What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry?
In this episode of the Data Engineering Central Podcast, I sit down with Dave Langer to explore nearly three decades of experience across software engineering, business intelligence, analytics, data science, and AI.
Dave’s career spans COBOL programming on IBM mainframes, enterprise architecture, Microsoft’s Xbox division, machine learning, startup leadership, authorship, and building one of the largest personal brands in the data space.
We discuss why many of the biggest problems in data haven’t changed, even as the tools continue to evolve. We dive into the reality behind self-service analytics, the importance of dimensional modeling, what organizations are getting wrong about AI adoption, and why developing strong analytical skills matters more than ever.
Dave also shares practical advice for data professionals navigating the AI era, explaining why tools like Copilot should be viewed as partners rather than replacements.
If you’re a data analyst, BI developer, data scientist, or data engineer wondering what the future holds, this conversation offers both perspective and optimism.
What We Cover
How Dave got started programming on a Commodore 64
The transition from COBOL to modern analytics
Why the core problems in data haven’t changed
The evolution of business intelligence and dashboards
How Dave discovered machine learning
Why data science needs more than just Jupyter notebooks
The limitations of self-service analytics
Why semantic layers and governance matter for AI
Advice for staying relevant as AI reshapes the industry
Building a personal brand in data
Writing a technical book and becoming an independent creator
Connect with Dave:
LinkedIn: https://www.linkedin.com/in/davelanger/
Substack: The DIY Data Scientist
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