News Roundup - November 2025
Orlando, Florida, October 2025
To help remind you of this week’s data concept, enjoy Big Time by Peter Gabriel from theFrictionless Data Spotify playlist.
Can a data strategy withstand the test of time? Probably not - if it’s tied to a specific technology. But just asking that question helps you think more broadly about the purpose of your work. There must be some greater goal in managing data than simply chasing the latest AI solution.
I’ve discovered that I’m not the only one thinking this way. Writers like Jessica Talisman and Ramona Truta describe data managers as “knowledge architects” and “context engineers.” That’s a far greater vision—and responsibility—than simply choosing which software to use. These thought leaders (and others) provide practical insight into the knowledge structures behind AI, and listening to them can help you avoid chasing projects that don’t add lasting value.
The articles below suggest a deeper, more thoughtful approach than most, focusing on the unchanging fundamental practices that help you use new technologies as tools rather than strategies. Digesting all this takes a lot of time (which you probably don’t have), so I’ve started condensing and curating the best articles and posts I’ve read on this topic for you.
Give them a read!
The A.I.-Profits Drought and the Lessons of History by John Cassidy, The New Yorker, August 2025
Everyone thinks we’re already seeing the effects of AI everywhere, but the impact of game-changing technologies on jobs and corporate profits usually follows a J-curve. Many of the ultimate winners from the internet (like Google and Facebook) didn’t emerge until after the dot-com bubble had burst. Meanwhile, companies continue to search desperately for AI use cases.
The Original Chatbot by Nicholas Carr, New Cartographies, August 2025
Carr traces the origins of conversational AI back to the mid-1970s. How did he find this? He uncovered it when he wrote The Shallows about 20 years ago, while trying to explain how the internet has changed the way humans think. Carr’s timely and timeless writing sounds like prophecy.
Why You Need Systems Thinking Now by Tima Bansal and Julian Birkinshaw, Harvard Business Review, September 2025
I didn’t realize I was already a system thinker until I read this article. It makes all the difference in how you approach data solutions, shifting you from a service mindset toward an innovation mindset. You define the end state first, frame and reframe the problem, focus on the flows and relationships, and nudge your way forward. And ironically, some writers even critique this HBR article for not considering enough complexity.
Language is Political by Jessica Talisman, Intentional Arrangement, September 2025
After reading articles like this, you start to hear conversations differently. You recognize where decisions about language are not explicit, and you see how that not only confuses people but also decreases trust among them. Talisman isn’t trying to solve the problem; instead, she provides a method for transparently managing the decisions. “The real work of data quality is the work of vocabulary diplomacy,” she wisely says.
The Knowledge Architect’s Playbook: The Cost of Interruptions by Ramona Truta, The Knowledge Architect, September 2025
“We are the first, and most important agents, the ones who must see the problem before we can ever expect a machine to solve it.” I love Truta’s insight here; it reflects the mindset of people who use technology as tools, not substitutes for their own agency. It helps you see how automation for its own sake adds friction to decisions rather than reducing it.
5 Things in Data Engineering That Still Hold True After 10 Years, by Benjamin Rogojan, Seattle Data Guy, August 2025
Now let’s look at the connection between Business Intelligence and AI from the inside out: “Dashboards and queries are still slow,” says Ben Rogojan. If you don’t already know this fundamental insight, you’ve been away from hands-on analytics too long. Let The Seattle Data Guy help bring you back to reality and (hopefully) help you recognize the enduring value of data engineers.
AI-Ready Data: A Technical Assessment. The Fuel and the Friction by Sagar Paul, Modern Data 101, September 2025
Paul ties a successful AI strategy to one of the most fundamental rules of good data management: always fix data at the source. He calls it “inline” data governance, and it’s a critical awareness all CIOs need. He’s not just saying you need a master data management strategy; he’s emphasizing the need to do the process work that links data and maintains lineage back to its source. This point requires much more consideration and learning in corporate IT environments.