Podcasts about data teams

In Episode 9 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Keith Moody, a renowned Data & AI Executive, where they discuss the relationship between the mandate of the CDAO and the results that data and analytics teams continue to deliver, which includes;How the absence of a clear value delivery mandate is the root cause of data being treated as a cost centre rather than a business asset.Why most CDO mandates are designed based on what organisations think the job is, not what it actually needs to be to generate commercial impact.How Keith built four analytics organisations across two companies and delivered over $500 million in incremental annual value.Why the data function's reporting line is rarely neutral, and how a once-thriving value-delivery team was reduced to an order-taking service desk.How convincing leadership that "no analytics could happen until data was perfect" brought an entire organisation to a standstill.Why CDOs who push for commercial accountability in interview processes face a catch-22.Why most interview processes focus on capability over delivery expectations leaving the value mandate undefined from day one.Why you should actively push for commercial targets.Why and how to routinely reframe the mandate once inside an organisation.Why the CFO should be the ultimate validator of any value numbers attributed to analytics.How reporting directly to the CEO removes prioritisation deadlock entirely.Why governance committees are a poor substitute for having a single accountable decision-maker at the top.How change management done at the end of a project is the single biggest reason analytics initiatives fail.Why blanket data literacy programmes are largely an admission of failure.How automating decision-making away from VPs was successfully sold internally.Why the AI investment cycle is repeating the exact same hype and collapse pattern seen with data science.How FOMO, shareholder pressure, and competitive optics drive organisations to invest in AI capabilities they haven't defined a use case for.How the first practical step for any CDO stuck in cost-centre mode is to audit their existing portfolio and how to do so.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

AI adoption looks very different when mistakes can create legal, financial, and reputational risk.Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.Practical Takeaways• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.Timestamped Highlights00:47, Acrisure's shift from insurance brokerage toward fintech and financial tools.01:44, Why regulated industries face a trust gap with AI and need explainable decisions.04:41, How data teams are evolving from dashboards to conversational data enablement.08:28, The build versus buy question and where internal AI tools may still make sense.10:52, Why AI experimentation can get expensive before companies know what works.16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.18:14, Why data governance and data quality need to be treated as day one requirements.One Line That Stuck“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”Subscribe to The Tech Trek for more conversations with technical leaders building, operating, and adapting modern teams around AI, data, platform, product, and engineering execution.

NOTE - Sorry for the edits in this video. I used Descript to edit out the umms and uhhs, and it was a bit too aggressive. Will make it less jarring in future videos. Thanks.Freestyle Friday, May 15, 2026Walking around Salt Lake City and unpacking the April 2026 data modeling survey results (334 respondents). Across three surveys now: January's State of Data Engineering (1,100), March's AI usage poll (193), and April's data modeling deep-dive. Not surprisingly, the same two pain points keep surfacing: time pressure and lack of clear ownership.90% of respondents have a data modeling pain point. When asked what would actually help, only 4.8% wanted better tools. Training, business requirements, time, and ownership crushed tooling in the rankings. Will AI improve things or make them worse? Time will tell...Also covered:Why physical data modeling has become the default (and why that's a problem)Data modeling vs. schema design - they're not the same thingSemantic layers (yay or nay?), Lloyd Tabb, and MalloyConway's Law, Reis's Law, and what changes when org charts get flattened by AIWhy leadership is under more pressure than everThe June half-year survey is coming

Einfach hunderttausend Euro nehmen, ein KI-Tool ins Unternehmen stopfen und alles wird gut? Eher nicht! Heute hat Host Dr. Christian Krug den wunderbaren Jack Lampka zu Gast. Jack ist Keynote Speaker, AI Advisor und ein absoluter Pragmatiker, der Künstliche Intelligenz jenseits des Hypes betrachtet. Wir klären die drängende Frage: Bist du als Unternehmen "cooked", wenn du die letzten Jahre deine Hausaufgaben nicht gemacht hast? Jack und Christian sprechen darüber, warum die grassierende FOMO (Fear Of Missing Out) in den Chefetagen oft fehl am Platz ist und warum Chatbots nicht die magische Lösung für alles sind. Wir decken die 80/20-KI-Lüge auf: Warum das gehypte Generative AI oft nur 20 % des Mehrwerts bringt, während traditionelles Machine Learning seit Jahrzehnten die eigentlichen 80 % liefert. Erfahre, warum Data Teams endlich aus dem dunklen Keller raus ins Business müssen, warum Dashboards wie Witze funktionieren und wie du mit Data Storytelling deine Kolleginnen und Kollegen wirklich abholst. Am Ende des Tages macht immer noch der Mensch den Unterschied – mit oder ohne KI! ▬▬▬▬▬▬ Profile: ▬▬▬▬Zum LinkedIn-Profil von Jack: https://www.linkedin.com/in/jacklampka/Zum LinkedIn-Profil von Christian: https://www.linkedin.com/in/christian-krug/Christians Wonderlink: https://wonderl.ink/@christiankrugUnf*ck Your Data auf Linkedin: https://www.linkedin.com/company/unfck-your-data▬▬▬▬▬▬ Buchempfehlung: ▬▬▬▬Buchempfehlung von Jack: Daniel Kahnemann – Thinking fast and slowAlle Empfehlungen in Melenas Bücherladen: https://gunzenhausen.buchhandlung.de/unfuckyourdata▬▬▬▬▬▬ Hier findest Du Unf*ck Your Data: ▬▬▬▬Zum Podcast auf Spotify: https://open.spotify.com/show/6Ow7ySMbgnir27etMYkpxT?si=dc0fd2b3c6454bfaZum Podcast auf iTunes: https://podcasts.apple.com/de/podcast/unf-ck-your-data/id1673832019Zum Podcast auf Deezer: https://deezer.page.link/FnT5kRSjf2k54iib6Zum Podcast auf Youtube: https://www.youtube.com/@unfckyourdata▬▬▬▬▬▬ Merch: ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬https://unfckyourdata-shop.de/▬▬▬▬▬▬ Kontakt: ▬▬▬▬E-Mail: christian@uyd-podcast.com▬▬▬▬▬▬ Timestamps: ▬▬▬▬▬▬▬▬▬▬▬▬▬00:00 Intro: Muss ich KI jetzt sofort einsetzen oder bin ich erledigt? 02:20 Jack Lampka stellt sich vor: KI jenseits des Hypes und Vendor-Versprechen03:52 Das Excel-Problem und die KI-FOMO in der Chefetage07:11 Vertraue nicht jedem KI-Verkäufer: Warum internes Know-how der Schlüssel ist11:22 Sind Unternehmen ohne Daten-Historie jetzt völlig "cooked"? 15:22 Use Cases: Wie lange dauert ein KI-Projekt wirklich? (Next Best Action & Marketing Mix) 21:06 Warum Tech-Projekte ohne Business-User kläglich scheitern26:19 Data Storytelling: So verkaufst du KI-Lösungen intern richtig30:11 Vibe Coding & GitHub Copilot: Warum in jeder 16. Zeile ein Fehler steckt34:09 KI ist nur ein Verstärker: Am Ende entscheiden die Menschen39:10 Holt die Coder aus dem Keller! Warum Data Teams ins Business müssen45:42 Die Tesla-Regel: Warum dein Dashboard wie ein Witz funktionieren muss47:09 Der 80/20-Irrtum: Warum klassisches Machine Learning wichtiger ist als GenAI50:57 Outro: Deutsche Sprichwörter, Heavy Metal und Buchtipp

Most data teams do not have an AI problem yet. They have an operating model problem.Mike Doll, VP of Data at Guitar Center, joins The Tech Trek to talk about why analytics teams often become reactive ticket factories, and what it takes to turn data into a true business partnership.As companies push harder into AI, automation, and faster decision making, the foundation matters more than ever. If the data team is buried in scattered requests, unclear priorities, and dashboard maintenance, AI will not magically fix the problem. It may only expose it faster.Mike shares how modern data teams can rethink intake, structure analytics partnerships, separate quick BI needs from deeper analytical work, and create a more consultative model that helps the business answer harder questions.Key Takeaways• AI will not fix a broken data operating model. Teams still need clear intake, trusted data, business context, and a better way to prioritize work.• Data teams become ticket factories when every request is treated the same and stakeholders do not understand what happens after they ask for help.• BI and analytics serve different needs. Quick reporting should be fast and reliable, while deeper analytics requires judgment, framing, and business partnership.• Self service only works when the data foundation is strong. Without that foundation, it can create more confusion instead of more speed.• The future of analytics is not just faster answers. It is better questions, stronger context, and data teams that understand how the business actually operates.Timestamped Highlights00:41 Mike explains his role leading Guitar Center's central data organization, including data engineering, analytics, BI, data science, and data strategy.02:09 How data teams become ticket factories, and why unstructured requests can turn analytics into a black box for the business.05:29 Why analytics delivery is different from software delivery, and why data teams need closer alignment with business leaders.07:28 Where self service helps, where it breaks down, and why simple questions need a different model than complex business problems.09:47 Mike explains the consulting model for analytics teams, with dedicated business partners, stronger dialogue, and shared value creation.15:35 How AI is changing quick BI workflows, and why harder analytics questions still require human judgment and problem framing.18:00 How Mike started shifting Guitar Center away from reactive ticket taking by improving intake, visibility, communication, and trust.Line Worth Remembering“The value that analytics teams can bring is answering those hard questions.”Practical MovesFor data leaders trying to move beyond reactive analytics, Mike's advice is to start with the biggest points of friction.That might mean creating a clearer intake process, giving stakeholders visibility into work, assigning dedicated analytics partners to key business areas, or rebuilding trust through fast but meaningful wins.The point is not to add process for the sake of process. The point is to create a data function that can move quickly without losing context, accountability, or connection to business value.Stay ConnectedFollow The Tech Trek for more conversations with technology leaders on data, AI, engineering, platforms, and the operating models behind modern technical teams.

Kenneth Schwartz, VP of Global Data and Governance at Genmab, joins The Tech Trek to talk about what happens when data teams start applying software engineering discipline to modern data work.As AI raises expectations across the business, the challenge is no longer just building more dashboards or models. It is building data products, governance systems, and engineering cultures that can move from experiment to production in a repeatable way.In this episode, Kenneth shares how data teams can reduce sprawl, create stronger stakeholder alignment, shift governance earlier in the process, and use AI agents to accelerate the data roadmap without simply creating more noise.Key Takeaways• Data sprawl often starts with good intentions. Teams want to move fast, but without alignment they can end up solving the same problem in multiple ways.• Software engineering practices are becoming essential in data. Stable interfaces, data contracts, testing, modular design, and clear ownership help data teams scale with fewer downstream breaks.• Governance works better when it is built into the process early. Kenneth explains why governance should not be treated as a cleanup project after the data already exists.• AI can help data teams move faster, but speed alone is not the goal. The bigger opportunity is using automation to improve quality, reduce manual work, and give teams more time to think.• The future of analytics may depend on better foundations. Catalogs, semantic layers, data marketplaces, and governed metrics can make data more usable across BI, apps, chat interfaces, and agents.Timestamped Highlights00:00Kenneth Schwartz joins the show to discuss data engineering, governance, data products, and the growing role of AI in modern data teams.01:17Why data is still catching up to software engineering, and how low barriers to entry have created sprawl across dashboards, models, and experiments.02:55How stakeholder trust, honest conversations, and change management help reduce duplicated work without slowing the business down.05:23The software engineering ideas data teams should borrow, including stable interfaces, data contracts, tests, modularity, and repeatable frameworks.09:21Why infrastructure, data, and security teams need a more unified engineering culture as AI and data use cases become more complex.14:43What it means to shift governance left, and why governance has to become easier for the people expected to follow it.20:35How unstructured data, semantic layers, catalogs, metrics layers, and data marketplaces could change how analytics gets delivered.24:38Why faster delivery should not automatically mean more dashboards, more models, or more work products.Standout Line“More is not always better.”Pro Tips• Do not treat every new data request as a net new build. Look for overlap, reuse, and shared definitions before creating another dashboard or model.• Build trust before trying to reduce sprawl. People are more willing to standardize when they believe the data team is helping them win, not just saying no.• Move governance earlier in the lifecycle. Capture ownership, quality expectations, access needs, and context when data is ingested, not months later.• Use AI to accelerate the hard parts of the roadmap, but keep the focus on better decisions, not just faster output.Call to ActionSubscribe to The Tech Trek for more conversations with technology leaders building the data, AI, and platform foundations behind modern companies. Follow Amir Bormand on LinkedIn for more clips, takeaways, and episode updates.

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. This week on The Data Stack Show, Eric and John welcome Michael Driscoll, Co-Founder and CEO of Rill Data. Mike discusses the transformative impact of AI on business intelligence (BI) and data analytics. He also explores the shift from traditional dashboard-based tools to more dynamic, code-driven, and AI-powered interfaces that provide deeper insights. During the conversation, the group emphasizes the importance of a metrics-first approach, the potential of leapfrog architectures using technologies like data lakes and real-time analytical databases, and how AI agents are increasingly becoming the primary users of data tools. The conversation highlights the evolving landscape of data infrastructure, where open standards, flexibility, and intelligent interfaces are reshaping how businesses interact with and understand their data, and more.  Highlights from this week's conversation include: Welcome Back Mike Driscoll (1:11) Philosophy Behind BI Tools (2:04) Building a Natural Language Processing Server (4:33) Deployment of MCP Servers (6:07) The Role of Visualizations in BI (10:09) Measuring Product Success (12:43) Navigating Changes as Data Professionals (16:13) Efficiency Gains with Code (19:00) The Future of Data Teams (22:29) Long-term Use of Rutter Stack (25:16) Analytics Landscape Overview (30:59) Future of BI Architecture (33:04) AI's Role in Analytics (35:07) Interoperability and Talent Pool (39:41) The Crowded BI Market (42:03) Final Thoughts and Takeaways (46:07) The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Data teams spend enormous energy building pipelines, platforms, and governance frameworks but often skip the most fundamental step: truly understanding what people are actually asking for. In this episode, Juan and Tim sit down with data librarians Jenna Jordan and Amalia Child to explore why library science may be the missing lens for data work. At the heart of the conversation is the reference interview, a structured technique librarians use to uncover a user's "true information need," which almost never matches the first question they ask. From establishing trust and listening without judgment, to asking open-ended questions and verifying whether the need was actually met, the reference interview offers a rigorous, repeatable framework for anyone serving data users. If you've ever wondered why data projects deliver less value than expected, this episode will reframe the problem entirely and give you a practical toolkit to start closing the gap.See omnystudio.com/listener for privacy information.

Data teams obsess over pipelines and platforms but often skip the most fundamental step: truly understanding what people are actually asking for. We chat with data librarians Jenna Jordan and Amalia Child who share a framework for exactly that; it's called the reference interview, and it might be the most practical toolkit data teams have never used.See omnystudio.com/listener for privacy information.

Synthetic data is everywhere in AI conversations!!!! But what does it actually solve? I had an amazing conversation with Michael Eckhoff on The Ravit Show at Gartner he brought this down to reality. We spoke about when synthetic data makes more sense than masking or subsetting production data.It shines when:• Compliance makes moving production data into lower environments a bottleneck• Teams need data that simply does not exist• Rare edge cases are missing from real datasetsSynthetic data lets teams generate fit-for-purpose datasets on demand without copying real customer records across environments.We also tackled the big concern. Is synthetic realistic enough?Realistic does not mean copied. It means the relationships hold. The distributions look right. The system behaves the same way.And you prove it.You compare statistical properties.You validate patterns.You ensure no record is traceable to a real individual.Finally, where does synthetic fit in AI and GenAI?It removes the compliance friction.It helps balance datasets.It enables experimentation without exposing sensitive information.For AI teams trying to move fast and stay compliant, this is a serious lever.#data #ai #gartner #k2view #theravitshow

Cam Crow, Director of Data and Analytics at Vacatia, joins The Tech Trek to unpack what happens when a startup outgrows informal ways of working. This episode looks at how data teams can introduce project management frameworks without killing speed, how to manage stakeholder demand as complexity rises, and why the right operating model matters even more as AI begins to reshape analytics work.Cam shares a practical view from the middle of real growth, from startup scrappiness to acquisitions, migrations, and a much wider stakeholder base. He explains when process becomes necessary, how to build trust during that shift, and where AI is starting to change both delivery workflows and the future of business insights.In this episode• Why early stage teams should add process cautiously, not by default• The moment speed and quality start breaking under too many competing requests• How public communication and domain based stakeholder channels reduce friction• Why planning routines matter as much for stakeholders as they do for the data team• Where AI fits today, from faster delivery to semantic layers that support better answersHighlights00:00 Cam Crowe joins the show to discuss project management frameworks through the lens of data, startup growth, and stakeholder alignment01:58 Why Cam resisted formal sprint planning in the startup phase and why that made sense at the time05:58 The tipping point where too many priorities start hurting both velocity and quality11:49 How moving conversations out of direct messages and into domain channels changed team operations15:03 Inside the two week development cycle and the planning week that keeps stakeholders engaged21:08 How Cam is thinking about AI, semantic layers, and the future of on demand analyticsA standout idea from this conversation, process should be added conservatively, only when the business truly needs it.Practical takeaways• Do not formalize too early, but do not wait until the system is already breaking• Make prioritization visible once demand exceeds capacity• Use shared channels instead of one to one communication to reduce bottlenecks• Build stakeholder rituals into the operating model, not just team rituals• Treat AI readiness as an infrastructure challenge, not just a tooling decisionFollow The Tech Trek for more conversations with operators, builders, and technology leaders shaping how modern teams work and scale.

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. Thank you for being part of the Data Stack community. Stay up to date with the latest episodes at datastackshow.com.This week on The Data Stack Show, John welcomes back repeat-guest Ben Rogojan, Owner and Data Consultant of Seattle Data Guy. John and Ben discuss the evolving relationship between data teams and businesses, highlighting the challenges of proving value in a cost-conscious environment. Ben explores the impact of technological advancements, the rise of AI, and the critical skills data professionals need to succeed. Key insights include the importance of understanding business context, being proactive, and focusing on delivering tangible outcomes rather than just producing dashboards. Ben also emphasizes the need for data teams to communicate value effectively, show rather than tell, and be willing to take calculated risks. The conversation provides practical advice for data professionals looking to advance their careers, with a focus on developing business skills, understanding organizational needs, creating meaningful impact beyond technical expertise, and so much more. Technical Freelancer Academy & Consulting Community (1:21)Evolution of Data Teams and Technology (2:52)Data Team Growth and Output vs. Outcome (4:47)Internal Optimization vs. Client-Facing Data Work (7:23)Audience, Delivery Mechanisms, and Actionability (12:40)Proving ROI and Prioritizing Work (15:27)Practical Tips for Data Team-Business Alignment (18:31)Dealing with Vanity and Security Blanket Metrics (23:39)AI's Impact on Data Workflows (27:07)BI Tools, AI Integration, and Dashboards (32:25)Top Skills for Data Professionals (37:27)Career Growth: Technical, Communication, and Business Skills (42:02)Show, Don't Tell: Prototyping and Feedback (44:37)Taking Initiative and Risk in Data Roles (50:21)Parting Advice and Closing Thoughts (51:16)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this second Tag1 Team Talk episode about the Insights Supply Chain Framework, we examine this structured approach designed to provide a clear strategy for organizing your data teams, eliminating ad hoc collaboration, and ensuring insights flow seamlessly.Discover the fundamental decision every organization must make: whether to keep all data experts in one centralized team or spread them across the business in a decentralized model. Learn how organizational context, not size, is the critical factor for success and why this framework is essential for building robust career paths that attract and retain top data talent.Tune in to explore the framework's core principles and set the stage for our next discussion on the Data Organization Matrix (DOM) and the impact of viewing data as a core competency.Listen now to get the definitive strategy for structuring your data team!

What happens when a team of seven engineers spends a year trying to build a production-ready CDC connector and fails? For Artie CTO and co-founder Robin Tang, it was the spark needed to build a platform that makes data streaming accessible. In this episode, Robin joins Benjamin to discuss the "DFS" (Deep First Search) approach to data sources, the engineering hurdles of real-time Postgres-to-Snowflake pipelines, and why "theoretically correct" architectures often fail in practice.

For years, enterprises have discussed data democratisation as if it were an inevitable end goal. An assumption was made that turning on dashboards and training the business would lead to insight following naturally. But according to Barry McCardel, Co-Founder and CEO of Hex Technologies, the reality has been much more complicated.In the recent episode of the Don't Panic, It's Just Data podcast, McCardel joined host Kevin Petrie, VP Research and Head of Data Management at BARC, to talk about why access alone has never been enough. He also discussed how artificial intelligence (AI) is forcing the analytics community to rethink the purpose of data. The conversation dives into a familiar issue: how can organisations empower non-technical users without compromising data trust or overwhelming the technical teams responsible for it?“We've spent a decade pretending the problem was solved by self-service,” McCardel says. “But what we actually did was move complexity around instead of removing it.”As AI becomes part of analytics platforms, that complexity is finally being addressed. This includes long-standing beliefs about roles, ownership, and teamwork.Addressing the Myth of Data DemocratisationTracing many of the analytics issues faced by organisations in the present day, McCardel alludes to the early self-service BI, which promised that business users could explore data on their own. This was supposed to allow analysts and engineers to focus on more important tasks. In reality, the outcome often included duplicated logic, inconsistent metrics, and a widening trust gap between teams.“Access without context is chaos,” McCardel tells Petrie. “If everyone can answer questions, but everyone answers them differently, you haven't democratized anything; you've just created noise.”This issue has grown more urgent as organisations expand. Different roles—data engineers, analysts, data scientists, and business stakeholders—approach data with distinct goals and skills. Traditional tools forced everyone into the same interfaces, often designed for one group while ignoring the needs of the others.Petrie notes that many companies responded by adding layers of control, but this approach had drawbacks. Stricter guidelines slowed insight generation and pushed business users back into reliance on centralised teams.McCardel argues that the main problem isn't a lack of governance or tools but a lack of shared understanding. “We've treated analytics like a handoff,” he explains. “The data team builds it, the business consumes it. That model doesn't work when questions are fluid, and decisions are continuous.”He believes AI is revealing the limits of that model and providing a path forward.Also Watch: “Data Teams Suffer from Fragmentation” | Charles Schaefer @ Big Data LDN 2025AI is the Bridge, Not the ShortcutWhile much of the industry conversation about AI in analytics focuses on automation and natural language querying, the CEO of Hex is cautious about viewing AI as a quick fix. “If AI just gives you faster wrong answers, that's not progress,” he points out.Instead, he presents AI as...

Data as a Product: Was steckt dahinter?Warum ist AI überall, aber der Weg von der Datenbank zu "Wow, das Modell kann das" wirkt oft wie ein schwarzes Loch? Du loggst brav Events, die Daten landen in irgendwelchen Silos, und trotzdem bleibt die entscheidende Frage offen: Wer sorgt eigentlich dafür, dass aus Rohdaten ein zuverlässiges, verkaufbares Datenprodukt wird.In dieser Episode machen wir genau dort das Licht an. Gemeinsam mit Mario Müller, Director of Data Engineering bei Veeva Systems, schauen wir uns an, was Datenteams wirklich sind, wie "Data as a Product" in der Praxis funktioniert und warum Data Engineering mehr ist als nur ein paar CSVs über FTP zu schubsen. Wir sprechen über Teamstrukturen von der One-Man-Show bis zur cross-functional Squad, über Ownership auf den Daten, Data Governance und darüber, wie du Datenqualität wirklich misst, inklusive Monitoring, Alerts, SQL-Regeln und menschlicher Quality Control.Dazu gibt es eine ordentliche Portion Tech: Spark, AWS S3 als primärer Speicher, Delta Lake, Athena, Glue, Airflow, Push-Pull statt Event-Overkill und die Entscheidung für Batch Processing, obwohl alle Welt nach Streaming ruft.Und natürlich klären wir auch, was passiert, wenn KI an den Daten rumfummelt: Wo AI beim Bootstrapping hilft, warum Production und Scale tricky werden und wieso Verantwortlichkeit beim Commit nicht von einem LLM übernommen wird.Wenn du Datenteams aufbauen willst, Data Products liefern musst oder einfach verstehen willst, wie aus Daten verlässlicher Business-Impact wird, bist du hier genau richtig.Bonus: Batchjobs bekommen heute mal ein kleines Comeback.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Wendy Lynch, PhD, the founder of Analytic Translator, where she helps organizations bridge the gap between data teams and business leaders joins Enterprise Radio.… Read more The post Why Data Teams and Leaders Miscommunicate and How Analytic Translators Fix It appeared first on Top Entrepreneurs Podcast | Enterprise Podcast Network.

Jason Ash, Chief of Data at Symetra, joins the show to unpack how a mid sized insurer is rebuilding its data stack and culture so business and technology actually pull in the same direction. He shares how his team brings actuaries, product leaders, and engineers into one data platform, and why opening that platform to non technical contributors has been a turning point. If you work in a regulated industry and are trying to move faster with data, this conversation gives you a very practical view of what it takes.Key takeaways• Business and tech only work when they share context and trustJason has sat in both seats, first as an actuary and now as a data and engineering leader. That dual background helps him translate between risk, regulation, and modern data practices, and it shapes how he frames projects around shared business outcomes rather than tools.• Put data leaders inside business line leadership, not on the outsideSeveral of Jason's managers sit on the leadership teams for Symetra's life, retirement, and group benefits divisions. They hear priorities and constraints at the same time as product and distribution leaders, which lets them frame data as a value add for new products instead of a back office cost.• Treat the warehouse as a shared product and measure contributors, not just tablesSymetra's dbt based warehouse started with about five contributors. Over three years they grew that to more than sixty, and half of those people sit outside the core data team. Business users learn to contribute SQL, documentation, and domain knowledge directly into the repo, which spreads ownership and reduces bottlenecks.• Shift stakeholders away from big bang launches to steady deliveryJason pushes his teams to think like software engineers. Rather than promising a perfect data product on a single date, they deliver an early slice of data, have partners use it right away, collect feedback, and improve every month. That builds trust and avoids the usual disappointment that comes with one big release.• Use maturity as a guide for where to investEarly on, his group picked a few strong champions who were willing to accept slower delivery in exchange for building real infrastructure. Now that the platform and practices are in place, the focus is on scale, reuse, and getting more people to build on the same foundation, including as AI capabilities start to reshape the work.Timestamped highlights00:53 Jason explains what Symetra actually does and how their product mix makes data work more complex than the company size might suggest02:19 From actuary to Chief of Data, and what sitting on both sides of the fence taught him about business and technology expectations08:08 Why mixing data engineers, data scientists, actuaries, and analysts on the same problems leads to stronger solutions than any single discipline alone13:44 How embedding data leaders into each business division's leadership group changed when and how data enters product discussions16:38 The dbt story at Symetra, and how more than sixty people across the company now contribute directly to the shared data warehouse26:22 Moving away from big bang data launches and setting expectations around early value, continuous feedback, and ongoing quality improvements32:06 The tension between safety and speed as AI advances, and what Jason worries about most for established insurers that move too slowlyPractical moves you can steal• Put data leaders on business line leadership teams so they hear priorities and constraints in real time, not after the roadmap is set• Track how many unique people contribute to your data warehouse and make that a visible success metric across the companyStay connectedIf this episode helped you think differently about data leadership in regulated industries, share it with a colleague who owns product, data, or actuarial work.

Join us for a special farewell episode of the Alter Everything Podcast as we celebrate the impactful journey of host Megan Bowers. In this episode, Megan reflects on her career in data analytics, her experiences at Alteryx, and the evolution of the podcast. Discover insights on building a personal brand, the importance of networking in the data industry, and the future of data science and AI. Hear memorable stories from past episodes, expert interviews, and practical advice for data professionals. Panelists: Michael Cusic, Sr. Learning Experience Designer @ Alteryx - @mikecusic, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Alteryx Community Blog ContentEpisode 194: AI and Data PipelinesEpisode 134: Building Trust in AI with FiddlerEpisode 140: Using Alteryx to Understand Climate ChangeAlteryx ACE program 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.

This episode is a re-air of one of our most popular conversations from this year, featuring insights worth revisiting. Thank you for being part of the Data Stack community. Stay up to date with the latest episodes at datastackshow.com.This week on The Data Stack Show, John and Matt dive into the latest trends in AI, discussing the evolution of GPT models, the role of tools in reducing hallucinations, and the ongoing debate between data warehouses and agent-based approaches. They also explore the complexities of risk-taking in data teams, drawing lessons from Nate Silver's book on risk and sharing real-world analogies from cybersecurity, football, and political campaigns. Key takeaways include the importance of balancing innovation with practical risk management, the need for clear recommendations from data professionals, the value of reading fiction to understand human behavior in data, and so much more.Highlights from this week's conversation include:Initial Impressions of GPT-5 (1:41)AI Hallucinations and the Open-Source GPT Model (4:06)Tools and Determinism in AI Agents (6:00)Risks of Tool Reliance in AI (8:05)The Next Big Data Fight: Warehouses vs. Agents (10:21)Real-Time Data Processing Limitations (12:56)Risk in Data and AI: Book Recommendation (17:08)Measurable vs. Perceived Risk in Business (20:10)Security Trade-Offs and Organizational Impact (22:31)The Quest for Certainty and Wicked Learning Environments (27:37)Poker, Process, and Data Team Longevity (29:11)Support Roles and Limits of Data Teams (32:56)Final Thoughts and Takeaways (34:20)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O'Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup.The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.Gable: https://www.gable.ai/

In Episode 47, of Season 5 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Mike Leverington, Senior Director of Data & Analytics at Skyscanner, where they discuss the evolving landscape of data analytics in the era of AI, which includes;How AI and GenAI are transforming not only the way we work but the way we think, impacting creativity and originality across the workforce.Why the decline of junior and entry-level data roles is reshaping the traditional career ladder in data.The societal and ethical implications of AI-driven workforce changes.How the automation of foundational work could affect how the next generation of data professionals learn and develop.How leaders can strike the right balance between “future-fitting” now and managing the risks of waiting.The implications of AI becoming a key trainer of the workforce, and what that means for learning, upskilling, and talent development.The potential risks smaller businesses face in the AI race against well-funded competitors.Whether the industry is over-talking “data value” and what that means for real business impact.Why a persistent gap remains between how data teams define value and what executives actually care about.The lessons from the “bold claims” era when inflated ROI promises were needed to secure investment.How organisations can hedge their bets in a rapidly shifting AI landscape.What an “AI-ready culture” truly looks like and why it matters for long-term...

This episode is a re-air of one of our most popular conversations from this year, featuring insights worth revisiting. Thank you for being part of the Data Stack community. Stay up to date with the latest episodes at datastackshow.com.This week on The Data Stack Show, Eric and John explore the transformative impact of artificial intelligence (AI) on technology and business. They discuss AI's rapid advancements, drawing parallels to historical shifts like e-commerce. The conversation explores the future of roles within companies, particularly in data management and SaaS products, and considers the broader implications for business operations. They also touch on the changing landscape of data roles, the accessibility of AI-driven services, the potential for AI to democratize high-value services and reshape industries, and more. 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Are dashboards dead? For complex enterprise use cases, the answer might be yes. In this episode, I'm joined by Irina Malkova (VP Data & AI at Salesforce), to discuss her team's transformational journey from building complex dashboards to deploying AI-powered conversational agents.We dive deep into how this shift is not just a change in tooling, but a fundamental change in how users access insights and how data teams measure their impact.Join us as we cover:The Shift from Dashboards to Agents: We discuss why dashboards can create a high cognitive load and fail users in complex scenarios , and how conversational agents in the flow of work (like Slack) provide targeted, actionable insights and boost adoption.What is Product Telemetry?: Irina explains how telemetry is evolving from a simple engineering observability use case to a critical data source for AI, machine learning, and recommendation systems.Why Standard RAG Fails in the Enterprise: Irina shares why typical RAG approaches break down on dense, entity-rich corporate data (like Salesforce's help docs) where semantic similarity isn't enough, leading to the rise of Graph RAG.The New, Measurable ROI of Data: How moving from BI to agents allows data teams to precisely measure impact, track downstream actions, and finally have a concrete answer to the ROI question that was previously impossible to justify.Data Teams as Enterprise Leaders: Why data teams are uniquely positioned to lead AI transformation, as they hold the enterprise "ontology" and have experience building products under uncertainty.

As AI adoption accelerates in financial services, governance leaders face the challenge of keeping oversight processes aligned with innovation. OneTrust provides data governance and risk management tools, serving over 14,000 customers worldwide across privacy, security, third-party, and AI domains. Traditional data governance models, often designed for structured databases, are not built to manage today's dynamic, unstructured AI workflows. Without clear frameworks, organizations risk compliance gaps, inconsistent oversight, and even internal conflict over ownership of AI decision-making. In this episode, Shane Wiggins, Director of Product at OneTrust, explores how enterprises can build governance models that not only mitigate risk but also support innovation. He explains why visibility into unstructured data is essential, how to align compliance and product teams through self-service governance, and the importance of making governance “invisible to developers but visible to auditors.” Wiggins also details best practices for staying ahead of regulatory change, from embedding flexible policies to creating system cards that clarify model purpose and limitations for clients. The discussion highlights how governance, when done right, can accelerate time to market, reduce compliance costs, and even create a competitive advantage. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show! This episode is sponsored by OneTrust. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

This week on The Data Stack Show, John and Matt dive into the latest trends in AI, discussing the evolution of GPT models, the role of tools in reducing hallucinations, and the ongoing debate between data warehouses and agent-based approaches. They also explore the complexities of risk-taking in data teams, drawing lessons from Nate Silver's book on risk and sharing real-world analogies from cybersecurity, football, and political campaigns. Key takeaways include the importance of balancing innovation with practical risk management, the need for clear recommendations from data professionals, the value of reading fiction to understand human behavior in data, and so much more. Highlights from this week's conversation include:Initial Impressions of GPT-5 (1:41)AI Hallucinations and the Open-Source GPT Model (4:06)Tools and Determinism in AI Agents (6:00)Risks of Tool Reliance in AI (8:05)The Next Big Data Fight: Warehouses vs. Agents (10:21)Real-Time Data Processing Limitations (12:56)Risk in Data and AI: Book Recommendation (17:08)Measurable vs. Perceived Risk in Business (20:10)Security Trade-Offs and Organizational Impact (22:31)The Quest for Certainty and Wicked Learning Environments (27:37)Poker, Process, and Data Team Longevity (29:11)Support Roles and Limits of Data Teams (32:56)Final Thoughts and Takeaways (34:20) The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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.

Transforming one's business with AI while maintaining that human element is the balance everyone's struggling with. But what if the answer's hidden in plain sight? In this episode of Bringing Data and AI to Life, host and VP of Data Governance and Data Integration Solution Sales at Informatica, Amy Horowitz sits down with Alanna Val Hackett, Manager of Data Solutions at TripleLift, to explore the evolution of modern data teams, effective data-driven product management, and practical AI implementation strategies. Tune in for valuable insights on how to balance automation with human expertise, develop junior talent effectively, and create a documentation culture that empowers self-service analytics. The only data teams that will win are the ones who keep ethical considerations at the forefront.

This week on The Data Stack Show, John and Matt bring you another edition of the Cynical Data Guy. John and Matt dive into the quirky world of data analytics, exploring common challenges like unrealistic data requests, the limitations of self-service BI, and the evolving role of data analysts. They also discuss the importance of understanding business context, the need for effective data storytelling, and the emerging trend of "BI as code" which promises more flexible and version-controlled analytics tools. The conversation highlights the gap between technical data capabilities and business user needs, emphasizing that the real value of data professionals lies not just in tool proficiency, but in their ability to provide meaningful insights and guide decision-making. Key takeaways include the importance of context in data analysis, the limitations of self-service tools, the ongoing evolution of data roles in modern organizations, and more. Highlights from this week's conversation include:Reading and Reacting to the LinkedIn Data Request Post (1:36)Changing KPIs and Data Skepticism (2:21)The Burden of Proving Data Integrity (5:00)Handling Metric Changes and Historical Comparisons (7:16)Preparing Stakeholders for New Metrics (9:16)BI Code, Version Control, and Modern Dashboards (11:20)Scoping and Business Context in Data Roles (14:38)Technical vs. Business Understanding in Data Teams (16:29)GUI vs. Code in Dashboard Customization (20:41)The Analyst's Role: Guidance Over Tools (23:23)Hiring and the Real-World Analyst Skillset (28:11)Final Thoughts and Takeaways (30:36)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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.

Shane Murray (Field CTO Monte Carlo, Former Head of Data NY Times) joins me to chat about the impact of AI on data teams and business strategies, data observability on unstructured data, and more.

What if your data science team could drive business outcomes across products, not just models? In this episode, Hicham El-Hassani shares a tested blueprint for building data teams that are adaptable, retention-proof, and ready to ship.With 18 years of experience, Hicham has led high-impact data science orgs across insurance and software—and he's not afraid to challenge the standard playbook. He explains why most teams fail to scale, how generalist data scientists can outperform specialists, and what actually matters in model success (hint: it's not just the algorithm).Whether you're a technical leader, hiring manager, or data practitioner, this conversation is packed with insights on how to design for execution, avoid attrition, and get your models into production—fast.Key TakeawaysData science orgs need flexible, crew-style structures—not rigid vertical silosGeneralists thrive when given exposure, ownership, and tailored trainingFeature engineering and domain context often beat algorithm tuningExecution and documentation matter more than flashy toolsGenAI will boost productivity—but won't replace real data science judgmentTimestamped Highlights02:00 — Why rigid, specialized teams backfire in data orgs06:45 — The real value of domain knowledge and how to build it quickly11:50 — How data scientists can shape sales, pricing, and go-to-market strategy17:30 — A four-phase matrix to structure projects and reduce context switching23:00 — How AI tools are already speeding up DS workflows (and what's next)26:00 — One habit that separates scalable teams from forgettable onesQuote of the Episode"Cross-pollination is the best thing—when data scientists are exposed to different business problems, they evolve faster and stay longer."Call to ActionEnjoyed the conversation? Share this episode with someone building or managing a data team. And if you haven't yet, subscribe to The Tech Trek for more no-fluff insights from leaders building the future of tech.

This week on The Data Stack Show, Eric and John welcome back Matt Kelliher-Gibson for another edition of the Cynical Data Guy. The group explores the current state of data engineering and team dynamics while critically examining the evolving landscape of analytics engineering, dissecting the hype around the modern data stack and its tools. The conversation also explores the challenges of data team management, including headcount reductions, rising technology costs, and the struggle to maintain efficiency. Key discussions revolve around the need for open standards, the impact of AI on data roles, the complex hiring practices in tech startups, and so much more.  Highlights from this week's conversation include:The Evolution of Analytics Engineer Roles (1:53)Job Titles and Role Consolidation in Data (3:20)Standardization and Open Data Standards (7:51)SQL as a Universal Standard & Vendor Lock-In (11:58)Modern Data Stack: Hype vs. Reality (13:29)The State of Data Teams in 2025 (18:12)Morale and Job Market Realities for Data Professionals (25:17)Bonus Round: Extreme Work Culture Satire (28:41)Honesty in Hiring and Team Building (33:18)Challenges of Building and Leading Data Teams (37:31)Final Thoughts and Takeaways (41:15)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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 episode of the Lights On Data Show, George Firican interviews Ollie Hughes, CEO and co-founder of Count, to explore the challenge every data team faces: showing real Return on Investment (ROI). Ollie shares a clear framework built on four principles that help data teams break free from the 'service trap' and make a measurable business impact. Get ready to dive into strategies for driving operational clarity, problem-solving, optimizing time to decision, and measuring team value effectively. Don't miss this insightful discussion on transforming how data teams add value to an organization.

Highlights from this week's conversation include:Technical Freelancer Academy & Consulting Community (1:21)Evolution of Data Teams and Technology (2:52)Data Team Growth and Output vs. Outcome (4:47)Internal Optimization vs. Client-Facing Data Work (7:23)Audience, Delivery Mechanisms, and Actionability (12:40)Proving ROI and Prioritizing Work (15:27)Practical Tips for Data Team-Business Alignment (18:31)Dealing with Vanity and Security Blanket Metrics (23:39)AI's Impact on Data Workflows (27:07)BI Tools, AI Integration, and Dashboards (32:25)Top Skills for Data Professionals (37:27)Career Growth: Technical, Communication, and Business Skills (42:02)Show, Don't Tell: Prototyping and Feedback (44:37)Taking Initiative and Risk in Data Roles (50:21)Parting Advice and Closing Thoughts (51:16)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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 episode of TAG Data Talk, Dr. Beverly Wright discusses with Akhil Mahajan, Technical Director at Proctor & Gamble:What are some of the core skill of traditional data teams?How is AI modifying the way we work in data?Describe ways to upskill, re-skill, or hire data teams of the future.Akhil Mahajan, Technical Director at Proctor & GambleFollow Akhil Mahajan

This week on The Data Stack Show, Eric and John welcome Michael Driscoll, Co-Founder and CEO of Rill Data. Mike discusses the transformative impact of AI on business intelligence (BI) and data analytics. He also explores the shift from traditional dashboard-based tools to more dynamic, code-driven, and AI-powered interfaces that provide deeper insights. During the conversation, the group emphasizes the importance of a metrics-first approach, the potential of leapfrog architectures using technologies like data lakes and real-time analytical databases, and how AI agents are increasingly becoming the primary users of data tools. The conversation highlights the evolving landscape of data infrastructure, where open standards, flexibility, and intelligent interfaces are reshaping how businesses interact with and understand their data, and more. Highlights from this week's conversation include:Welcome Back Mike Driscoll (1:11)Philosophy Behind BI Tools (2:04)Building a Natural Language Processing Server (4:33)Deployment of MCP Servers (6:07)The Role of Visualizations in BI (10:09)Measuring Product Success (12:43)Navigating Changes as Data Professionals (16:13)Efficiency Gains with Code (19:00)The Future of Data Teams (22:29)Long-term Use of Rutter Stack (25:16)Analytics Landscape Overview (30:59)Future of BI Architecture (33:04)AI's Role in Analytics (35:07)Interoperability and Talent Pool (39:41)The Crowded BI Market (42:03)Final Thoughts and Takeaways (46:07)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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.

Screenshots of dashboards. Dashboards no one uses. Naming conventions we regret. In this cohosted episode, Cecilia and Karen unpack the everyday habits in data that might just make us cringe in 20 years.   Inspired by the Future Data Cringe, https://www.futuredatacringe.com/, where 50 data leaders shared what they think will age badly, this conversation is full of laughs, reflection, and a reminder that we're all learning as we go. 

Ash Smith is a data engineer based in Perth, Australia (the most remote city in the world). We chat about data products, interoperability, and the importance of collaboration and incentive structures within data teams.

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.

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.

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.

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.

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.