The Data Culture Podcast

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.