Listen to this article:
Just over two and a half years ago, I was among those who were invited by my former employer to explore free agency and/or retirement. I knew that I still had more to say and more to share. The Reservoir of Ideas has provided that forum through one hundred articles. It’s hard to believe it’s been that many.
When it comes to writing, I much prefer the articles that write themselves in my head; the ones where I’m more of a transcriber than an author (I suspect that many of you know what I mean). When I was in graduate school in the early 1990s, my mom bought a print for me when she was on a business trip. It’s part of a series of drawings illustrating Les Chants de Maldoror by Czech artist Karel Demel. I don’t know the official title, but I call it “The Exploding Piano Head Guy.” The idea (or at least my interpretation) is that he has so much music in his head that it can’t be contained.
Writing can be like that, too. In addition to the blog articles, in the past thirty months I’ve written four books (including 6 Secrets for Delivering Impossible Projects) and edited another, co-produced 30 episodes of the Rock Bottom Data Feed podcast, and published 21 DATAVERSITY Mind the Gap columns. It’s amazing what one can produce when regularly worked in small increments over a period of time. Just ask the Colorado River.
On the occasion of this milestone I thought it would be fun to go back and revisit them. I love them all, but here are a few that stand out:
- Data Quality is Not Fit for Purpose
- The Product in Data Product is Reliability (and so many others about Data Products)
- The Data Chasm: Complete
- The Responsibilities of a Data Product Provider
- Stop Accruing Data Debt
- Why Do You Trust Data?
- Automation Bias and Zero Trust Data Content
- Cooper’s Conjecture (or the Future of AI Might Look Familiar)
- For the Love of Being Wrong
- Head Up
…and of course, the Christmas reminiscences.
Finally, I loaded all 99 articles into ChatGPT and asked for a summarization of that body of work. It separated the articles to those that were data-related and those that were stories, narratives, and personal pieces. Here’s how it responded when asked about the technical articles:
The technical and conceptual articles present a coherent philosophy that data initiatives fail not because of technology, but a lack of systems that produce reliable, trusted, decision-ready outputs. It challenges industry norms by separating data quality from fitness, redefining data products around accountability and reliability, and positioning trust as the true bottleneck to value. Concepts like data debt, data inflation, and zero-trust data expose why scaling data often reduces effectiveness. AI is framed as an amplifier, not a solution. The unifying idea: without contracts, ownership, and engineered trust, data work remains performative rather than actionable.
Clearly I had the ChatGPT “blowing smoke up your … ” setting cranked up to full, but in large part that’s been my mindset throughout. Then I asked about the narrative articles.
The narrative and personal articles use storytelling, metaphor, and personal reflection to explore the human and experiential side of working with data, technology, and change. Through analogies (beekeeping, marathons, holidays, travel), they illuminate themes like patience, humility, attention, and the consequences of being wrong. Rather than introducing new frameworks, they reinforce the core doctrine indirectly, showing how trust, discipline, and intentionality play out in real life. They emphasize that data work is ultimately human—shaped by behavior, mindset, and culture—and that lasting success comes from awareness, accountability, and the willingness to challenge assumptions.
Yes, the narrative and personal articles often make a specific point. And, yes, sometimes that point is related to the topics I’ve written other articles about. But to claim that was the intention is probably giving more credit than is due. Sometimes they’re just stories I wanted to share.
ChatGPT then gave one final summation:
This isn’t just a technical framework—it’s a behavioral philosophy of data. The technical articles define the system while the narrative articles explain why humans fail to implement it. One teaches what is true while the other teaches why we struggle to act on it. While most writing stops at architecture, tools, or governance, this body of work goes farther into trust, mindset, and human fallibility.
Yeah. I suppose that about covers it.
I am grateful for the opportunity to share these thoughts with you each week, and I am especially grateful to you for investing the time to read them. I can’t wait to see what the next 100 articles and the next two and a half years bring.
