Podcasts about tejas manohar

Time for a Newsroom summarizing everything that's happened in our usual areas of focus, although we are dropping the last two (Zero-Party Data and Future of media) this time around.  ePrivacy & Regulatory Updates Enforcement On September 5th, the CNIL fined CEGEDIM SANTÉ 800,000 euros for processing health data without authorization. The healthcare software provider collected sensitive personal information, assigning a unique identifier for each patient of the same doctor. This method was considered sufficient to ensure that personal data remained anonymous in order to put together certain comparative studies, but the CNIL concluded that, given the risk of re-identification, it could merely be considered pseudonymized, exposing a breach of the GDPR as a result (for starters, patients had not been informed of additional purposes). A Reference was made to the EDPB's Opinion 05/2014 on Anonymisation Techniques.  On September 27th The Irish DPC issued a 91 million euro fine to Meta for storing certain user passwords in plain text files.  On October 22nd, NOYB filed a claim against Pinterest before the French supervisory authority alleging that the company relies on legitimate interest to underpin its behavioral advertising practices, in contravention of the CJEU Bundeskartellamt decision. The social network has also been accused of breaching the transparency principle and not responding to data subject requests appropriately.  On October 24th, the Irish DPC imposed a 310m EUR fine on LinkedIn. The professional social network is not properly applying a valid legal basis for targeted ads and the processing of first party data about their members, despite referring to three separate grounds: consent, legitimate interest and contractual necessity. This has also resulted in a breach of the fairness principle. On October 30th, the California Privacy Protection Agency announced an investigative sweep of data broker registration compliance under the Delete Act. This law requires data brokers to register with the CPPA and pay a fee annually.  On November 6th, the Canadian government ordered the closure of TikTok in the country. Citizens are however allowed to keep using the app, as this is considered a personal choice.  Legal updates and guidelines On October 4th, the CJEU resolved a famous dispute between the Royal Dutch Lawn Tennis Association and the Dutch DPA. The latter had imposed a fine on KNLTB for relying on legitimate interest for sharing data with its sponsors for purposes of direct marketing. Five days later, the EDPB requested comments on its draft Opinion on processing data on the basis of Legitimate Interest: It is made clear that this legal basis should not be treated as a “last resort” as it is of equal value to the rest, and a differentiation is made between an interest (or broader benefit that a controller may have) and a purpose (or specific reason why the data is processed). The Opinion has also stated that an interest must be related to the data controller's activities. On the same day (October 9th), the EDPB adopted its Opinion 22/2024 on certain obligations following from the reliance on processors and sub-processors: every controller should extend the diligence they currently have over direct processors to the entire chain of custody, no matter how many degrees apart.  On October 16th, the EDPB adopted new Guidelines on the technical scope of article 5.3 of the ePrivacy Directive: given that very little has changed since they opened up an initial draft for comments, we recorded a separate episode with Peter Craddock pondering the far reaching implications of these Guidelines.  Turning our attention to the UK, on October 7th the UK ICO launched its own Data Protection Audit Framework including self-assessment toolkits and other practical resources.  Also, the UK Data Protection reform is back, now with a Data Use and Access Bill (with a second reading announced on November 1st). It maintains an exception for analytics cookies that will not require consent. DPOs are back on the table (the previous reform proposal was getting rid of the role).  On November 5th EDPB adopted its first report under the EU-U.S. Data Privacy Framework and a statement on the recommendations on access to data for law enforcement. The redress mechanism has been implemented successfully but it is yet not being widely used. The EDPB has voiced concerns about recent changes to Section 702 FISA and how that could expand the role of private companies in gathering data about EU citizens.  MarTech and AdTech On November 12th, Meta introduced a plan C to its Pay or Consent models, having been told by the EDPB that the current proposal would not be acceptable. A third option (besides paying and relying on behavioral ads) is now available which will use less data and remain mostly contextual. It will also compensate its decreased targeting capabilities with increased audience reach by showing ads (“ad breaks”) that become unskippable for a few seconds. A study conducted by Boston University has concluded that the Protected Audiences API (building on the formerly called FLEDGE protocol, a part of Chrome's Privacy Sandbox), can produce similar results to those of third party cookies in the context of retargeting campaigns.  On November 5th, David Raab, who back in the day had coined the label CDP (Customer Data Platform), published a provocative piece titled “The Composable CDP is Dead”. In summary the author argues that all CDPs have already caught up with the modularization that came from sitting on top of more flexible data warehouses, so every single CDP has either become a niche modular component or an all-encompassing, highly-modularized software suite. In sum, the term will not help a Hightouch differentiate itself uniquely any longer. We suggest that you listen to our interviews with Tejas Manohar and Jonathan Mendez, CEOs of Hightouch and Neuralift AI respectively, for further context.  AI, Competition and Digital Markets The community is still recovering from Hamburg's DPA's opinion (adopted on July 15th) stating that LLMs do not contain personal data. The supervisory authority made three key points that we will be covering with some future guests: a) No personal data is stored in LLMs; b) Data subject rights as defined in the GDPR cannot relate to the model itself, but they can be exercised against the provider or deployer of a system built on top of such models, with regards to the input or output of such system; c) The training of LLMs using personal data must comply with data protection regulations.  The Irish DPC announced an investigation into Google's foundational AI model (PaLM 2) on September 12th, with a focus on the DPIA that Google is expected to have undertaken.  An ICO report released on November 8th found that AI recruitment technologies can filter candidates according to protected characteristics including race, gender, and sexual orientation. On November 13th, Meta received an 800,000 EUR fine for anti-competitive practices in the bundling of its Marketplace feature with the primary Facebook application. So, they have leveraged their control over one market to take control of another, adjacent market, in this case threatening pretty large companies in the classified ads space. That's it for today! Thanks again for listening.  

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)

What's up folks, we've got another roundup episode today and we're talking AI. Before you dismiss this and skip ahead, here's a quick summary of why the excitement around generative AI isn't just hype—it's a sustainable shift.While some may perceive AI to be losing steam, largely due to a surge of grifters in the field, this is not your average trend. In Episode 78, we spoke with Juan Mendoza, CEO of TMW, about why generative AI is distinct. It's not mere hype or a future possibility; generative AI delivers practical value today.Examining Google Trends data for the search term "AI + marketing," we notice a significant surge starting in November 2022, coinciding with the release of ChatGPT. This surge peaked in May 2023 when GPT-4 became mainstream. Normally, you'd expect interest to wane after such a peak, but it has barely dipped. We're currently sitting at a 94/100 search interest, compared to this summer's peak. This suggests a sustained, rather than fleeting, interest in the technology.While nobody has a crystal ball, there's broad agreement that AI is far from making marketing roles obsolete. Instead, it's augmenting the work we do, not replacing it.In an effort to explore further how we can better future proof ourselves, I've asked guests what specific aspects of marketing make it resistant to AI. The insights from these discussions have been fascinating, underscoring the unique value and human touch that marketers bring to the table.Here's today's main takeaway: Your real edge in marketing fuses a nuanced understanding of business context, ethics, and human emotion with capabilities like intuition, brand voice and adaptability—areas where AI can sort data but can't match ability to craft compelling stories. AI isn't pushing you aside; it's elevating you to a strategic role—given you focus on AI literacy and maintain human oversight. This isn't a story of human vs. machine; it's about how both can collaborate to tackle complexities too challenging for either to navigate alone.AI is less a replacement and more of a reckoning. It's not coming for us; it's coming for our inefficiencies, our lack of adaptability, and our refusal to evolve. AI is holding up a mirror to the marketing industry, asking us not if we can be replaced, but rather, why we haven't stepped up our game yet. Buckle up; this roundup of experts doesn't just debate the future—it challenges our very role in it.Why AI Can't Fully Replace Human Nuance in Marketing OperationsLet's start off in Marketing Operations with Mike Rizzo, the founder of MarketingOps.com. We asked him to dive into his view that AI won't be replacing marketing jobs "anytime soon," a point that has some level of ambiguity. The question aimed to uncover what Mike specifically means by "anytime soon" and why he believes that AI won't fully automate the marketing Operations sector in the near future.Mike highlighted the intricacy of marketing operations that he believes will be resistant to full automation. Specifically, he mentioned that marketing across SMBs and enterprises involves nuanced processes. The differentiation between types of leads—MQL, SQL, PQL, and so on—each has its own distinct workflow and architecture. This makes it a highly tailored field, more a craft than a science, and challenging to automate.Mike pointed out that the entire operational architecture, from data movement to notification protocols, is unique to each organization. It's precisely this framework that makes it hard to replicate with AI, regardless of its computational abilities. While he admitted that AI could offer suggestions in optimizing specific metrics or elements, such as lead scoring, Mike emphasized that these technologies serve better as consultants rather than decision-makers.The implementation of martech stacks, according to Mike, is akin to running a product. From understanding the product roadmap to enabling team members, AI can at best serve as a consultation service, streamlining processes but never fully taking over. Each tech stack is tailored to an organization's needs, something that AI, for all its merits, struggles to capture in its full complexity.Mike also confessed to leveraging AI for particular tasks but remains skeptical about its ability to handle the fine-tuning required in the marketing ops and RevOps space. He argued that while AI can assist, it can't replace the distinct, specialized requirements that each marketing operation demands.Key Takeaway: Mike suggests that AI has its uses, but the nuanced, unique nature of marketing operations makes it a field that's resistant to full automation. There's value in human oversight that not even the most advanced AI can replicate.Trust in Data and the Ability to Constrain AI ResponsesWhile AI might have some challenges with the nuances of marketing Ops, AI does have a foothold in some marketing sectors. Boris Jabes, the co-founder and CEO at Census, acknowledged AI's ability to drive efficiency, especially in advertising. In spaces where "fuzziness" is acceptable, such as Ad Tech, AI already performs exceptionally well. Marketers utilize advanced algorithms in platforms like Google and Facebook to better place their ads, and these platforms are continuously fueled by world-class AI. In these instances, AI isn't just convenient; it's almost imperative for maintaining competitive performance.However, Boris warns that there are areas where AI falls short, specifically in customer interactions that require nuanced understanding and empathy. For example, using AI to answer questions about ADA compliance or other sensitive matters can result in "hallucinations," or incorrect and inappropriate responses. Herein lies a crucial challenge: How do you constrain AI to deliver only appropriate, correct information?Additionally, Boris identifies data trustworthiness as a significant hurdle. AI's performance depends on the quality of data it's trained on. Large enterprises are often hesitant to adopt AI without reliable data, and thus, miss out on its advantages. Conversely, smaller companies are more willing to experiment, but their scale is insufficient to make industry-wide impacts.Despite the challenges, Boris argues that staying away from AI is not an option for today's marketers. Whether you are aiding the machine with quality data or deciphering how AI can be employed responsibly, there's room for human marketers to provide valuable input and oversight.Key Takeaway: AI has carved out a substantial role in specific sectors of marketing like Ad Tech, but it still has limitations that require human oversight. Trust in data and the ability to constrain AI responses are areas where marketers can add significant value.Marketers Are Future Prompt Thinkers and AI RegulatorsOver the next few years, marketers will be invaluable when it comes to ensuring data integrity and guiding AI's influence. Let's explore how marketing roles might evolve across different verticals. Pratik Desai has some fascinating predictions about the role of marketers. He's the founder and Chief Architect at 1to1, an agency focused on personalization strategy and implementation.When asked about the limitations preventing AI from taking over the marketing landscape, Pratik dives into the intricacies of how AI operates in different sectors. According to him, AI in marketing can be bifurcated into "Curation AI" and "Generation AI." Curation AI, as the name suggests, curates content and recommendations. Generation AI, a more recent evolution, generates content from scratch.Curation AI has shown promise, especially in less regulated industries like e-commerce. Here, even if AI gets it wrong 15% of the time, the increase in efficiency and accuracy for the remaining 85% is often considered a win. But switch the lens to highly regulated sectors like financial services or healthcare, and the stakes skyrocket. Here, even a 1% mistake rate could translate into severe regulatory or even life-impacting issues. This inherent limitation necessitates a "marketer control" layer to ensure compliance and accuracy.In comes Generation AI, aimed at resolving some of these content-based challenges. With its ability to generate images and copy at scale, Pratik posits that it could revolutionize how marketing programs are run. This technology can create content in seconds, which would otherwise take a design team weeks to produce. But again, the human element isn't completely removable. Marketers will still need to oversee these automated processes, especially in regulated sectors where the margin for error is minuscule.Key takeaway: The role of the marketer is changing but not disappearing. In industries with low regulation, marketers transition to becoming "critical prompt thinkers," while in more regulated sectors, they wear the additional hat of "AI regulators." This reveals the dual nature of AI: a tool that can enhance efficiency yet requires human oversight for nuance and regulatory compliance.The Need for Ongoing Dialogue Between AI and the MarketerThis inherent necessity of a "marketer control" layer to ensure compliance and accuracy is a shared thread. When asked about the potential of AI to take over the marketing realm, Tamara Gruzbarg—VP Customer Strategy at ActionIQ—offered a seasoned perspective, advocating for a more nuanced view. She was explicit that AI can certainly handle the grunt work—automating repetitive tasks and even aiding in content generation. However, Tamara highlighted the irreplaceable role of human marketers when it comes to understanding brand voice, tone, and style.Tamara also cautioned against overlooking the human element in data analytics and predictive modeling. She argued that constructing models for critical business metrics like conversion rates and lifetime value demands a deep understanding of business context. AI tools may be adept at crunching numbers, but they fall short in interpreting the underlying structure and implications of the data.Tamara introduced the "human-in-the-loop" philosophy that they follow at ActionIQ, emphasizing the need for ongoing dialogue between the AI and the marketer. This interaction ensures that AI-generated content aligns with the brand's unique voice and message, preventing a homogenized marketplace where every brand sounds the same.The discussion confirmed the ongoing need for marketers to "cut through the noise." Tamara argued that a human touch is essential for achieving this, particularly in an era where AI can churn out volumes of generic content. She pointed out that while AI could be a valuable partner in initial drafts and multiple versions of content, the final say should always be human.Key Takeaway: Tamara stressed the importance of human expertise in data analytics and predictive modeling. While AI can handle data computation, it lacks the ability to understand business context and the nuances of data. She advocates for a "human-in-the-loop" approach at ActionIQ, which keeps marketers engaged with AI tools. This collaboration ensures brand-specific messaging and avoids market homogenization.AI's Creative Strengths and Brand Style Guide LimitationsThis idea of brand specific messaging also extends to visual brand marketing and AI's lack of ability to follow a brand guideline… at least for now. Pini Yakuel is the CEO of Optimove, a platform that's operating light years ahead of most martech when it comes to AI features. When asked about the roadblocks stopping AI from completely replacing human marketers, Pini focused on the intricacies of creative studio work. He points out that while AI can perform well in tasks such as comic book illustrations, it still falls short when you factor in the human elements—like nuance, emotion, and unique design language—that often define a brand.Pini recounts a conversation he had with one of his designers about this very issue. The designer expressed that AI could create fantastical images—like a unicorn riding a motorcycle on Mars—but couldn't quite replicate the specific design language integral to their brand, Optimove. Despite AI's capabilities in artistry and replication, it lacks the human touch needed to navigate the complex and nuanced design landscape that brands often require.He emphasized that while AI can go wild with creative elements, it's not yet proficient at maintaining the unique "look and feel" that a brand's specific style guide may dictate. For instance, integrating various elements into a cohesive design that represents a brand authentically is something AI still struggles with. According to his designer, the technology simply isn't there yet, at least not to a level that can replicate the careful and intentional choices a human designer would make.This limitation isn't just about not having enough processing power or data; it's about an inherent lack of understanding of human emotion, culture, and nuanced communication. These elements often serve as the underpinning for any successful marketing campaign, aspects that AI can't yet replicate.Key Takeaway: Pini argues that the barrier to AI fully replacing human marketers lies in the inability to understand and replicate the nuanced, human elements that make up a brand's unique design language. Until AI can integrate this "human touch," it will remain a tool rather than a replacement.The Trust Barrier in AI's Quest to Replace MarketersIf you asked a marketer in the mid 80s if the Internet would replace everything a marketer did back then, they probably would've been skeptical. To be fair it didn't replace everything but marketing looked dramatically different 10-15 years after that. At the heart of roles shifting and a marketer control layer is this idea of adapting. Deanna Ballew is Senior Vice President of DXP Products at Acquia where her team is focused on innovating with AI for marketers. When asked about the likelihood of AI replacing marketers, Deanna emphasized that it's not a matter of "if," but "how" we adapt to this looming shift. In line with comments from Boris, she added that the obstacle isn't the capability of the AI but the trust—or lack thereof—in the data it uses. Deanna points out that tools like ChatGPT aren't yet trusted because they rely on an immense pool of uncurated data. To trust an AI with marketing tasks, there's a need for curated, proprietary models.Deanna brings the focus back to a crucial but often overlooked factor: AI literacy among marketers. As AI technology advances, so must the understanding marketers have about the underlying models. The future isn't just about AI doing the work but about marketers asking the right questions. Chat UX interfaces could enable marketers to query data effectively, bypassing the need for a business intelligence analyst. However, this streamlined process depends on the trustworthiness of the data.Here's the flip side: As marketers become more literate in AI, their roles will shift from manual tasks to higher-value activities. Think about posing complex questions to AI-driven systems, which could then provide strategic insights that marketers can translate into actionable campaigns. Marketers could use these interfaces to directly ask, "What's the next best customer segment to go after?"—with the system offering insights based on trusted data.The advancement of AI is like a double-edged sword. On one side, it promises to relieve marketers of mundane tasks; on the other, it demands a new set of skills and a higher level of trust in the data. Deanna stresses that the transformation is inevitable, but the timeline is undetermined, hinging on how quickly trust can be established in AI-generated data and models.Key Takeaway: Deanna underscores the role of "trust" as the linchpin for AI adoption in marketing. Marketers should focus on increasing their AI literacy and understanding of underlying models to prepare for this seismic shift. Without trusted data and models, even the most advanced AI can't eliminate the human checkpoint in marketing decisions.The Organic Evolution of AI in MarketingThere's a clear trend so far, that the human checkpoint in AI is going away anytime soon. That means there's a clear signal for marketers to follow Deanna's advice and double down on AI literacy. The next question is really about how fast you should consider doing this. How fast will we need to adapt?Aliaksandra Lamachenka, a Marketing Technology Consultant, had a surprising and insightful answer. She drew an analogy with post-war Japanese architecture, specifically a concept known as "Japanese Metabolism." This architectural philosophy thought of buildings as living organisms with a spine to which modular capsules could be attached or detached. Despite its early promise in the '50s and '70s, this concept now largely exists as an idea, with few practical implementations. The buildings initially envisioned as the future of living are now mostly used for storage.What does this have to do with AI replacing marketers? Aliaksandra contends that society needs time to adapt and accept new concepts, just as with Japanese Metabolism. The notion of AI taking over marketing roles is a similarly radical shift that society isn't ready to fully embrace. Moreover, she believes that the evolution of AI will be more organic than revolutionary, a natural progression shaped by cultural and societal shifts.Aliaksandra underscores that although AI has vast potential, the speed at which humans can adapt and accept these changes is the bottleneck. She compares AI's future impact to the way modular buildings and integrated landscape houses have slowly, but organically, become part of architectural reality. Aliaksandra asserts that AI's growth will similarly happen organically over decades, not through immediate disruption but by evolving naturally into our processes and systems.She concludes by pointing out that the ideas of the past often serve as the blueprints for future innovation. Whether it's post-war Japanese architects or today's AI developers, the radical concepts and technologies introduced will take time to become an integral part of society. Like the modular houses of today that owe their conceptual roots to Japanese Metabolism, future AI capabilities will likely be adaptations of current bold ideas.Key Takeaway: Aliaksandra suggests that the pace at which humans can adapt to new ideas is the limiting factor in AI's ability to replace marketers. She predicts a gradual, organic evolution of AI in marketing, driven more by human adaptation than by technological capabilities.AI's Shortfall in Grasping Marketing's Emotional and Intuitive SideWhile the advance of AI in the marketing sphere could be more of a steady march than an overnight revolution, there's a threshold it hasn't crossed: the realm of human intuition and gut decision-making. Tejas Manohar, Co-founder and Co-CEO at Hightouch, offered a nuanced take, emphasizing both the promises and limitations of AI. Tejas mentioned that AI technologies, like generative AI and reinforcement learning, have already begun revolutionizing how marketing campaigns and experiments are run. They offer incredible potential for automating tasks such as data experimentation, audience segmentation, and personalization.However, Tejas made it clear that AI is not ready to replace human marketers entirely. The core of his argument lies in the duality of the marketing role, which requires both quantitative and qualitative skills. While AI can crunch numbers, run experiments, and even generate content, it falls short when the job requires a deeper understanding of human emotions or intuition-based decision-making. Tejas points out that marketers often rely on a mix of data and gut feeling, using insights to make substantial strategic changes. Current AI technologies are just not equipped to understand or implement these nuanced elements.He also discussed the notion of AI as a complementary tool rather than a replacement. Tejas is bullish on the idea that AI will augment marketers, particularly by providing them with easier access to critical business data. He envisions a future where marketers won't have to request specific scripts or datasets but can work independently to glean insights, thanks to advancements in AI technologies.The issue of AI completely taking over marketing, Tejas concluded, is also tied to broader ethical and societal questions. If AI gets to a point where it can wholly replace human skills and intuition, society will face "singularity type problems" affecting not just marketing but every job role.Key Takeaway: According to Tejas, AI's current role in marketing is as an augmenter, not a replacer. While it excels at quantitative tasks, it lacks the nuanced understanding of human emotion and intuition that is critical for effective marketing. Its potential lies in the empowerment it can offer marketers through data access and automation.The Thrill of Using Generative AI in Your Martech StackMany of the marketers I chatted with echoed Tejas, that AI may be able to process data and spit out automated directives, but it can't yet replicate the unpredictable, qualitative essence of what makes marketing tick. One particular guest flipped the script on me and argued that the exciting debate is how AI will augment, not replace, the roles of marketers.The Martech Landscape creator, the Author of Hacking Marketing, The Godfather of Martech himself, mister Scott Brinker had a clear perspective: we're not there yet. For Scott, "good marketing" remains a domain where human intuition and creativity hold court. AI may be able to process data and spit out automated directives, but it can't yet replicate the unpredictable, qualitative essence of what makes marketing tick. The buzzphrase "Your job won't be replaced by AI; it will be replaced by another marketer who's good at using AI" captures the current sentiment aptly. Cheesy as it may sound, Scott sees a grain of truth here. Far from envisioning a future where AI eliminates human roles, he expects technology to bolster the capabilities of marketing professionals. It's about learning how to weave AI into current practices to improve efficiency and expand possibilities.But where Scott finds the most promise is in the evolving role of marketing ops leaders and martech professionals. The real thrill comes from the ability to leverage generative AI to optimize what a marketing stack can do. Essentially, AI becomes a potent tool in the toolbox of the modern marketer, especially in operations. The tech is less about replacing humans and more about magnifying their abilities.However, Scott's perspective doesn't herald the end of human involvement; it simply reframes it. AI becomes a part of the job, a powerful component in the array of strategies and tactics that marketers employ. For him, it's about balance, not replacement. AI might be good, even exceptional, at crunching numbers and predicting outcomes based on existing data. But it can't yet think creatively or strategically in the way humans can, which is where the core of "good marketing" lies.Key Takeaway: The future of marketing isn't a binary choice between human intuition and machine capabilities. Rather, it's a synergistic relationship where each amplifies the other. For Scott, the real excitement lies in how AI will augment, not replace, the roles of marketers.AI's Storytelling Shortfall in Marketing's Emotional LandscapeWhile AI will continue to amplify the reach and efficiency of marketing efforts, experts agree, their role remains largely complementary to human skill sets. Despite its analytical prowess and automation capabilities, AI hasn't cracked the code on intuition and following brand guidelines but what about emotional intelligence or compelling storytelling—elements that are often considered the heart and soul of effective marketing. Lucie De Antoni, Head of Marketing at Garantme, brought forth some astute observations. Sure, AI is making strides in many industries, marketing included. It can automate and even enhance several elements of the marketing process. But what AI notably lacks, according to Lucie, is the ability to replicate human creativity and emotional intelligence.Marketing isn't just a numbers game. It's about storytelling, tapping into human emotions, and crafting narratives that resonate with people. Lucie argues that these are areas where AI falls short. While machine learning can analyze trends and predict consumer behavior to a certain extent, it's not equipped to fully understand the nuances of human sentiment or create emotionally resonant campaigns. This shortcoming isn't necessarily a drawback; Lucie sees it as a positive aspect. If AI were capable of such emotional intelligence and creativity, it would put marketers in a tricky situation. The very things that make marketers invaluable—understanding human behavior, crafting compelling stories, evoking emotion—are elements that AI can't yet emulate.So, the reality isn't that AI is primed to push marketers out of their jobs, but rather that it can become a tool that complements human skills. Lucie suggests that this "limitation" of AI serves as a safeguard for the unique value that human marketers bring to the table. The tech may evolve, but it's unlikely to eclipse the human ability to connect on an emotional level anytime soon.Key Takeaway: Lucie emphasizes that the strength of human marketers lies in their ability to understand and evoke human emotions—a skill set that AI, despite its advancements, cannot yet replicate. Therefore, while AI can be a powerful tool, the human element in marketing remains irreplaceable.Episode RecapAI is already rampant in marketing, particularly in fields like Ad Tech. However, generative AI is not a magic bullet; human expertise is essential for interpreting data and grasping brand nuances. A "human-in-the-loop" approach creates a checks-and-balances system, fostering trust in the data generated by AI and offering the emotional intelligence that machines lack.Marketing roles are evolving but definitely not vanishing. In sectors with fewer regulations, marketers could morph into strategic thinkers, whereas in tightly controlled industries, they're becoming essential AI regulators. To effectively ride this wave, increasing AI literacy among marketers is non-negotiable.The speed at which AI becomes a staple in martech is not solely a question of technological prowess. It's about how quickly humans can adapt and find ways to integrate AI into existing frameworks. The most viable future is not a zero-sum game between human and machine; it's a collaborative one, where each enhances the other's strengths.You heard it here first folks: Your real edge in marketing fuses a nuanced understanding of business context, ethics, and human emotion with capabilities like intuition, brand voice and adaptability—areas where AI can sort data but can't match ability to craft compelling stories. AI isn't pushing you aside; it's elevating you to a strategic role—given you focus on AI literacy and maintain human oversight. This isn't a story of human vs. machine; it's about how both can collaborate to tackle complexities too challenging for either to navigate alone.✌️--Intro music by Wowa via UnminusCover art created with Midjourney

Many large organisations have the data to pull off sophisticated marketing strategies, but only if they avoid the common pitfalls that limit the potential. In this episode I interview Tejas Manohar on the huge – and typically unexploited – potential for data-driven marketing and personalisation. Tejas is co-founder and co-CEO of Hightouch. Hightouch is a reverse ETL platform that helps organisations synch their data warehouses with business facing tools and technology. Their products are used by big name corporations like Warner Music, Chime, Spotify, NBA, and PetSmart. In this wide-ranging conversation Tejas and I discuss: What a reverse ETL platform is and why we need it Why Tejas is bullish on turning data warehouses into marketing engines The key steps marketers should take to implement personalization effectively using existing company data and platforms The pitfalls and common mistakes businesses make in data-driven personalisation and how to avoid these, and much more. Tejas on LinkedIn: https://www.linkedin.com/in/tejasmanohar/ Tejas on Twitter (or is it X?): https://twitter.com/tejasmanohar

This week, Jim talks with Tejas Manohar, about he and his co-founders grew his data startup High Touch through leveraging communities and marketing smart cuts, and how data activation can become a super power for businesses. TOPICS DISCUSSED IN TODAY'S EPISODE Selecting the right idea Selecting the right Co-founders How to be scrappy Smart cutting Leveraging communities What is data activation  Resources Jim Huffman website Jim's Twitter GrowthHit The Growth Marketer's Playbook   Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Summary: The future of CDPs, as envisioned by Tejas, is a more flexible, adaptable data architecture that Hightouch is actively shaping. Hightouch, even without the data collection component, is recognized by some of the largest companies in the world as their go-to CDP. Tejas stresses that the reconciliation of 'truth' in data between marketing and data teams isn't solely a tech or architecture problem; it requires an operational shift and closer collaboration between teams. The conversation serves as an essential guide for businesses seeking to optimize their data use and enhance customer experiences.The Software solutions like Hightouch provide a solid framework to tackle this, but the human element—teamwork, alignment, and communication—remains a key determinant in solving these challenges.From Corporate Travel to Reverse ETL: Teja's Journey Back to DataWhen asked about the journey of reverse ETL's inception at Hightouch, Teja revealed the interesting twists and turns of his entrepreneurial path. His initial venture after leaving Segment wasn't directly into the data sphere. He founded a startup, Carry, in the corporate travel space.However, Teja's departure from Segment wasn't just fueled by an entrepreneurial itch. He had reservations about the future trajectory of Customer Data Platforms (CDP). He didn't fully believe CDPs were set to become the standard for managing customer data across industries. With inklings of impending acquisitions and significant changes in the data industry, he left Segment.Teja then spent about eight to nine months with Carry until the onset of COVID-19. Despite the inherent challenges of the travel industry—low margins, high human operation requirements, price-sensitive customers—Carry was growing. Yet, COVID-19 brought it to a grinding halt.With business metrics falling to zero almost overnight, Teja and his co-founders, Auren and Josh, found an unexpected opportunity. They decided to pivot back to their roots in the data industry, tapping into their old ideas and experience from their Segment days. The pandemic, in all its harshness, became a catalyst for their return to the customer data space.Teja's story is far from a linear narrative. The travel venture, the COVID-19 pivot, and the return to the data industry all added unique layers to his entrepreneurial journey. Looking back, Teja feels gratitude for these unexpected turns of events, which led him back to the dynamic world of data and customer platforms.Takeaway: An entrepreneur's journey isn't always a straight path. Teja's experiences, from his departure from Segment to his foray into the travel industry and eventual return to data, highlight the unforeseen opportunities that can surface in the face of challenging times. His story underscores the importance of adaptability and leveraging past experiences to seize new opportunities in the ever-changing business landscape.Composable CDP - The Birth and Journey of a New ParadigmWhen asked about the emergence of the term "composable CDP" and the role Hightouch played in its inception, Tejas reminisced about the early days of this concept's birth. Tejas recalled collaborating with one of their esteemed partners to develop a novel way of approaching Customer Data Platforms (CDPs), distinct from the traditional models. Their goal was to define an architectural blueprint that would resonate with a marketing audience while providing a fresh solution to existing CDP challenges. The result was the "composable CDP."Despite its somewhat confusing nature, this term became a touchstone for their market positioning. But Tejas admitted, many terms in the martech world like "marketing cloud" or "engagement hub" often induce more head scratching than clarity. Their aim, however, was not merely to coin a catchy phrase but to address a pervasive dissatisfaction within the industry. At the time, many large enterprises and mid-market companies were investing heavily in CDPs, hoping to enable marketers to freely explore customer data, create audiences, and tailor customer journeys across all channels. Yet, despite the widespread adoption, most were finding little value in these investments. This stark discrepancy between aspiration and reality was the driving force behind Hightouch. The aim was not just to sell another CDP, but to propose an innovative approach that would enable marketers to leverage data more effectively across the organization. This approach advocated the utilization of the rich data sources already present in company warehouses, and activating it across various customer journey touchpoints. Tejas highlighted that the core value of a solution should not be whether it's bundled or unbundled, but rather, the tangible business outcomes it can drive. As companies invest in housing their data using various BI tools, from Microsoft Power BI to newer players like Looker, the potential to empower marketing teams with this wealth of data is tremendous. Solutions like Hightouch or a robust CDP should offer infinite flexibility, not limiting themselves to specific data collected for a CDP.The term "composable" was chosen to reflect this mindset - working with existing resources, scaling with future technologies, and avoiding the rigid, off-the-shelf solutions. While the term may elicit confusion, the purpose behind it - empowering businesses to effectively use their data - remains clear.Key Takeaway: The term "composable CDP" emerged from the need for a novel approach to CDPs that focused on empowering marketers to use data more effectively. It's about leveraging existing data, offering infinite flexibility, and scaling with future technologies, rather than sticking to rigid, traditional solutions.Breaking Down the Power of Composable CDP vs Packaged SolutionsProbing deeper into the potential of Composable CDP, Tejas was asked to illuminate the benefits of adopting such an approach over a monolithic all-in-one package solution. Tejas, ever insightful, took this as an opportunity to share the unique strength of a composable strategy.He started by emphasizing the fundamental flaw in traditional customer data platforms (CDPs) - their reliance on a pre-defined data architecture. Brands using conventional CDPs like Segment, Oracle, or Salesforce CDP are forced to adapt their data into a format acceptable to the platform, and this restriction severely limits the platform's capability. In Tejas' words, "they can only operate on data that they understand and that was built for them." This myopic vision becomes problematic in the complex, diverse landscape of large enterprises where every business is unique and possesses an array of distinct data. Tejas vividly illustrated this point by citing the case of a Fortune 500 company that wanted to leverage its pet loyalty program data - a dataset unique to their business - to drive personalization and engagement. Traditional CDPs failed to handle this unique set of data due to their rigid architecture, but Hightouch's flexible and inclusive approach brought the data to life.The ability of Hightouch to tap into an organization's existing data, whether it's stored in Snowflake, Databricks, or any other system, and utilize it to deliver highly personalized experiences is at the heart of its value proposition. By contrast, the challenges of molding data to fit into a traditional CDP's format have led to a high failure rate, Tejas pointed out, making the novel architecture of Hightouch all the more appealing.Takeaway: The real power of a composable approach like Hightouch's lies in its flexibility and inclusivity. It's not restricted to pre-defined data architectures and can handle unique, diverse data sets that are crucial to large enterprises, unleashing new potentials for customer experiences.Unpacking the Legacy CDP: Teja's Insights and Vision for the Future of MartechThe question of the legacy or packaged Customer Data Platform (CDP) was put forth. Looking for a deep dive into the anatomy of a traditional CDP, Teja's insights were sought. Referencing Arpit Choudhry's informative blog post that delineates eight vital components of a legacy CDP, he was asked to reflect on these elements and offer his unique perspective.With his extensive experience at Segment, a leading CDP, Teja was expected to provide valuable insights. Choudhry's components ranged from the basic SDK (the infrastructure to collect first-party data) to the advanced reverse ETL (Extract, Transform, Load) process used for extracting customer data from a warehouse to other business tools. His list also included ID resolution, data quality, accuracy, consistency, and governance aspects. In response, Teja noted the emphasis on comprehensiveness and referred to Hightouch's resources for a broader view of the differences between composable and packaged CDPs. However, he highlighted the importance of considering the why over the what — understanding why companies opt for CDPs in the first place. According to Teja, companies pursue CDPs to leverage their customer data to personalize the customer experience and drive business outcomes. The ultimate goal isn't necessarily about the different components or features, but about leveraging data to the fullest. Teja emphasized that in his view, the crux of any CDP lies in three core things: a way to collect data, a way to transform it, and a way to activate the data.Highlighting the advantages of a composable CDP approach, Teja mentioned that instead of using an off-the-shelf CDP platform as the data warehouse, companies could leverage their own data warehouse. This allows businesses to access and activate data that wasn't originally built for the CDP's end purpose. From the start, Hightouch, focused on large enterprises, emphasizing compliance, privacy, and governance. Their experience with companies such as NBA, Warner Music, PetSmart, and GameStop necessitates a high degree of data accuracy and consistency.In conclusion, Teja underscored the importance of use case-driven selection in martech. Instead of comparing solutions on a feature basis, marketing technologists should identify the activations they need for their business and then look for the features that enable those activations.Key Takeaway: Teja's approachable but profound insights bring clarity to the complexity of the CDP landscape. He encourages a shift in perspective — from a feature-based approach to a use case-driven strategy in martech decision-making. In doing so, he positions the future of martech not as a quest for the most comprehensive solution, but as a tailored journey to activate and leverage data for personalized customer experiences.Hightouch's Evolution and Embracing the Composable CDP ApproachWhen asked about Hightouch's position in the martech ecosystem, Tejas articulated the company's journey and its dynamic development in relation to the composable CDP (Customer Data Platforms) paradigm. Initially, Hightouch revolved around the concept of reverse ETL (Extract, Transform, Load), a solution born from the recognition that warehouses brimmed with data requiring accessibility across various business tools. There was debate within the team about adopting the term "reverse ETL", but the gamble paid off, allowing them to catalyze a burgeoning space. Audience segmentation was part of Hightouch's vision from the beginning, and despite its late incorporation, it has become a valuable asset. Tejas painted a picture of Hightouch's trajectory over the years, highlighting its broadened capabilities. With the exception of ETL and data collection - the pathways for getting data into the warehouse - Hightouch has extended its functionality to encompass virtually all aspects of a CDP. While ETL isn't currently on Hightouch's menu, Tejas hinted that it may not be off the table for future consideration. Given the rising commoditization and numerous ways for companies to collect events into their data warehouses, Hightouch has not prioritized this feature. However, it's worth noting that they are not philosophically against its inclusion. Despite Hightouch's reluctance to enter the data collection arena, the company excels in delivering a wide range of services under the composable CDP approach. It shines in its commitment to offer more than just reverse ETL, providing marketers with an extensive product to facilitate audience segmentation and identity resolution. Tejas confirmed Hightouch's commitment to this path by teasing an imminent announcement concerning their advancements in identity resolution. Takeaway: Hightouch has emerged as a leader in the composable CDP space, excelling beyond reverse ETL. By circumventing the need for extensive engineering effort and tapping into existing data sources across businesses, Hightouch enables marketers to build audiences efficiently and effectively. This approach, anchored on data warehouses, allows businesses to preserve their unique data structure and offers them the flexibility to personalize based on their distinct attributes and customers' needs.Can Hightouch Truly Replace Legacy Customer Data Platforms?When Tejas was asked about the perceived controversy around composable tools and their approach to marketing solutions, the conversation moved towards the role of Hightouch as a competitor or substitute for traditional Customer Data Platforms (CDPs). Critics argue that the popular sentiment surrounding reverse ETL tools only adds to the confusion, veiling the true utility and function of such tools.Tejas, having authored several thought-provoking blog posts last year on the subject, including "CDPs are Dead" and "Friends Don't Let Friends Buy a CDP", was asked whether Hightouch can indeed replace a legacy CDP today. In the marketing world, Hightouch is often touted as a 'fast, flexible, affordable CDP alternative'. Yet, Tejas pointed out, Hightouch doesn't incorporate components like tracking and ETL, crucial elements of a typical CDP.Tejas responded to this query with an insightful admission - describing Hightouch as a CDP alternative when used in combination with other solutions like Snowplow and FiveTran would be misleading. However, he did emphasize that less than 20% of Hightouch's enterprise clients leverage such complementary solutions, defying the notion that Hightouch is solely for data teams at technology-forward companies.Moreover, Tejas provided examples of Hightouch's significant enterprise customers, such as Blizzard Activision and Warner, who have transitioned from other CDPs and now consider Hightouch their CDP of choice. The primary difference between Hightouch and a conventional CDP lies in the data collection component, which Hightouch addresses through partnerships with companies like Snowplow.Hightouch aims to facilitate data activation success for marketing teams and personalization efforts. The future of CDP, according to Tejas, is an architecture where companies possess their own data and can activate it across different channels, allowing for flexibility and adaptability - a future that Hightouch is ambitiously pioneering.Takeaway: Hightouch, even without the data collection component, is recognized by some of the largest companies in the world as their go-to CDP. The future of CDPs, as envisioned by Tejas, is a more flexible, adaptable data architecture that Hightouch is actively shaping. With its data activation capabilities, Hightouch is carving a new path for the evolution of CDPs.Breaking Down the Data Truth: Martech vs Data TeamsWhen Tejas was questioned about the conflict within organizations, arising due to the diverging understanding of 'truth' between the Customer Data Platforms (CDPs) used by marketers and the data warehouses employed by analysts, his response presented a balanced blend of practical insights and empathetic understanding. The issue he addressed relates to the potential disparities in data interpretation between different teams, leading to complications in metric reconciliation and incomplete data in CDPs.Tejas readily acknowledged the problem's complexity, stressing the need for a single source of truth across the board. In Hightouch's vision, this unified truth would arise from a data set that's not only flexible but also entirely owned by the organization, fostering more control for marketers.However, Tejas expressed a level of realism regarding the technology's role in resolving these disputes. The idea that software vendors can eradicate any collaboration issues between teams within a company is simply overpromising, according to him. While Hightouch provides an enabling framework that facilitates businesses in successfully resolving these conflicts, he was clear that there's an inherent human element necessary for this to work. For any company to succeed in their data initiatives, a robust alignment and collaboration between the marketing team and the data team is crucial. Irrespective of the platform or the nature of the CDP—be it composable or otherwise—those teams must work together in harmony. The role of Hightouch, as Tejas pointed out, is to offer a conducive framework where teams can work on the same source of truth, using the same data set, and capitalizing on the data stored in their warehouses.Tejas' doubled down on the need for fostering mutual understanding between marketing and data professionals. In his view, communities like Arpit's Data Beats serve an essential role, bridging the gap between these two distinctive professional arenas. By elucidating data concepts to marketers and marketing concepts to data analysts, they contribute significantly towards promoting interdisciplinary knowledge.However, as Tejas noted, expecting marketers to understand SQL and data engineering or data analysts to build marketing campaigns is not a realistic expectation. The solution to this problem doesn't lie in enforcing these roles to cross over fully, but in designing and utilizing software that's user-friendly for marketing teams, while still leveraging the data and technical infrastructure provided by the data teams.Takeaway: The reconciliation of 'truth' in data between marketing and data teams isn't solely a tech or architecture problem; it requires an operational shift and closer collaboration between teams. Software solutions like Hightouch provide a solid framework to tackle this, but the human element—teamwork, alignment, and communication—remains a key determinant in solving these challenges.A Closer Look at The Rise of Warehouse-Native Approach Tejas was prompted with a significant question concerning the progression of martech and the potential role of the warehouse-native approach. The heart of this discussion revolves around the effectiveness of martech tools that hinge solely on the data warehouse, extracting real-time insights without creating superfluous data replicas.Tejas posited that incumbent martech providers are not far behind Hightouch in making a paradigm shift towards the data warehouse as the central data point. This move is not only predictable but also a reaffirmation of the intertwined nature of martech and data. As internal data pots like Snowflake and Databricks gain ground, the idea of siloed marketing data is rapidly becoming outdated.Despite this, Tejas expressed reservations about a complete sweep of the martech landscape by warehouse-native marketing tools. He brought up the diversity of marketing channels, encompassing advertising, app personalization, and more. This variety makes it impractical to expect a full-scale migration onto the data warehouse. Marketers have intricate needs; while data access is paramount, they also have to deal with aspects such as IP warming, which may not be catered to by warehouse-native tools.This isn't to say that Tejas doubts the potential of emerging platforms like Castled.io, Vero, and MessageGears to carve a niche in the martech landscape. His perspective isn't grounded in their inability to create robust businesses, but the improbability of these tools triggering a platform shift of a magnitude capable of upending the reign of heavyweights such as Salesforce and Adobe. Despite the undeniable advantages to marketing, data, and IT teams, the chances of a mass SaaS apps swap out do not appear immediate, given the historic hurdles startups face while trying to capture a substantial market share in this space.Tejas highlighted the ethos of Hightouch, stating that their approach is to tackle the present problem rather than develop new platforms. Their primary goal is to create a bridge between the data and marketing facets of businesses, without the hassles of managing new templates or handling novel platforms for email analytics. Takeaway: Tejas's insights indicate that while the martech industry will continue to embrace a warehouse-native approach, it does not spell a complete overhaul of the martech landscape. The future likely holds more integration and convergence than a radical replacement of existing tools and platforms. Businesses need to focus on merging their data and marketing efforts seamlessly without being lured into acquiring new platforms unnecessarily.The Potential and Limitations of AI in MarketingWhen asked about the growing influence of artificial intelligence (AI) in the marketing sphere, Tejas provided an insightful overview of the exciting opportunities and inherent challenges. The idea of AI replacing or radically altering marketing roles is a common fear among many early-stage marketers. However, Tejas maintains a more balanced perspective on the matter, acknowledging both AI's potential and its limitations.Tejas recognizes the transformative potential of AI in marketing, emphasizing how AI could drastically enhance the marketing data experimentation process. Current limitations often restrict marketers to a few trials or force them to rely on gut instincts due to a lack of tools to test every possible variable. However, the introduction of AI can enable marketers to create numerous audiences and launch more sophisticated and targeted campaigns.Furthermore, Tejas underlines his enthusiasm for providing marketers with access to data platforms like Databricks and Snowflake, which are increasingly incorporating AI capabilities. This approach aligns with his strong belief in the power of AI to augment, rather than replace, the human intelligence involved in marketing strategies.Despite AI's capabilities, Tejas is cautious about the notion of it replacing the marketing department. Drawing from his experience as a CEO and dealing with the intricate facets of marketing, he emphasizes the enduring importance of qualitative changes. Data-driven strategies can't fully replicate the nuanced insights, intuition, and reasoning that human marketers bring to the table. These qualitative factors can dramatically alter program outcomes.In Tejas's view, while AI has the potential to radically alter the marketer's role, the most likely scenario is that AI will augment rather than replace these roles. The real revolution lies in how AI can unlock access to crucial business data, empowering marketers to self-service data, build audiences, and understand customer cohorts. The power of AI needs to infiltrate all aspects of marketing, from brand planning and audience segmentation to personalization and experimentation.Takeaway: While AI continues to evolve and impact various aspects of marketing, it isn't poised to replace marketers in the immediate future. Instead, it stands to augment their capabilities, empowering them with more data-driven insights and decision-making tools. The role of the marketer is likely to undergo a transformation, one marked by the increased integration of AI but still very much driven by human intelligence and intuition.Balancing Personal and Professional LivesWhen quizzed about maintaining happiness and success amidst his multi-faceted roles, Tejas unraveled his approach towards striking a perfect work-life balance. As a co-founder and co-CEO of Hightouch, a developer, and with various hobbies and interests, he certainly has his plate full.Yet, Tejas manages to juggle these roles without losing sight of his happiness and motivation. He attributes his balanced approach to the significant investment he makes in his personal relationships. Engaging with family and friends, participating in activities outside of work, and pursuing various hobbies help him navigate the demanding nature of his professional life.Tejas embraces the notion of being a "good amateur" in various pursuits, ranging from trying out new recipes with his girlfriend to exploring powerlifting, and occasionally playing the harmonica. These activities serve as refreshing breaks from the intensity of his professional life and provide him with joy and satisfaction. Though Hightouch demands his consistent and intense attention, the joy derived from personal life's simple pleasures ensures he maintains a balance. Of course, he humorously acknowledges the possibility of AI eventually replacing him, referencing our previous discussion.Takeaway: For Tejas, personal relationships and hobbies outside work are critical components in maintaining happiness, motivation, and achieving a balanced life. This illustrates that while professional success is important, investing in personal interests and relationships can significantly contribute to an individual's overall well-being.Episode RecapTejas offered an enlightening tour of the evolving Customer Data Platform (CDP) landscape. His profound insights provide us with a fresh perspective on the role of Composable CDPs in enhancing customer experiences and enabling marketers to leverage data more effectively.We dive deeper into the practical applications of Hightouch's composable CDP approach, we learned how this tool excels beyond reverse ETL. Hightouch eliminates the need for an extensive engineering effort and taps into existing data sources across businesses, offering marketers a more efficient way to build audiences. What's more, Hightouch is not just recognized for its capabilities but is also acknowledged by some of the world's largest companies as their go-to CDP.Tejas underscored a crucial point about the evolving martech industry — that it isn't about chasing comprehensive solutions but about adopting use case-driven strategies. Hightouch embodies this shift by focusing on data activation and personalization. The company carves a path towards a future where CDPs provide more adaptable data architectures, staying aligned with changing business needs.A key takeaway from the episode is the importance of alignment and collaboration between marketing and data teams. Tejas explained that the 'truth' reconciliation in data isn't solely a tech problem—it requires an operational shift that involves teamwork, clear communication, and alignment. Software solutions like Hightouch provide the necessary tech framework, but the human element remains instrumental in addressing these challenges.Finally, Tejas' insights caution us that while the industry is leaning towards a warehouse-native approach, it doesn't equate to a complete overhaul of the martech landscape. Rather, businesses should focus on integrating their data and marketing efforts seamlessly without unnecessary diversions to new platforms. This shift should lead to more convergence and integration than radical replacements.✌️--Intro music by Wowa via UnminusCover art created with Midjourney

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?

Tejas Manohar, Co-CEO at Hightouch, talks about the importance and benefits of data warehousing. Traditional CDPs can be a time-consuming and complex process for larger enterprises with a lot of data. However, by eliminating the need for a separate source of truth, composable CDPs reduce the laborious processes and incomplete views that traditional CDPs often result in. Today, Tejas discusses why composable CDPs are your next-generation tech. Show NotesConnect With: Tejas Manohar: 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.

Tejas Manohar, Co-CEO at Hightouch, talks about the importance and benefits of data warehousing. Traditional CDPs can be a time-consuming and complex process for larger enterprises with a lot of data. However, by eliminating the need for a separate source of truth, composable CDPs reduce the laborious processes and incomplete views that traditional CDPs often result in. Today, Tejas discusses why composable CDPs are your next-generation tech. Show NotesConnect With: Tejas Manohar: 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.

Tejas Manohar, Co-CEO at Hightouch, talks about the importance and benefits of data warehousing. Many marketing technology problems come down to the challenge of effectively activating data. However, many companies are starting to take a new approach to the issue by leveraging existing investments like their data warehouse to simplify the data activation process. Today, Tejas discusses the data warehouse and why it's your most powerful marketing tool. Show NotesConnect With: Tejas Manohar: 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.

Tejas Manohar, Co-CEO at Hightouch, talks about the importance and benefits of data warehousing. Many marketing technology problems come down to the challenge of effectively activating data. However, many companies are starting to take a new approach to the issue by leveraging existing investments like their data warehouse to simplify the data activation process. Today, Tejas discusses the data warehouse and why it's your most powerful marketing tool. Show NotesConnect With: Tejas Manohar: 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.

Data Activation is the method of unlocking the knowledge sorted within your data warehouse, and making it actionable by your business users in the end tools that they use every day. In doing so, Data Activation helps bring data people toward the center of the business, directly tying their work to business outcomes. Hightouch is The post Data Activation with Tejas Manohar appeared first on Software Engineering Daily.

Data Activation is the method of unlocking the knowledge sorted within your data warehouse, and making it actionable by your business users in the end tools that they use every day. In doing so, Data Activation helps bring data people toward the center of the business, directly tying their work to business outcomes. Hightouch is The post Data Activation with Tejas Manohar appeared first on Software Engineering Daily.

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/)

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.

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

Tejas Manohar is Co-Founder of Hightouch, a Bay Area-based startup that helps customers sync data over 80 destinations. Hightouch's goal with data activation is to help companies be proactive about the kinds of business workflows they want to drive based on the data in their warehouse. Listen as Tejas shares about Hightouch's exponential growth. Given their customer base spans across so many industries, it's obvious that this is a problem every company faces. Show Notes: Check out HightTouch: https://hightouch.com/ Connect with Tejas on LinkedIn: https://www.linkedin.com/in/tejasmanohar/ Follow Hightouch on Twitter: https://twitter.com/HightouchData On Tap for today's episode: Coffee! Contact Us: https://www.hashmapinc.com/reach-out

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.

Meet today's Sharks: - Tejas Manohar, Co-Founder & CEO of Hightouch - Jared Hillam, EVP of Emerging Technologies at Intricity - Arkady Kleyner, Principal, and CoFounder of Intricity Watch the Video on YouTube Sign up for future live sessions intricity.com/datasharks Talk with a Data Shark: intricity.com/intricity101 www.intricity.com youtube.com/intricity101

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 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

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.

Mark Rittman is joined by Tejas Manohar from Hightouch to talk about the concept of "reverse ETL", his journey from working on Segment's Personas product to co-founding Hightouch and his recent guest post on the Fivetran Blog, "Why Your Customer Data Platform Should Be the Data Warehouse".Why Your Customer Data Platform Should Be the Data Warehouse Hightouch Ushers In The Era Of Operational Analytics Hightouch homepage Customer 360 Data Warehousing and Sync to Hubspot using BigQuery, dbt, Looker and Hightouch Why (and How) Customer Data Warehouses are the New Customer Data Platform Drill to Detail Ep.76 'Segment, Ecosystems and Customer Data Platforms' with Special Guest Calvin French-Owen

Mark Rittman is joined by Tejas Manohar from Hightouch to talk about the concept of "reverse ETL", his journey from working on Segment's Personas product to co-founding Hightouch and his recent guest post on the Fivetran Blog, "Why Your Customer Data Platform Should Be the Data Warehouse".Why Your Customer Data Platform Should Be the Data Warehouse Hightouch Ushers In The Era Of Operational Analytics Hightouch homepage Customer 360 Data Warehousing and Sync to Hubspot using BigQuery, dbt, Looker and Hightouch Why (and How) Customer Data Warehouses are the New Customer Data Platform Drill to Detail Ep.76 'Segment, Ecosystems and Customer Data Platforms' with Special Guest Calvin French-Owen