Connecting Business Intelligence to Artificial Intelligence
News Roundup - September 2025
Can we really automate wisdom? Of course not, but just asking that question forces you to think about the relationship between data, information, knowledge, and human insight. That progression, known as the DIKW pyramid, helps you see new connections between the data solutions we build and the decisions people use them for.
In my book, Fricitonless Data, I refer to this progression as “thought architecture.” I’ve discovered that I’m not the only one thinking about data management in this way. Malcolm Hawker, a leading influencer on data topics, explains that there’s a need to find the “connecting tissue” between master data management and knowledge management.
He’s not alone. Writers like Jessica Talisman, Russel Ackoff, and Shweta Shah explore this connection with deep experience in artificial intelligence and excellent writing. 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.
These articles suggest a deeper, more thoughtful approach than most, rooted in understanding fundamental concepts like knowledge graphs and library science, rather than the expensive, short-term solutions I see data teams chasing. They expose parallels between knowledge management in the AI world and corporate decision-making.
Give them a read!
“Why AI Isn't Autonomous (Yet)” by Jessica Talisman, Intentional Arrangement, June 2025
Talisman writes about “ontology,” a word that (for AI thinkers) describes the organization of knowledge. She convincingly argues that artificial intelligence can’t advance without human-designed knowledge structures. It’s a brilliant perspective, and I think it exposes a massive gap in the way most of us understand AI. Her articles pinpoint the limitations of AI in replacing human thought.
“From Data to Wisdom” by Russell Ackoff, 1989
This foundational essay explains the relationship between data, knowledge, and wisdom. Very short, profound, clear, and a must-read for theorists. You’ll find this paper cited in many places; this link is the most concise version I’ve found.
“The Discipline of Organizing,” by Robert Glushko, UC Berkeley, 2013
I’ve just started this massive volume, and I’m already convinced that it explains the most important skills needed for success in managing data and AI solutions. Jessica Talisman recommended this to me as the starting point for understanding ontologies. Good news: this PDF edition is free!
“How Should Christians Prepare for the AI Revolution?” by Jason Newell, Biola Magazine, July 2025
Although his article is aimed at Christians, Jason Newell’s framework will help anyone navigate AI decisions with clarity. He summarizes the perspectives of Biola University’s thought leaders. I know this firsthand - these thinkers help bring thoughtful nuance to your interactions with AI.
“Zen and the Art of the Internet” by Yakov Shkolnikov, July 2025
My friend Yakov Shkolnikov recently shared this insider perspective on LinkedIn. Very few people have as much experience and history in data science as Yakov. He’s one of the most thoughtful, outside-the-box thinkers I know, and he humbly uses his expertise to put your mind at ease about AI. You’ll see more of Yakov’s insights in my upcoming newsletters.
“From Unstructured Content Chaos to LLM-Ready: Your Seven-Pillar CQM Framework” by Dr. Shweta Shah, June 2025
When you’ve worked with structured business data your whole career (like me and probably most of my readers), the mental transition to managing the unstructured data used by LLMs isn’t easy. Dr. Shah writes technical solutions for data quality (something I love!) in unstructured data, without getting deep into the systems themselves. Warning: it’s heavyweight reading!
