewees evaluate team culture- How data gets political within a company- How should data practitioners forge alliances- What is data activation- How he manages data teams

Some of our topic highlights include:The history and background of Monte CarloWhy the planning process is so important for data teamsHow to set (and crush) a data goalThe difference between being obsessed with systems and being obsesses with goalsHow data teams waste timeHow often data downtime and data issues are tied to a lack of knowledge transfer and process within an orgHow we can continue to improve the corporate culture around dataAs always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather. Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. Resources:Barr Moses on LinkedIn Barr Moses on Twitter Barr Moses on MediumMonte Carlo on Twitter Monte Carlo on LinkedIn Monte Carlo's websiteMusic by the talented Joe Stevens. 

Highlights from this week's conversation include:Panel introductions (2:23)What is driving the trend behind Reverse ETL? (5:24)The obstacles to building an internal Reverse ETL tool at scale (15:34)How to decide system management vs. user flexibility (20:14)Why previous products failed in creating this category (29:12)Increased demand and democratization of datastack skills via SaaS (42:03)Broader applications for Reverse ETL (47:29)Limitations of Reverse ETL (55:05)How user technical ability affects design and build roadmaps (58:14)What do you anticipate comes next for Reverse ETL? (1:02:45)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Eric and Kostas preview their upcoming panel discussion on reverse ETL and the modern data stack.

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. Resources:Jessica Cherny on LinkedIn Jessica Cherny on TwitterData Angels on Twitter Ironclad on LinkedIn Ironclad on TwitterIronclad's website Music by the talented Joe Stevens. 

Highlights from this week's conversation include:Boris' background career journey (2:32)The origins of “reverse ETL” (6:39)Reverse Fivetran (16:35)Product as an experience (22:41)Fivetran users vs Census users (24:14)How to add value to a data dump (26:56)Ways companies are creating IP (33:48)The cascade effect of the modern data stack (37:56)Defining “data federation” (43:51)Lessons from building a product (49:10)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Eric and Kostas preview their upcoming conversation with Borris Jabes of Census.

As always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. Resources:Bigeye's websiteBigeye on TwitterBigeye on LinkedinBigeye on MediumEgor on LinkedinMusic by the talented Joe Stevens. 

Reverse ETL is (not surprisingly) the opposite of the traditional ETL process. Its goal is to take data from your data warehouse and send it back to business intelligence, marketing, sales, and operations tools. This process then makes customer data actionable. "Data warehouses contain a lot of data that is typically used to drive analytics, reporting, and so on. These unique data points and insights are locked inside the warehouse and only leveraged for analytics purposes. Reverse ETL allows you to instead take action on that data by syncing it into other business tools."On our latest episode at the Data Unlocked Podcast, Jason Davis, CEO and Co-Founder at Simon Data; and Josh Curl, Co-Founder & CTO at Hightouch sit down and talk about Reverse ETL: What, How and Why It's Taking Off. Listen to this exclusive conversation on Reverse ETL, Hightouch, where people are getting stuck with data and how people can unlock better marketing with better data capabilities.

In this episode, host Kelly Kohlleffel is joined by James Weakley, Co-Founder of Omnata. James and his team are building enterprise integration for the modern data stack. James started Omnata in his spare time while working at NIB, a publicly listed healthcare insurer. NIB introduced Databricks to create predictive modeling which created large data sets. The team quickly faced the issue of storing those 40 million records and being able to access and gain value from that data. Omnata was successfully implemented to solve this challenge as a free app in the Salesforce App Exchange. Since then, James has taken Omnata on full time. They offer an enterprise integration solution with Omnata Connect and a reverse ETL tool with Omnata Push. Listen in to the episode to hear more about how Omnata developed, their GTM strategy, and how James got started from an agriculture technology background. Show Notes: Learn more about Omnata: https://omnata.com Find Opinions, technical guides, and product news from Omnata: https://omnata.com/blog Connect with James on LinkedIn: https://www.linkedin.com/in/james-weakley/ On tap for today's episode: White Russian and a modified "Vegan White Irish Russian" Contact Us: https://www.hashmapinc.com/reach-out

Kashish Gupta, Co-Founder of Hightouch discusses reverse ETL, some reasons for moving data out of a data warehouse, and the critical role that APIs play in the data analytics industry.

Reverse ETL is a product category that evolved from the landscape of customer data platforms with a number of companies offering their own implementation of it. While struggling with the work of automating data integration workflows with marketing, sales, and support tools Brian Leonard accidentally discovered this need himself and turned it into the open source framework Grouparoo. In this episode he explains why he decided to turn these efforts into an open core business, how the platform is implemented, and the benefits of having an open source contender in the landscape of operational analytics products.

https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB); George Trujillo's contact info: email (george.trujillo@datastax.com) and https://www.linkedin.com/in/georgetrujillo/ (LinkedIn) In this episode, Scott discusses three concepts that are at best a concern. Consider it a late Grinch-inspired present for Xmas :) Reverse ETL meets a real need for analytical data being pushed into CRM, marketing, and other similar systems. But treating another pipeline as a first order concern is fraught with the same issues of most similar data pipeline treatment: who owns it, how does it evolve, who is observing/monitoring it for uptime and semantic drift, etc.? Should we look to create data products on the mesh to serve those needs instead of another ETL tool? Some organizations implementing data mesh are forcing their domains to consume any analytics from their own data products on the mesh. The good of this is that it aligns the domain with creating a high-quality data products. But will those data products be designed to fit the general organizational needs or specifically the domain's needs? There is an emerging push for software engineers to also own the data modeling. To get to a place where this is even feasible, don't we need far better abstractions for domains to _do_ the data modeling? And will this overload software engineers that are already dealing with a metric buttload of technologies and requirements already? Where would a junior engineer fit in that kind of organization? Does this mean more software engineers on the team -> 2 pizza teams now 3? 4? 5? 10? Maybe we pump the brakes on this for now? Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or on LinkedIn: https://www.linkedin.com/in/scotthirleman/ (https://www.linkedin.com/in/scotthirleman/) If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/ (https://datameshlearning.com/community/) If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing (here) All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): https://pixabay.com/users/lesfm-22579021/ (Lesfm), https://pixabay.com/users/mondayhopes-22948862/?tab=audio (MondayHopes), https://pixabay.com/users/sergequadrado-24990007/ (SergeQuadrado), https://pixabay.com/users/itswatr-12344345/ (ItsWatR), https://pixabay.com/users/lexin_music-28841948/ (Lexin_Music), and/or https://pixabay.com/users/nevesf-5724572/ (nevesf) Data Mesh Radio is brought to you as a community resource by DataStax. Check out their high-scale, multi-region database offering (w/ lots of great APIs) and use code DAAP500 for a free $500 credit (apply under "add payment"): https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB)

Reverse ETL is the hot, popular trend within the modern tech stack. ETL tools like Fivetran, Stitch, and Matillion make it easy to set up and send data to a warehouse with the click of a button. As software firms of all sizes generate more data than ever, their data warehouses are becoming more and more important in facilitating the rise of operational analytics in various internal organizations. Hightouch is a data platform that helps users to sync their customer data in their data warehouse to their SaaS sales and marketing tools such as Hubspot, Salesforce, Marketo, Zendesk, Gainsight and others. We wrap up Season 2 of our podcast with an interview with Kashish Gupta, a co-founder and co-CEO of Hightouch, discussing the main key innovation in data warehouse technology that facilitated empowering business teams with operational analytics.   https://hightouch.io/blog/ kashgupta.com/

The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data. A natural outgrowth of that capability is the more recent growth of reverse ETL systems that use those analytics to feed back into the operational systems used to engage with the customer. In this episode Tejas Manohar and Rachel Bradley-Haas share the story of their own careers and experiences coinciding with these trends. They also discuss the current state of the market for these technological patterns and how to take advantage of them in your own work.

ETL (Extract Transform and Load) was the SOP for data integration for 25+ years. A decade ago the introduction of data lakes pushed transformation to the end of the process and into tools like Snowflake, BigQuery, and Redshift. Now the latest chatter in the data management industry is Reverse ETL. Shouldn't we call this LTE? Join Tim, Juan and special guest Tejas Manohar, CEO of Hightouch for a conversation about Reverse ETL and why it matters now. This episode will feature: The evolution of data integration pipelines Use cases for Reverse ETL What other acronyms make you smh?

Enterprise data warehouses store all company data in a single place to be accessed, queried, and analyzed. They're essential for business operations because they support managing data from multiple sources, providing context, and have built-in analytics tools. While keeping a single source of truth is important, easily moving data from the warehouse to other applications

Enterprise data warehouses store all company data in a single place to be accessed, queried, and analyzed. They're essential for business operations because they support managing data from multiple sources, providing context, and have built-in analytics tools. While keeping a single source of truth is important, easily moving data from the warehouse to other applications The post Reverse ETL: Operationalizing Data Warehouses with Tejas Manohar appeared first on Software Engineering Daily.

Enterprise data warehouses store all company data in a single place to be accessed, queried, and analyzed. They're essential for business operations because they support managing data from multiple sources, providing context, and have built-in analytics tools. While keeping a single source of truth is important, easily moving data from the warehouse to other applications The post Reverse ETL: Operationalizing Data Warehouses with Tejas Manohar appeared first on Software Engineering Daily.

Enterprise data warehouses store all company data in a single place to be accessed, queried, and analyzed. They're essential for business operations because they support managing data from multiple sources, providing context, and have built-in analytics tools. While keeping a single source of truth is important, easily moving data from the warehouse to other applications The post Reverse ETL: Operationalizing Data Warehouses with Tejas Manohar appeared first on Software Engineering Daily.

In this episode, we speak with Tejas Manohar, Co-Founder of Hightouch, a leading Reverse ETL platform. That syncs data from your warehouse or lake back  into tools your business teams rely on.Top 3 Value Bombs:Organizations should be sending more holistic customer data back into their marketing solutions. Reverse ETL is the process of creating pipelines to extract data from the warehouse/lake and move back into operational components. Utilize CDC when extracting data to minimize the impact to your source system.

We talked about: Data-led academy Arpit's background Growth marketing Being data-led Data-led vs data-driven Documenting your data: creating a tracking plan Understanding your data Tools for creating a tracking plan Data flow stages Tracking events — examples Collecting the data Storing and analyzing the data Data activation Tools for data collection Data warehouses Reverse ETL tools Customer data platforms Modern data stack for growth Buy vs build People we need to in the data flow Data democratization Motivating people to document data Product-led vs data-led Links: https://dataled.academy/ Join our Slack: https://datatalks.club/slack.html