The Data Culture Podcast

Culture eats strategy for lunch, and on this episode it eats your AI roadmap too. Sid Atkinson sits down with Mathias Vercauteren to make the case that data is the fourth production factor, AI is the fifth, and most companies are managing both with a fraction of the rigor they apply to money and people. They get into why Amazon runs circles around competitors who sit on far more data, how shadow AI quietly outgrew the old BYOD headache, and what the EU AI Act signals for everyone operating outside Europe. Mathias also lays out where to actually begin on Tuesday morning, and the answer is refreshingly unglamorous: start counting what you've got.

The term "forward deployment engineer" is everywhere right now, Palantir popularized it, Anthropic is leaning into it, and every data and AI platform seems to be packaging it as their answer to the last mile problem. But what is it, really? Michael Wharton, VP of Engineering at Kung Fu AI, joins Sid and Lee to cut through the noise. They dig into where the FDE model works, where it falls short, why process matters more than the individual, and why buying the platform and buying the deployment help are two very distinct purchases. They also get into token maxxing versus quality, the bimodal split happening across engineering teams right now, and why the speedup from agentic coding tools is probably more modest than most leaders think.

As a founder of the Berryville Institute of Machine Learning, Gary McGraw has been researching AI security since before most people knew what machine learning was. He's identified 78 risks across ML systems and was sounding the alarm on recursive pollution and model collapse long before those terms went mainstream. He joins Sid and Lee to break down what practitioners need to understand about the systems they're implementing, why 23 of those risks live in a black box controlled entirely by the foundation model vendors, and what good governance looks like when you can't see inside the thing you're governing.

Julia Bardmesser, CEO of Data4Real and former Chief Data Officer at Voya Financial, brings 25+ years of financial services experience to a conversation about what's actually standing between companies and real AI value. She and Sid dig into why AI layered on top of broken data and workflows delivers little ROI, why the unsexy work of semantic clarity and data management at scale is now more critical than ever, and why the human roles of the future might look less like engineers and more like librarians, lawyers, and judges.

You've heard of UX. You've heard of developer experience. Now meet AX — Agent Experience — and it's about to reshape how enterprises think about their entire tech stack. Lee Harper talks with Vihan Patel of Australia's Mantel Group about what happens when AI agents start bumping up against APIs that were never designed for them, why e-commerce is ground zero for the agentic shift, and the uncomfortable truth that enabling Cursor for your whole dev team doesn't automatically make you faster. They also tackle the governance nightmare of scaling from two agents to twenty, and whether we're heading toward a world where we get paid for decisions, not deliverables.

Most data programs don't fail because of bad technology, they fail because nobody wants to talk about the hard stuff. John Ladley has spent decades proving that behavior change is what separates the programs that stick from the ones that quietly die. In this episode, he's not holding back. From walking away from clients who weren't serious to telling a CIO to admit he didn't know what he was doing, John shares the unfiltered lessons that most consultants won't say out loud. If you work in data and you're tired of watching good ideas go nowhere, this one's for you.

Data governance is often seen as restrictive—but what if it’s actually the key to making data work?Bob Seiner joins Sid Atkinson to challenge how organizations think about governance, accountability, and behavior change. Drawing from decades of experience, Bob explains why governance efforts stall, what shifts when organizations focus on outcomes instead of activity, and how ownership, not tooling, drives real adoption.He also introduces the concept of data fluency, exploring why the ability to communicate with data is becoming just as important as understanding it.

Hosts Sid Atkinson and Lee Harper engage with guests Gary Griffin and David Holcomb, authors of "Building a Data Culture: The Usage and Flow Data Culture Model", to explore the concept of data culture. They challenge the notion that culture is unmanageable, presenting frameworks and models that make culture visible and actionable. The discussion covers the three R's of data culture, the usage and flow model, the significance of microcultures, and the differences between public and private sector dynamics. The conversation culminates in the introduction of the Data Culture Institute, aimed at equipping leaders to build and sustain a data-driven culture.

Data management purity and pragmatism built opposed camps starting in the 1990s, and echoes of those tenants present themselves today in Generative AI. Ontology purists and vector-based engineers appear at opposite ends, but how might we reframe so everyone's in the same picture? Tracy Talbot is a consummate data practitioner and has seen it all—from mainframes to machine learning. In this episode, she sits down with Sid Atkinson to reveal how the age-old Kimball vs. Inman debates mirror today’s AI struggles—and why her concept of “semantic patches” could be the key to keeping generative AI grounded in reality.

Building a data program isn't all about the tech—it's about getting people to actually work together. Carlos Rivero shares what it was really like starting as Virginia's first Chief Data Officer with no team, no budget, and a lot of skeptical agencies. He talks about how he learned to be more of a "people wrangler" than a data expert, turning critics into allies and figuring out that most of the job was just getting everyone on the same page.

Ever wonder why so many promising tech pilots never make it past the starting line? In this episode, Sid chats with Jonathan Alexander about breaking free from “pilot purgatory” and actually scaling innovation. From cavemen pushing carts with square wheels to global rollouts of cutting-edge solutions, Jonathan brings stories, strategy, and wisdom about how to get things done in a giant enterprise. They dive into building business cases that don’t flop, why change management beats the perfect tech stack, and how to avoid getting stuck with great ideas that go nowhere.

Leading complex IT systems in a major city demands more than technical know-how—it requires a shift from individual success to team achievement. Summer Xiao, Deputy CIO of Houston, shares her journey from "I" to "we," discussing the crucial transition from "doer" to "facilitator," the importance of shared ownership in large projects, and how team performance reflects directly on leadership.

State University Systems are frequently impacted by state legislation- from funding to rules, policies, and everything in between. Universities typically operate with streamlined (slim) budgets, busy staff, and with lots of focus on serving students and research. Legislators are similarly busy and concerned with many things across the state, not just the university system. Proposals and draft legislation impact the universities, but lots of content, lots of drafts, and a short legislative cycle make it very time consuming for the university to find all the legislative content that impacts them, and then decide how to best to collaborate. Ryan Jockers and the NDUS team utilized AI to drastically reduce this burden, cutting the time to insight from weeks to minutes. LegiTrack, their system, is just the start!You might hear "NDSU" instead of "NDUS" a few times during the episode. Apologies for the slip of the tongue! We're still talking about the North Dakota University System throughout.

North Dakota is well known for energy and food production, but less known is the states roots in technology. Rep. Josh Christy discusses North Dakota's ambitions in AI, what they mean, and how North Dakota is taking a holistic approach to safety and innovation.

Successful AI projects/ideas have many necessary ingredients, one of the most important being the people you identify and choose to be part of the project. How to you decide and how to you build culture? Ron Green is a successful entrepreneur, listen in as he discusses his most current company (almost seven successful years now!) as he walks us through finding the right folks to add to the team, how they build their culture of discovery and delivery at KungFu, and how they deliver projects to their clients.

The first Federal Chief AI Officer, Oki Mek, runs through three key components in building responsible and secure AI programs, something desperately needed in today's organizations.

How do you get all the cowfolk to giddy-up in the same direction? Smart people are notorious for having opinions; combine intelligence with the independent streak in North Dakota folks and it makes for a lot of work in driving change. Kim Weis has some insightful tips in lessons learned in trying change, failing, learning, and trying again.

Do you want some AI with that? It's everywhere, though thankfully not IN our coffee (yet). So if AI conversations are ubiquitous, how do we sort value from folly? Keatra Nesbitt is a practiced strategy leader and data scientist, listen in as she shares her thoughts on how she navigates these complex conversations with her clients.

Utilizing Gen AI towards your organization's specific needs presents both an opportunity and a challenge. Opportunity to take advantage of massive investments by large tech firms; challenges in that it can be difficult to know what is correct and usable at scale out of these projects. Sabre's Laura Palomino discusses novel approaches they've used towards that have helped her team, and others at Sabre, pursue innovation and change, and be more efficient in testing, and more proactive in resolving potential issues before users find them.

What does it mean to have AI ready data? And once I know that, what do I do about it? Ian Stahl is Director of Product Management @ Informatica and has seen many data centric applications come and go. He provides insights into what's happening in the market today and how we all may work better together to make data highly useable and fit for purpose.

We have come a long way since the publication of "Hidden Technical Debt in Machine Learning Systems" was published almost a decade ago. ML Ops has transformed how data science work is delivered, managed, and monitored. Great? Maybe. In this discussion we cover what is still one of the most glaring gaps in the AI/ML field. Disagreement is accepted and encouraged.

Audio from our LinkedIn Live Event!Organizational structure, team, and culture are critical components to repeatedly and consistently delivering innovation, business results, and absorbing innovative techniques from outside the org walls. AI is rightfully getting the lion’s share of attention, but to make purposed and impactful use, and to have it generate value, many teams across many people and many business units need to align on a baseline operating model. Without this, work efforts, collaboration, and implementations will continue to have marginal gains, if any.And the future will be left to the companies that have or will figure out operating models for innovation and AI.

Engineers are famous for building the amazing, and for wasting time on pet projects, dead ends, and losing track of the customer and real problems. How do you balance creativity and solving real problems? Jodi Blomberg, VP of Data Science at Cox Automotive, has sound, entertaining, and insightful advice!

Digital transformation has been around for a while, but have we succeeded at it? AI, for better and worse, is pushing change in organizations. One of the positives is that AI is creating urgency for organizations to do the things they have deprioritized for a long time: data management, governance, or in this case, digital transformation. Now that this is come back into focus, what are the foundations of effective change?

At the end of a spectacular achievement, the journey there can sometimes seem obvious, but we all know clarity at the start is frequently missing. Nancy Tickle was at the center of Chesterfield County's pioneering transformation in using data to forecast micro-level growth, achieving something no other city or county had done before. In this episode, Nancy offers a detailed account on how Chesterfield achieved remarkable results.

Software is eating the world (or used to, that was soooo 2011). AI is eating the world now at a blistering pace, and while a lot of it feels like hyperbole, and in some cases faked gains (Devin), many initiatives in AI & data are very real, and organizations are adopting and adapting their cultures to include these gains and take advantage of what they enable. Within this astounding pace of change, we have lots of anxiety on learning and adapting. How do I stay current? Is RAG even going to be a thing a year from now?

Want some AI with your coffee? It seems anywhere you turn, AI is infused into every interaction, decision, and experience, so it makes sense that your organization should upgrade (or create) your AO strategy. But how? CDO Peggy Tsai offers incredibly practical advice on AI Strategy for today's Chief Data Officers.

"What's in a name?" Shakespeare's romantic notion that naming something is irrelevant may work in purposes of his plays, but for the data world, naming, and conformance on the meaning of names, is critical. Amit Pahwa has spent a good portion of his career dedicated to making our data lives easier, all by focusing on that most basic of tasks: naming things.

What is most useful for making decisions? Leaders and business owners face decisions almost every moment of the day. We aspire to be an entirely data driven world, so where does this leave 'gut' or intuition? Seyi Fabode helps us explore these questions; be prepared for some philosophical and practical insights.

Mergers and acquisitions have a high failure rate in terms of hitting the envisioned objectives. There are many root causes, but lack of alignment on operating models is definitely on the list. Jeremy Kingry discusses how Argano quickly analyses a target acquiree using data and enforces aligned operating models, using data!

A data strategy is an AI strategy. Subroto has 20+ years in data, innovation, and creating companies; he walks us through several topics that everyone should pay attention to in assembling their AI and data strategy.

We have worked across the globe for a while, but not everyone succeeds at it. Laura of IFC, a division of the World Bank, provides expert advice and guidance on how you might better understand your colleagues across the globe, gain understanding, and succeed together.

Emerging Leaders Series: Where do millennials get their leadership advice? There is a bias in the market to perceive that what big tech and other large progressive companies are publishing and discussing around open, collaborative, and supportive models for growing, supporting, and mentoring the next generation of talent and leaders IS the pervasive model in the market. But there are many orgs across the US and the world, with many different cultures, and many different models in how they grow and support the next generation. Stephanie Rennie shares her experiences in coming to data via the Army to her current position at Sysco.

Collaborating smoothly using data with our colleagues can be a challenge, but what about with external partners? Properly sharing insights and analytics is a challenge even today. Solomon has worked in some of the largest data environments wherein sharing and collaborating with external partners was critical to success, and has some useful insights on collaboration challenge history and what we do about it.

How can you influence change? In the minds of developers, data infrastructure, and those that manage it, can be seen as slow, stodgy, and resistant to moving quickly. Steve Jones, Advocate at Red Gate software, has lived both sides of the fence and offers his thoughts on how we may influence change that lasts.

Product management has always been an interesting intersection in looking at human desires, intents, and behaviors, the value of the system, and how to make everything smoothly interact. Throw in AI and now you have variability in both the inputs and outputs that vastly complicate the product manager's job. Reza Shirazi of Procore discusses successful ways to adapt and adopt the canon of product management methods to AI/ML focused products and their increasingly variable end user interactions.1

Buy versus build and everywhere in between - thinking through what and how you should manage your data estate can be tricky. Storable's Jerry Gregoire walks us through how he and the team break down planning and investments in their data stack so that they are lean, close to business value, and operate smoothly.

The rise in practical uses of Gen AI and AI/ML is underlining an oft forgot and but necessary step in using data: data quality and governing of data quality. DW and analytics practitioners have trumpeted quality for ages, but practical and cost effective methods haven't always been widely known or taught. Rob Hawker thankfully has published a wonderful and practical book, covering real world examples and easily applied methods. Well worth a read! Listen in as he discusses the reasons for writing, and some of the stories behind the story.

Talking about data governance is (typically) not the topic that gets people jumping for joy. Rather, governances invoke "here comes the process monster" type responses. Yet this topic is critical, and considering Gen AI and advanced analytics, becoming more rewarding and, dare say it, fun?!? to undertake. Mathias Vercauteren has been bringing forward an enlightening and engaging way to kick start data governance efforts. In this episode, we explore Mathias’ journey towards creative and empowering workshops and collaboration methods, as well as the bumps along the road.

What is data product management and the associated concepts? How should we frame and think of this so we care successful? Dr. Elena Alikhachkina has been working in this area for a while, before we had recognized naming for data product thinking. She provides some amazing perspectives that will help you and your org adopt successful mindsets and patterns.

Coaching/mentoring has seemed more art than science. Data is everywhere to guide leaders and their teams, but not always accessible. And in today's remote and hybrid world, we have lost much of in the way human observations, one of the largest tools used by leaders to guide the next generation. In this episode, Colum discusses these challenges and how AI/ML can positively impact our ability to be better and more human leaders.

Emerging Leaders Series: We have a new sub-thread to our show wherein we talk with emerging thought, tech, or business leaders in data. Discover how they got to where they are, what's next, and who to watch out for! This first episode features Matt McGuinness of Buckland.

We talk with Eckerson Group's VP of Research Kevin Petrie on the research he is conducting on LLMs and Gen AI, and what he sees coming up that organizations should pay attention to in the coming months.

How do we better frame our data management challenges so IT and business can better align and pursue value? Data4Real CEO Julia Bardmesser walks us through practical advice based on real world data challenges and transformations.

How might we imagine and make a better future? Gen AI and other advances are rapidly impacting how we view, see, and interact with the world, but instead of taking a dystopian view, Jake Hirsch-Allen challenges us all to picture the positive and move accordingly.

There is so much talk on data quality, yet are we actually progressing? Do we have a good definition on data quality and how big or small the problem is? Chad Sanderson walks through his thinking on the topic and how he arrived at his focus on data product thinking as a path forward for the industry.

Listen in as I talk with Malcom Hawker of Profisee as we dive into a Captain Obvious question, but a question that still lacks an appropriate and consistent response: why are we still having to talk about business value? Why do organizations still need prompting to align efforts to the right outcomes? And what can we do about this perpetual wheel we are all on?

Buy or build? What goes into designing, developing, and managing a successful data estate if you choose to build from scratch? Listen to Peloton's Arun Vasudevan as he covers critical components that support amazing outcomes (whether you buy or build!).

Microsoft's Christina Tillbrook walks through her observations on innovation and creating Centers of Excellence to support exploration, collaboration, and growth inside an organization.

Hypergiant's Michael Roberts has spent considerable time in the AI and data field, having collected many enviable experiences and ideas. In this episode, we pick his brain on the current state of the art and what an ex-NASA engineer thinks of current approaches.