Data Day Texas 2026: A High Signal Day for Data + AI

I attended Data Day Texas 2026 today and I’m leaving with that rare combination of energy and clarity. The schedule struck a great balance: strong fundamentals (databases, SQL, measurement) alongside the very real conversations around AI what works, what does not, and what it takes to make it useful in the real world.

Standout Sessions

These were my favorite sessions of the day:

Bill Inmon — “Generative AI and Business Value: Why Corporate Deployment Falls Short”

This felt like a grounded reality check. Instead of focusing on shiny demos, the talk highlighted the gap between “we built a prototype” and “we created repeatable business value.” My biggest takeaway: GenAI work fails when teams skip the hard parts clear goals, trustworthy data, operational ownership, and measurement.

Hannes Mühleisen — “The Joy of SQL - If Properly Implemented”

This was a reminder that SQL is still one of the most powerful tools we have when the system underneath it is engineered well. I loved the focus on fundamentals: performance, correctness, the autocomplete feature, and especially the progress bar it was kind of nostalgic :) It also highlighted the difference between “SQL as a language” and “SQL as an experience” that can be delightful (or painful) depending on implementation details.

Jon Haddad — “Stop Guessing, Start Measuring: A Decade of Database Experimentation and Tuning”

A very practical mindset shift: treat tuning like science. Measure, change one thing, measure again. Over time, the win isn’t just better performance it’s building the habit of being evidence driven, especially when databases (and production workloads) love to surprise you.

Kierra Dotson — “The Engineer's Guide to AI Strategy: Bridging the Gap Between Business and Technical Reality”

This talk connected the dots between what stakeholders want and what engineers can actually deliver. The thread I kept hearing: strategy isn’t slides it’s prioritization, constraints, tradeoffs, and communication. It was especially helpful framing AI work as a product/engineering problem, not just a model problem.

Jean Georges Perrin — “Hands on Data Product: lets build a data product in 30 minutes”

This was a fun “build it now” session that made the idea of a data product feel concrete. The best part was the speed: taking an idea and quickly shaping it into something usable, clear users, clear outcomes, and a path from raw data to something that supports decisions, plus data contracts and standards.

Final Thought

If you’re building data systems and trying to keep your footing while everything changes, this is a high signal event. The talks were strong, but the best part is the way it pushes you back toward fundamentals: measure, communicate, build trust in data, and ship what’s useful.

If you want to check out the schedule, it’s here: Data Day Texas. This year is the finale for Data Day Texas—so sad :(