Chris Morgan, VP of Data Science at Lincoln Financial Group, joins me to unpack what a real data culture looks like inside a complex, highly regulated business that has policies on the books for decades. We talk about how to turn Gen AI buzz into real value, why governance and quality suddenly matter to everyone, and how to tackle data technical debt without stalling delivery. Chris shares concrete ways he finds champions in the business, balances centralized and federated models, and keeps stakeholders excited about the future while he quietly fixes the messy data foundation underneath it all. Key takeaways Data culture is less about dashboards and more about curiosity, repeatable processes, and raising the analytical watermark across the company, not just in the data team. The teams that will win with Gen AI are the ones that can safely connect proprietary data to these models, which demands strong governance, clear definitions, and shared standards. A blended model works best for scaling data work, where a central function sets guardrails and standards while domain teams stay close to the business and own local decisions. Paying down technical debt works when it is framed in business terms, tied to revenue and risk, and treated as a regular slice of capacity instead of a one time side project. Education is now part of the job for data leaders, from internal road shows on Gen AI to simple stories that explain why foundational data work matters before you can ship shiny tools. Timestamped highlights 00:04 Setting the stage Chris explains his role at Lincoln Financial and how data science supports life and annuity products that can live for decades. 03:33 The Cobb salad story A simple grocery store analogy that makes data standards and shared definitions instantly clear to non technical stakeholders. 06:06 Finding the right champions Why Chris prefers curious partners who will invest time with the data team over senior leaders who just want results without changing behavior. 08:33 Governance as Gen AI fuel How regulatory pressure and the need to trust what goes into models are pushing data governance and quality into the spotlight. 11:11 A practical way to attack data technical debt How Chris decides what to fix first, and why he tries to reserve a steady slice of team time for cleanup so progress is visible and sustainable. 17:44 Managing Gen AI expectations From road shows to constant communication, Chris shares how he keeps enthusiasm high while also being honest about the timeline and effort. One line that sums it up “These generative models are going to become a commodity and what will separate companies is who can take the most advantage of their proprietary data.” Practical playbook Start small with data culture by picking one engaged business partner, one problem, and one outcome you can measure clearly. Reserve a consistent portion of team capacity for technical debt, even if it is only a small percentage at first, and make the tradeoffs visible. Use stories, analogies, and simple rules of the road so stakeholders can understand how data systems work without becoming experts in the tech. Call to action If this conversation helped you think differently about data culture and Gen AI inside your company, follow the show and leave a rating so more engineering and data leaders can find it. To keep the discussion going, connect with me on LinkedIn and share how your team is tackling data culture and technical debt right now.
