Podcasts about data teams

At Gartner Data & Analytics Summit in Orlando, I spoke with Raj Bains, CEO of Prophecy on The Ravit Show, about why they're back at Gartner, the key data challenges they're solving, and how AI is playing a bigger role in modern data transformation. We also discussed their latest funding, real-world customer use cases, and what to expect from their panel discussion with Sanjeev Mohan on Wednesday: "The Impact of GenAI on Data Teams."#GartnerDA #GenAI #GartnerOrlando2025 #theravitshow

Today, I am going to share some of the biggest challenges internal enterprise data leaders may face when creating their first revenue-generating data product. If your team is thinking about directly monetizing a data product and bringing a piece of software to life as something customers actually pay for, this episode is for you. As a companion to this episode, you can read my original article on this topic here once you finish listening!

Data teams have had a rough few years. Lots of layoffs and cuts, doing more work with fewer resources, and a general sense of malaise. On top of all of this, now we're in the middle of a trade war. This is war. If you're a data team, you need to act accordingly.

Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch, AI & Data Leader, reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.

Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch, AI & Data Leader, reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.

Thu, 10 Apr 2025 22:00:00 +0000 https://mydata.podigee.io/265-new-episode fc5a4e3f5fa24e2fb1ca02880b46d260 Wie entwickelt sich Data und Digital in einem Handelsunternehmen? Wie sieht die Reise über 25 Jahre hinweg aus? Und warum ist es so wichtig, verschiedene Datensysteme zu vereinen? Darum geht es in der neuen Folge von MY DATA IS BETTER THAN YOURS, in der Host Jonas Rashedi mit Christopher Gutknecht spricht. Er leitet bei der Bergzeit GmbH das interne Datenteam und hat eine spannende Reise vom Performance Marketing in die zentrale Welt des Datenmanagements hinter sich. Zunächst geht es im Gespräch um Bergzeit selbst - ein Bergsport-Onlinehändler, der letztes Jahr sein 25-jähriges Jubiläum feierte. Von seinen Anfängen als Skitourenportal hat sich das Unternehmen zu einem europaweit tätigen Spezialisten entwickelt, der besonders bei Ausrüstung "ab 1000 Höhenmetern" glänzt. Chris erklärt die Herausforderungen, die durch zwei parallele Datensysteme entstanden sind: Ein Microsoft-basierter Stack für die "offline" Daten rund um Warenwirtschaft und Logistik sowie eine Google-Umgebung für die Webanalyse. Nach vielen Jahren der getrennten Entwicklung erkannte man, dass diese Trennung nicht mehr praktikabel war. Besonders spannend ist die Erklärung, warum man bei solchen Migrationen zunächst alle Altlasten mitnehmen muss, bevor man Verbesserungen einführt. Chris beschreibt anschaulich, wie wichtig es ist, die Fachabteilungen nicht mit plötzlich veränderten Zahlen zu konfrontieren. Im weiteren Verlauf diskutieren die beiden die besondere "Digital-DNA" eines Onlinehändlers und warum dennoch Prozesse wie Logistik, Einkauf und Lagerhaltung entscheidend für den Erfolg sind. Chris betont, wie wichtig es ist, analytisches Denken in allen Teams zu verankern, und berichtet von positiven Beispielen, wo Mitarbeiter plötzlich anfingen, mit SQL zu arbeiten. Zum Schluss geht es noch um Chris' persönliche Sportdaten und seine Identifikation als "Purple People" - jemand, der weder nur Business noch nur Technologie ist, sondern beides verbindet. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts. Zum LinkedIn-Profil von Chris: https://www.linkedin.com/in/chrisgutknecht/ Zur Webseite von Bergzeit: https://www.bergzeit.de/ Zu allen wichtigen Links rund um Jonas und den Podcast: https://linktr.ee/jonas.rashedi 00:00:00 Intro und Begrüßung 00:01:21 Vorstellung Chris 00:02:05 Bergzeit als Unternehmen 00:03:18 Positionierung des Unternehmens 00:05:30 Entwicklung der Data-Teams 00:07:47 Die zwei Daten-Systeme 00:11:59 Herausforderungen bei der Datenmigration 00:15:12 Digitale DNA im E-Commerce 00:19:21 Die Customer Journey im Onlinehandel 00:27:17 Analytisches Denken in allen Teams 00:30:30 Herausforderungen des Treiberbaums 00:33:05 Hub and Spoke-Modell 00:36:35 Datenkultur und Organisation 00:41:39 Orchestrierung und technische Herausforderungen 00:44:28 Persönliche Datennutzung full

Highlights from this week's conversation include:The Impact of AI (1:25)Historical Context of Technology (2:31)Pre-existing Infrastructure for Change (4:42)AI as a Personal Assistant (7:10)Future of Company Roles (9:13)Managing Teams in a Dystopian AI Future (12:31)Business Architecture Choices (15:52)Integration Tool Usage (18:07)AI's Impact on Data Roles (21:53)AI as an Interface (24:04)Trust in AI vs. SQL (27:12)Snowflake's Acquisition of Dataflow (29:54)Regression to the Mean Concept (33:49)AI's Role in Data Platforms (37:04)User Experience in Data Tools (44:41)Future of Data Tools (46:57)Environment Variable Setup (51:10)Future of Software Implementation and Parting Thoughts (52:10)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Global Agile Summit Preview: Implementing Agile Practices for Data and Analytics Teams with Henrik Reich In this BONUS Global Agile Summit preview episode, we dive into the world of Agile methodologies specifically tailored for data and analytics teams. Henrik Reich, Principal Architect at twoday Data & AI Denmark, shares his expertise on how data teams can adapt Agile principles to their unique needs, the challenges they face, and practical tips for successful implementation. The Evolution of Data Teams "Data and analytics work is moving more and more to be like software development." The landscape of data work is rapidly changing. Henrik explains how data teams are increasingly adopting software development practices, yet there remains a significant knowledge gap in effectively using certain tools. This transition creates both opportunities and challenges for organizations looking to implement Agile methodologies in their data teams. Henrik emphasizes that as data projects become more complex, the need for structured yet flexible approaches becomes critical. Dynamic Teams in the Data and Analytics World "When we do sprint planning, we have to assess who is available. Not always the same people are available." Henrik introduces the concept of "dynamic teams," particularly relevant in consulting environments. Unlike traditional Agile teams with consistent membership, data teams often work with fluctuating resources. This requires a unique approach to sprint planning and task assignment. Henrik describes how this dynamic structure affects team coordination, knowledge sharing, and project continuity, offering practical strategies for maintaining momentum despite changing team composition. Customizing Agile for Data and Analytics Teams "In data and analytics, tools have ignored agile practices for a long time." Henrik emphasizes that Agile isn't a one-size-fits-all solution, especially for data teams. He outlines the unique challenges these teams face: Team members have varying expectations based on their backgrounds Experienced data professionals sometimes skip quality practices Traditional data tools weren't designed with Agile methodologies in mind When adapting Agile for data teams, Henrik recommends focusing on three key areas: People and their expertise Technology selection Architecture decisions The overarching goal remains consistent: "How can we deliver as quickly as possible, and keep the good mood of the team?" Implementing CI/CD in Data Projects "Our first approach is to make CI/CD available in the teams." Continuous Integration and Continuous Deployment (CI/CD) practices are essential but often challenging to implement in data teams. Henrik shares how his organization creates "Accelerators" - tools and practices that enable teams to adopt CI/CD effectively. These accelerators address both technological requirements and new ways of working. Through practical examples, he demonstrates how teams can overcome common obstacles, such as version control challenges specific to data projects. In this segment, we refer to the book How to Succeed with Agile Business Intelligence by Raphael Branger. Practical Tips for Agile Adoption "Start small. Don't ditch scrum, take it as an inspiration." For data teams looking to adopt Agile practices, Henrik offers pragmatic advice: Begin with small, manageable changes Use established frameworks like Scrum as inspiration rather than rigid rules Practice new methodologies together as a team to build collective understanding Adapt processes based on team feedback and project requirements This approach allows data teams to embrace Agile principles while accounting for their unique characteristics and constraints. The Product Owner Challenge "CxOs are the biggest users of these systems." A common challenge in data teams is the emergence of "accidental product owners" - individuals who find themselves in product ownership roles without clear preparation. Henrik explains why this happens and offers solutions: Clearly identify who owns the project from the outset Consider implementing a "Proxy PO" role between executives and Agile data teams Recognize the importance of having the right stakeholder engagement for requirements gathering and feedback Henrik also highlights the diversity within data teams, noting there are typically "people who code for living, and people who live for coding." This diversity presents both challenges and opportunities for Agile implementation. Fostering Creativity in Structured Environments "Use sprint goals to motivate a team, and help everyone contribute." Data work often requires creative problem-solving - something that can seem at odds with structured Agile frameworks. Henrik discusses how to balance these seemingly conflicting needs by: Recognizing individual strengths within the team Organizing work to leverage these diverse abilities Using sprint goals to provide direction while allowing flexibility in approach This balanced approach helps maintain the benefits of Agile structure while creating space for the creative work essential to solving complex data problems. About Henrik Reich Henrik is a Principal Architect and developer in the R&D Department at twoday Data & AI Denmark. With deep expertise in OLTP and OLAP, he is a strong advocate of Agile development, automation, and continuous learning. He enjoys biking, music, technical blogging, and speaking at events on data and AI topics. You can link with Henrik Reich on LinkedIn and follow Henrik Reich's blog.

Collaboration between data teams and stakeholders isn't always smooth. Misaligned expectations, shifting requirements, and a lack of mutual understanding can lead to frustration on both sides.     In this cohosted  episode, Karen and Cecilia dive into the key challenges data teams face when working with stakeholders —and, more importantly, how to overcome them. They share practical strategies to:  Get stakeholders to bring problems, not just predefined solutions  Align expectations early and avoid last-minute changes  Build empathy and trust between technical and business teams    Tune in for actionable insights that will help you work better with stakeholders and maximise the value of data analytics! 

Too many companies get caught up in the latest tools and technical details of managing their data. But what if they're missing the bigger picture? Pull up a chair as Lindsay Murphy chats with Tim and Juan about what happens when data teams break out of the technical box and become key players in business success. Discover why data isn't just about building systems – it's about making better decisions and driving your business forward.

Too many companies get caught up in the latest tools and technical details of managing their data. But what if they're missing the bigger picture? Pull up a chair as Lindsay Murphy chats with Tim and Juan about what happens when data teams break out of the technical box and become key players in business success. Discover why data isn't just about building systems – it's about making better decisions and driving your business forward.

In this engaging episode, Amir Bormand interviews Marko Vasiljevic, CTO at Ghost, about the evolving nature of data teams and how businesses can better structure them in a rapidly changing tech landscape. The discussion covers the shift toward more engineering-driven data teams, the influence of AI on team composition, and how businesses can adapt to remain competitive. Marko also highlights the future of self-service data solutions and the decreasing technical barriers for companies of all sizes. Key Takeaways: Redefining Data Teams: The need for more engineering-driven data teams as traditional data roles evolve into more technical, systems-oriented functions. Data as a Product: Viewing data teams as creators of products that support business insights, moving beyond traditional data reporting. Self-Service Analytics: How businesses can build infrastructure that empowers teams with on-demand data access while reducing reliance on specialized data analysts. AI-Driven Change: The growing influence of AI in automating technical tasks and shifting the focus toward business-driven problem-solving. Future of Data Roles: The potential merging of technical and business roles as AI-driven automation reduces the need for deep technical expertise. Episode Highlights: [00:00] Introduction – Overview of the episode and guest introduction. [01:24] Defining Data Teams – How the traditional data team model is evolving. [03:42] Building Self-Service Data Systems – The path toward more accessible and scalable analytics. [06:05] DevOps for Data Analytics – Applying DevOps principles to data team operations. [09:48] Evolving Data Skillsets – Shifting from SQL-heavy roles to full-stack data engineering. [13:53] AI's Role in Data Teams – How AI will reshape data and software engineering functions. [17:29] Business-Driven Data Models – Encouraging product managers to lead tech initiatives. [21:55] Organizational Impacts – Where technical data roles should report in a modern organization. [28:33] Competitive Pressures – How automation lowers entry barriers and intensifies market competition. Don't miss Marko's valuable insights on building future-proof data teams and staying ahead in an AI-driven world. Like, share, and subscribe for more expert discussions! Guest: Marko is a seasoned technologist and CTO who's built and managed engineering, data, product, and design teams at several high-growth startups and unicorns. Marko also founded a few startups and successfully exited one. He's passionate about solving real-world problems with technology, and he is excited about the future of AI. https://www.linkedin.com/in/marmarko/

In this episode of Alter Everything, we chat with Barzan Mozafari, CEO and co-founder of Keebo. We discuss how Keebo reduces data warehouse costs, optimizes data pipelines, and equips data professionals to leverage AI effectively. Barzan shares insights on Keebo's mission, technology, and approach to automating and enhancing data operations. He also offers advice for data professionals on optimizing pipelines, tackling challenges in data management, and embracing AI to stay ahead in the industry.Panelists: Barzan Mozafari, co-founder and CEO of Keebo - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Keebo.ai Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

Highlights from this week's conversation include:Brian's Background and Journey in Data and Marketing (0:56)AI and Data Strategy (2:12)Experience at DreamWorks Animation (3:15)Marketing Timeline for Movies (5:18)Data-Driven Decisions at Expedia (9:04)Advising High-Growth Companies (14:59)LinkedIn Connections and Networking (17:57)Tension Between Marketing and Data Teams (19:59)Technology Spending in Marketing (22:07)Advice for Tech Leaders Facing Brand Marketers (25:50)Frequency of Replatforming (30:11)Understanding Data Accessibility (33:58)Data as a Product (00:37:58)Overhyped AI Applications (39:00)Underutilized AI Opportunities (00:41:51)AI's ROI Challenges (47:01)Effective AI Support Systems (52:33)Potential Ventures to Pursue Outside of Data (56:04)Final Thoughts and Takeaways (1:00:11)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.But in helping everyone else in the business, data professionals can often forget to help themselves.However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there's no reason why they can't also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively.Guest BioProf. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems.Highlights(00:05) Meet Barzan Mozafari(00:50) The role of AI in data engineering(01:36) The birth of Keebo(02:34) Challenges in modern data pipelines(05:41) How Keebo optimizes data warehousing(07:35) AI and ML techniques behind Keebo(08:47) Reinforcement learning in practice(16:23) Guardrails and safeguards in AI systems(26:29) The build vs. buy dilemma(36:03) Future trends in data science and AI(39:36) Final advice for data scientists(40:50) Closing remarks and contact informationLinksKeebo websiteConnect with Barzan on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Genevieve Hayes Consulting Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.But in helping everyone else in the business, data professionals can often forget to help themselves.However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there's no reason why they can't also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively. Guest Bio Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems. Highlights (00:05) Meet Barzan Mozafari(00:50) The role of AI in data engineering(01:36) The birth of Keebo(02:34) Challenges in modern data pipelines(05:41) How Keebo optimizes data warehousing(07:35) AI and ML techniques behind Keebo(08:47) Reinforcement learning in practice(16:23) Guardrails and safeguards in AI systems(26:29) The build vs. buy dilemma(36:03) Future trends in data science and AI(39:36) Final advice for data scientists(40:50) Closing remarks and contact information Links Keebo websiteConnect with Barzan on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Highlights from this week's conversation include:Lightning Round Setup (1:15)Scenarios for New Data Leaders (2:33)Optimism vs. Reality (3:14)Cynical Perspective on Data Roles (5:32)Monitoring Systems Discussion (9:31)Executive Alignment Challenges (12:54)Understanding Team Dynamics (17:32)Head of Data vs. Head of Product (20:13)Product Development Steps (22:14)Consequences of Product Decisions (24:14)Challenges in Data Team Dynamics (26:03)Attribution Reporting Complexity (28:24)Long-Term Vision for Data Teams (29:22)AI Summaries Discussion (30:19)Closing Thoughts on AI Nuance (32:02)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

CEO and Co-Founder of GrowthLoop, Chris Sell, delves into bridging the gap between marketing and data teams. GrowthLoop, a leading composable customer data platform (CDP), enables marketers to create dynamic audience segments, orchestrate cross-channel journeys, and assess campaign performance through its advanced data cloud. Seamlessly integrating with top data warehouses, GrowthLoop is revolutionizing the connection between data cloud and growth platforms. Show NotesConnect With: Chris Sell: 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.

CEO and Co-Founder of GrowthLoop, Chris Sell, delves into bridging the gap between marketing and data teams. GrowthLoop, a leading composable customer data platform (CDP), enables marketers to create dynamic audience segments, orchestrate cross-channel journeys, and assess campaign performance through its advanced data cloud. Seamlessly integrating with top data warehouses, GrowthLoop is revolutionizing the connection between data cloud and growth platforms. Show NotesConnect With: Chris Sell: 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.

Highlights from this week's conversation include:Ethan's Background (0:47)John's Background (1:16)Data Teams vs. Engineering Teams (2:04)Career Paths in Data (3:40)Pressure of Large Companies (6:10)Contrasting Industries: Expedia vs. Gallo (9:02)Establishing Trust in Data (11:30)From Sales to Data (16:30)Understanding Success Metrics (18:58)Creating Daily Business Value (23:03)Aligning Data Work with Leadership Goals (29:30)Differences Between Data and Software Engineering (31:25)The Role of Data in Business (35:56)Understanding Data Contracts (39:35)Accuracy vs. Usability in Data (41:51)Observational Skills in Data Roles (44:03)Defining Product in Data vs. Software (47:07)Final Thoughts and Takeaways (52:42)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Larry Burns and I chat about all things data teams—how they fail, their challenges, and how they can add value. To add value, we need to reimagine not only how we think about data but also how we manage knowledge. Larry brings a fresh and battle-worn perspective to the data field, and if you work on or manage a data team, this conversation is worth a listen. LinkedIn: https://www.linkedin.com/in/larryburnsdba/

Today I had the pleasure of interviewing Alex Gold. Alex is the Director of Solutions Engineering + Support at posit and author of DevOps for Data Science. In this episode we talk about Alex's experience working in data for presidential campaigns, his experience in solutions engineering, and the role that DevOps plays in the data domain. Book: https://www.routledge.com/DevOps-for-Data-Science/Gold/p/book/9781003213345(You can use Code AFLY04 for a 20% discount)Alex's Links:LinkedIn - https://www.linkedin.com/in/alexkgold/Twitter - https://x.com/alexkgoldPodcast Sponsors, Affiliates, and Partners:- Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job)- Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today- Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee |  Interview prep questionsListen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!)On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFblOn Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQMORE DATA SCIENCE CONTENT HERE:

Show description: Scott Breitenother, founder of data consultancy Brooklyn Data Co., joins Tristan at Coalesce 2024 in Las Vegas to discuss the early days of dbt, the evolution of data teams, and what's next for the dbt community. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

In this episode, Jackie Clayton, Analytics & Data Science Lead at Mars, discusses creating effective learning cultures in data teams. Drawing from her experience establishing data academies, Jackie shares insights on:  Integrating learning into daily work  Making the most of your learning budget  Learning without having to spend money for it  Involving all team members in the learning process  Extending data education across organisations  She offers practical advice for fostering a learning environment in both large and small teams, emphasising the importance of aligning learning with business goals and sharing knowledge. Jackie also touches on how one can strategically use learning budgets when available. 

Mark Rittman is joined in this episode by Ethan Aaron, Founder & CEO of Portable, to talk about what you should do if you're the first data hire at a company, when and when not to hire a consultant, the founding story of Portable.io and the long tail (and economic model) of the data integration connectors market.Ethan Aaron LinkedIn ProfilePortable.io Homepage“The biggest misconceptions about data integrations..” (LinkedIn)“You join a 100 person company as the head of data. What should you do?” (LinkedIn)“The stuff no one will tell you about running a data team” (LinkedIn)

CEO and Co-Founder of GrowthLoop, Chris Sell, delves into bridging the gap between marketing and data teams. GrowthLoop, a leading composable customer data platform (CDP), enables marketers to create dynamic audience segments, orchestrate cross-channel journeys, and assess campaign performance through its advanced data cloud. Seamlessly integrating with top data warehouses, GrowthLoop is revolutionizing the connection between data cloud and growth platforms. Show NotesConnect With: Chris Sell: 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.

CEO and Co-Founder of GrowthLoop, Chris Sell, delves into bridging the gap between marketing and data teams. GrowthLoop, a leading composable customer data platform (CDP), enables marketers to create dynamic audience segments, orchestrate cross-channel journeys, and assess campaign performance through its advanced data cloud. Seamlessly integrating with top data warehouses, GrowthLoop is revolutionizing the connection between data cloud and growth platforms. Show NotesConnect With: Chris Sell: 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.

Finding order in chaotic datasets is key for companies to be able to implement AI solutions that actually work. This week's guest is Prukalpa Sanka, the CEO and co-founder of Atlan, a platform designed to help data teams build trust, streamline workflows, and collaborate more effectively. Atlan emerged from the challenges of managing complex data projects and is now empowering organizations to scale their data operations while maintaining transparency and trust. In this episode they discuss how Atlan has maintained a fun company culture despite the stress and how her and her co-founder have maintained a great working relationship for the past 12 years. 00:00 - Introduction02:31 - Founding story and early challenges06:19 - Overcoming high-stakes data issues08:45 - The evolution of data and AI13:31 - Building a product to solve key problems16:13 - Adapting to technological change19:21 - The growing role of AI in business21:05 - Preparing data for AI integration24:27 - Addressing bias and ethics in AI26:52 - Fundraising and fun marketing31:34 - Maintaining a strong co-founder relationship33:47 - Final thoughts and reflections Found posts every Tuesday. Subscribe on Apple, Spotify or wherever you listen to podcasts to be alerted when new episodes drop. Check out the other TechCrunch podcast: Equity . Subscribe to Found to hear more stories from founders each Connect with us:On TwitterOn InstagramVia email: found@techcrunch.com

Highlights from this week's conversation include:Edward's Background and Journey in Data (0:44)P&L Ownership Discussion (1:15)Challenges in Profit Ownership (3:38)Data Team Dynamics (5:52)Role Clarity Between CFO and CDO (7:31)Nuances of Data Leadership (11:24)Focus on Relevance in Data Work (14:05)Best Buy's Personalization Project (18:39)Building a Data Stack (21:00)Crowd-Driven Algorithms (25:26)Event-Based Personalization (28:12)Corporate Politics and Implementation (31:00)In-Store Experience Innovations (33:16)Impact of Data Science at Best Buy (37:14)The Importance of Data Teams in AI Implementation (39:19)Using AI Conversationally (42:09)Book Recommendations for Data Leaders (44:24)Final Thoughts and Takeaways (47:05)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

On this episode of Secret Ops, we talk with Antonia Landi and Joy Chatterjee, Co-Founders of One Camp, whose aim is to bridge the gap between Product & Data Teams by giving both groups the tools and knowledge to do their best work. Antonia and Joy bring to life their own experiences connecting these two specializations together, giving advice on how to improve communication and teamwork while doing it. But that's not all!In this episode, we discuss:Challenges faced in aligning product and data teams.Evolving roles of product ops and data science in growing organizations.Product ops as a strategic function rather than a support role.Overcoming insecurities around data skills and analysis.Need for improved accessibility to data for product teams.Fostering a team culture of discovery and curiosity

Highlights from this week's conversation include:Benn's Background and Journey in Data (0:48)Reflection on Strategy and Vision (2:10)The Importance of Doing It Your Way (4:10)Early Experiences and Blogging (6:27)Self-Imposed Pressure in Startups (8:24)The Challenge of Decision-Making (12:11)Key Decisions in a Startup's Trajectory (15:48)Understanding Startup Anxiety (17:24)Importance of Focus in Data Startups (20:02)Product Market Fit Insights (24:38)Cultural Change and Product Fit (30:23)Evolution of Data Teams (31:57)The Role of High-Profile Data Successes (34:12)Challenges of Data in Smaller Businesses (36:16)Product Team Dynamics (38:18)The Future of Excel (41:11)Anti-Patterns in Data Usage (45:05)Imagining Excel's Replacement (47:05)Exploring New Data Solutions (49:24)The Role of LLMs in Data Analysis (51:29)Final Thoughts and Takeaways (53:10)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

What do PMax, Advantage+ and an 8-year-old in an arcade have in common? They'll spend as much as they can, if you let them! In this episode, join Jim and Simon as they dive deep into the world of PPC with Navah Hopkins, a seasoned expert with over 16 years in the marketing industry. Navah shares her insights on Google's Performance Max (PMax) campaigns, discussing the intricacies of managing them, excluding branded searches, and overcoming broader measurement challenges. Links from the show: Navah on LinkedIn Optmyzr 00:00 Vacation Plans and PPC Jokes 01:25 Introducing Today's Guest: Navah Hopkins 03:21 Diving into Performance Max Campaigns 06:23 Challenges and Strategies in PPC 10:42 Understanding Performance Max and Asset Management 16:54 The Role of Ad Strength and Measurement in PPC 24:16 Incrementality Testing Challenges 24:35 Meta's Advantage Plus and A/B Testing 25:06 Google's Approach to Testing and Privacy 25:57 Geo-Based Holdout Testing 26:24 PMax Campaigns and Branded Search 27:46 Testing Strategies and Market Considerations 28:29 Google's Targeting Limitations 28:55 Branded Search in PMax 33:05 Data Teams and PPC Collaboration 38:16 Onboarding and Training for Measurement Practitioners 47:45 Creative and Data-Driven Marketing 49:47 Productivity Tips and Metal Music

Episode OverviewJordan Morrow is a visionary thought leader and prolific author in the world of data and analytics, and on Episode #51 of the CDO Matters Podcast, he joins Malcolm to discuss data leadership and building a ‘next gen' data team.In this engaging and provocative discussion, Jordan and Malcolm discuss the most important roles and responsibilities that are today missing on most data teams but are increasingly critical to succeed in an AI-driven world of constant change and disruption. Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Jordan Morrow on LinkedIn

Highlights from this week's conversation include:Ben's background and journey in data (0:18)Relating data to business outcomes (2:33)Facebook's approach to data-driven business outcomes (4:43)Subjectivity and data-driven business outcomes (8:43)Infrastructure and data collection at Facebook (12:04)The importance of first-party data and the death of third-party cookies (16:27)Facebook's Data and Attribution Challenges (20:08)Facebook's Infrastructure and Tooling (23:41)Differences in Data Approaches (28:26)Challenges of Data Project Alignment with Business Outcomes (32:58)Integration of Data into Tools and Partnerships (35:12)Building Alliances with Embedded Data Analysts (38:08)Budgeting for Data Teams (40:02)Healthy Team Dynamics and Budgeting (44:18)Data Team Reporting Structure (46:23)Connecting with Ben and More Content (50:55)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Gordon Wong and I chat about why most data teams aren't that valuable, and ways data teams can deliver more value.

Sol Rashidi is an esteemed executive, leader, and influencer within the AI, Data, and Technology space.  Having helped IBM launch Watson in 2011 as one of the earliest world applications of Artificial Intelligence, Sol has pioneered some of the early advancements of space.  Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to  @WeightsBiases  for sponsoring this episode. WandB Free Courses - http://wandb.me/courses_mlops MLOps podcast #227 with Sol Rashidi, CEO & Co-Founder of ExecutiveAI, Leading Enterprise Data Teams. // Abstract In the dynamic landscape of MLOps and data leadership, Sol shares invaluable insights on building successful teams and driving impactful projects. In this podcast episode, Sol delves into the importance of prioritizing relationships, introduces a pragmatic "Wrong Use Cases Formula" to streamline project prioritization, and emphasizes the critical role of effective communication in data leadership. Her wealth of experience and practical advice provide a roadmap for navigating the complexities of MLOps and leading data-driven initiatives to success. // Bio With eight (8) patents granted, 21 filed, and received awards that include: "Top 100 AI People" 2023 "The Top 75 Innovators of 2023" "Top 65 Most Influential Women in 2023" "Forbes AI Maverick of the 21st Century" 2022 “Top 10 Global Women in AI & Data”, 2023 "Top AI 100 Award", 2023 “50 Most Powerful Women in Tech”, 2022 “Global 100 Power List” - 2021, 2022, 2023 “Top 20 CDOs Globally” - 2022 "Chief Analytics Officer of the Year" - 2022 "Isomer Innovators of the Year" - 2021, 2022, 2023 "Top 100 Innovators in Data & Analytics” - 2020, 2021, 2022, 2023 "Top 100 Women in Business" - 2022 Sol is an energetic business executive and a goal-oriented technologist, skilled at coupling her technical acumen with story-telling abilities to articulate business value with both startups and Fortune 100's who are leaning into data, AI, and technology as a competitive advantage while wanting to preserve the legacy in which they were founded upon. Sol has served as a C-Suite member across several Fortune 100 & Fortune 500 companies including: Chief Analytics Officer - Estee Lauder Chief Data & Analytics Officer - Merck Pharmaceuticals EVP, Chief Data Officer - Sony Music Chief Data & AI Officer - Royal Caribbean Cruise Lines Sr. Partner leading the Digital & Innovation Practice- Ernsty & Young Partner leading Watson Go-To-Market & Commercialization - IBM Sol now serves as the CEO of ExecutiveAI LLC. A company dedicated to democratizing Artificial Intelligence for Humanity and is considered an outstanding and influential business leader who is influencing the space traveling the world as a keynote speaker, and serving as the bridge between established Gen1.0 markets and those evolving into 4.0. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sol's Book will be out on April 30, 2024 Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments: https://www.amazon.com/Your-Survival-Guide-Real-World-Deployments/dp/1394272634?ref_=ast_author_mpb --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sol on LinkedIn: https://www.linkedin.com/in/sol-rashidi-a672291/

In this episode of The Diary of a CDO, Bart Vandenreijt, Chief Data & Analytics Officer at Partenamut, a Belgian health insurer, shares his journey of transforming a disjointed data and analytics team into a business-critical function. In the context of his business background, he discusses how they've overcome challenges, aligned the data strategy with business objectives and been able to secure consistent funding to grow the team. Bart emphasises the value of hands-on, practical solutions, setting realistic expectations, and the pivotal role of his team in driving impactful change.

Join us for a heartfelt conversation with Jeremy Adamson, AI Strategy Leader and author of "Geeks with Empathy." Discover why empathy is the key to unlocking success in data-driven environments and learn practical strategies for cultivating empathy within your data team dynamics. Jeremy shares insights from his book on building meaningful relationships with clients and colleagues, and explores real-world examples illustrating how empathy fosters innovation and collaboration in data teams. Tune in and embark on a journey towards building a culture of empathy within your data team. You can get "Geeks with Empathy" at your local Amazon: https://www.amazon.ca/Geeks-Empathy-Jeremy-Adamson/dp/1738310809/

Folks in the data space are familiar with the concept of data literacy. However, a new idea is on the rise: business literacy. Whether folks sit in product, marketing, or commercial, there needs to be a productive balance between understanding business context and technical expertise of each department. This shared comprehension means ideas are more likely to be deployed and productionalized because everyone has deeper domain knowledge and business understanding.Sanjeevan Bala is making business literacy a top priority at his organization. He is the Group Chief Data and AI Officer at ITV, an Alation customer. There, he is responsible for driving the digital data and AI transformation and leading an offensive growth strategy that enhances how they produce, promote, distribute, and monetize content. Sanjeevan is an international thought leader, has won numerous awards for his work, and was named the most influential person in data by DataIQ. Satyen and Sanjeevan discuss the idea of a Data Product Manager, the importance of business literacy, and the power of experimentation.--------“I think because we went down the data as a product notion, that leadership role was a Data Product Manager. Incorporate product thinking in the way in which data is developed, designed, and used. I think what's beautiful about product thinking is it's very well adapted and equipped for understanding competing objectives and competing needs. Creating methods by which you're trying to either align or prioritize those needs. But, critically allows you to prioritize around the right things because you're constantly looking at how do you make sure you can productionize and scale and realize the full value? What does it take to do that? That goes way beyond what you're doing in data. That gets into organizational change, that gets into last mile technologies that you may not have thought about.” – Sanjeevan Bala--------Time Stamps*(05:16): How to define your organizational identity*(07:26): The art of storytelling and data-driven leadership*(18:20): Harnessing experimentation to drive organizational change*(25:10): Data literacy versus business literacy*(42:17): Balancing innovation and regulation in AI*(44:41): Satyen's Takeaways--------SponsorThis podcast is presented by Alation.Learn more:* Subscribe to the newsletter: https://www.alation.com/podcast/* Alation's LinkedIn Profile: https://www.linkedin.com/company/alation/* Satyen's LinkedIn Profile: https://www.linkedin.com/in/ssangani/--------LinksConnect with Sanjeevan on LinkedIn

This edWeb podcast is sponsored by Solution Tree.The webinar recording can be accessed here.In this edWeb podcast, you hear from Bo Ryan, Author of The Brilliance in the Building and Principal of Ana Grace Academy of the Arts 6-8 (CT). Bo shows how your urban school can make an impact on student achievement and change the trajectory of student lives using the time available during the day, the resources in the book, and the brilliance in the building—your amazing staff!Written with the needs of urban educators in mind, this essential guide shares specific action steps and ready-to-use tools for supporting successful Professional Learning Communities at Work®. All of the reproducibles were created based on 25 years of extensive research and actual use in schools. Leaders and staff in urban schools cannot do this work in isolation; they must work together to accomplish their goals, therefore, the work in this book is grounded in the PLC at Work principles. Listen to this session to learn how to create:A culture of care in your school environmentA culture of collaboration in your building with three categories: maximizing time, ensuring the success of teams, and coaching the teamsShared knowledge of the learning block process focused on creating a guaranteed and viable curriculum; creating, administering, and scoring common formative assessments; and meeting in data teams while bringing the team meeting to the classroomA culture of continuous learning in your school to provide staff with job-embedded, cost-effective professional developmentThis edWeb podcast is of interest to K-12 teachers and school leaders.Solution Tree Providing research-based, results-driven services that improve learning outcomes for students.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Learn more about viewing live edWeb presentations and on-demand recordings, earning CE certificates, and using accessibility features.

Highlights from this week's conversation include:The Unique Perspective of Practitioners (2:10)Account-based Marketing (6:30)Sales Development Representatives (SDR) (8:05)Descriptive, People, and Engagement Data (11:38)Data Overload and Actionable Data (14:20)Working with Data Teams and Internal Data (17:52)The relationship between business and data teams (22:27)The importance of collaboration between marketing and data teams (24:17)Travis and Hillary writing a book (25:33)The taxonomy of personas (34:23)Bucketing and grouping people in data systems (35:37)Account-based marketing and sales alignment (39:00)The data-driven approach and reliance on technology (44:25)Managing complexity in data and account-based marketing (45:35)Adapting to change and evolving data artifacts (51:58)The importance of understanding the business (54:58)Collaboration between data and go-to-market teams (55:56)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

In this week's episode of Data Unlocked, Jason sits down with Nick Schrock, the founder and CTO of Dagster Labs.With years of experience in the engineering space, Nick has worked for some of the biggest names in the industry, such as Microsoft, Facebook, and Care Evolution.In 2018, Nick left all of that behind to found his own company, Dagster Labs, where he is also CTO.Dagster Labs is a data orchestration platform built for productivity. Dagster, their open-source product, is primarily used by data engineering teams to make sure from a marketing and business stakeholder context that data is properly transformed.On Data Unlocked, we've brought in a lot of marketers to talk about their relationship with data teams and how they drive that relationship. But in this episode, Nick and Jason will be discussing that relationship from the other side of the house to answer three main questions: Are you working effectively with your data teams?What does great collaboration look like?And what steps do you need to take to make cross-functional collaboration as effective as possible?Are you ready?Let's dive in.Key Takeaways:Intro (00:00)Meet Nick (00:44)Let's go back in history (02:37)How to have better continuity and data access (11:00)Is your data good enough? (14:35)The data quality problem (22:05)Who would you have this conversation with again? (23:07)Additional Resources:Get in contact with Nick here.Learn more about Dagster Labs here.>>Learn more about us here.Follow us on LinkedIn, Twitter, and Instagram.If you enjoyed this episode, please follow, rate, and leave a review on your favorite podcast platform!

Description:On this episode, Benn Stancil, Co-Founder of Mode Analytics and current Field CTO at Thoughtspot  shares his thoughts on who wants to use data to solve problems, and who just wants data to validate an opinion. He brings unique perspectives to how data excellence can be proliferated through an organization, whether shadow IT is a nuisance or a guide post, and how large language models will influence the future of data. Key Moments:How an internal analytics tool Benn built at Yammer led to starting Mode Analytics to serve the new breed of technical data teams (4:00)Why the urgency around BI consolidation in 2023 prompted conversations with ThoughtSpot (52:30) The risks of shadow tools and lack of consistency; importance of a centralized data infrastructure (26:30)Using natural language search as an interface for asking novel questions and changing how people use data (44:00)Data teams need visibility into the business context; centralization for infrastructure but decentralization to embed in business units (23:30)How ambition plays an outsized role in the type of data tools selected in an organization (17:58)Key Quotes:Courage, ultimately, is needed in making a decision that you have to own. [Unfortunately], data can be a punt. If you make a data driven decision, nobody made the decision. The data made the decision. There is like an abstraction there of who was actually responsible for it. What ends up happening [in shadow IT] is you spend all this time just trying to figure out, like, which one do you trust? Who's right? Everybody has their different perspectiveGood Googlers are not people that know the, the exact syntax of weird Google searches. Good Googlers are the people who have some spider sense about where to go looking for things. And I think AI is probably going to be similar to that, where it's not the crazy prompt engineers that'll make it good. It's like some of the next level spider sense skills that we don't know yet.MentionsRShadow ITMicrosoft acquires YammerLarge Language Models (LLMs)ModeBioBenn Stancil is ThoughtSpot's Field CTO. He joined ThoughtSpot in 2023 as part of its acquisition of Mode, where he was a Co-Founder and CTO. While at Mode, Benn held roles leading Mode's data, product, marketing, and executive teams. He regularly writes about data and technology at benn.substack.com.  Prior to founding Mode, Benn worked on analytics teams at Microsoft and Yammer.Personal Details:- Enjoys playing baseball and is a Braves fan- Favorite pump up song: "Labour" by Paris Paloma- Grateful for opportunities to travel and gain new perspectives

Highlights from this week's conversation include:Santona's journey from nuclear physics to data science (4:59)The appeal of startups and wearing multiple hats (8:12)The challenge of pseudoscience in the news (10:24)Approaching data with creativity and rigor (13:22)Challenges and differences in data workflows (14:39)Schema Evolution and Quality Problems (27:01)Real-time Data Monitoring and Anomaly Detection (30:34)The importance of data as a business differentiator (35:48)The SQL job creation process (46:25)Different options for creating solver jobs (47:20)Adding column-level expectations (50:17)Discussing the differences of working with data as a scientist and in a startup (1:00:18)Final thoughts and takeaways (1:04:01)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Highlights from this week's conversation include:Nick's background and journey in data (2:28)Founding Dagster Labs (7:50)The evolution of data engineering (12:32)Fragmentation in data infrastructure (15:04)The role of orchestration in data platforms (19:53)The importance of operational tools for data pipelines (25:01)Lessons learned from working with GraphQL (26:19)The role of the orchestrator in data engineering (34:51)The boundaries between data infrastructure and product engineering (37:33)Different orchestrators in the data infrastructure landscape(42:03)The role of MLOps in data engineering (46:04)Data Quality and Orchestration (51:04)Future of Data Teams and Orchestration (54:27)Final thoughts and takeaways from (58:01)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Nick Schrock is Founder of Dagster Labs & Creator of Dagster - the open source orchestration platform for the development, production, and observation of data assets. Dagster Labs has raised just under $50M from investors including Sequoia, Index, and Georgian Partners. In this episode, we discuss how Dagster is bringing software engineering principles to the data space, what a great developer experience means for data engineers, how to think about launching the cloud version of your open source project & much more!

What's New in Data – a popular podcast and thought leadership series hosted by John Kutay – did a special live episode at the top of Salesforce Tower in San Francisco. Bruno Aziza from CapitalG, Ridhima Kahn from Dapper Labs, and Sanjeev Mohan of SanjMo (former VP of Data at Gartner)  did a recap of 2023 Google Cloud Next's biggest announcements, how (and why) data teams are adopting GenerativeAI, and gave examples of futuristic consumer experiences such as interacting with AI-generated social media influencers. John Kutay moderated the panel in front of a live audience.Follow our panelists on LinkedIn for more insights!https://www.linkedin.com/in/brunoaziza/https://www.linkedin.com/in/ridhimaah...https://www.linkedin.com/in/sanjmo/What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

How is your data team driving revenue and reducing costs? How is the impact of your data team being measured? All of these business value topics and more will be discussed with Ethan Aaron, CEO of Portable.

How is your data team driving revenue and reducing costs? How is the impact of your data team being measured? All of these business value topics and more will be discussed with Ethan Aaron, CEO of Portable.

Summary Building a data team is hard in any circumstance, but at a startup it can be even more challenging. The requirements are fluid, you probably don't have a lot of existing data talent to manage the hiring and onboarding, and there is a need to move fast. Ghalib Suleiman has been on both sides of this equation and joins the show to share his hard-won wisdom about how to start and grow a data team in the early days of company growth. 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 Ghalib Suleiman about challenges and strategies for building data teams in a startup Interview Introduction How did you get involved in the area of data management? Can you start by sharing your conception of the responsibilities of a data team? What are some of the common fallacies that organizations fall prey to in their first efforts at building data capabilities? Have you found it more practical to hire outside talent to build out the first data systems, or grow that talent internally? What are some of the resources you have found most helpful in training/educating the early creators and consumers of data assets? When there is no internal data talent to assist with hiring, what are some of the problems that manifest in the hiring process? What are the concepts that the new hire needs to know? How much does the hiring manager/interviewer need to know about those concepts to evaluate skill? What are the most critical skills for a first hire to have to start generating valuable output? As a solo data person, what are the uphill battles that they need to be prepared for in the organization? What are the rabbit holes that they should beware of? What are some of the tactical What are the most interesting, innovative, or unexpected ways that you have seen initial data hires tackle startup challenges? What are the most interesting, unexpected, or challenging lessons that you have learned while working on starting and growing data teams? When is it more practical to outsource the data work? Contact Info LinkedIn (https://www.linkedin.com/in/ghalibs/) @ghalib (https://twitter.com/ghalib) 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 Polytomic (https://www.polytomic.com/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)