Tag: AI

Sneak Peek at My New Book—Time Molecules

This is a sneak peek of my upcoming book, Time Molecules: The BI Side of Process Mining and Systems Thinking—to be published in the May-June 2025 timeframe. I’ve long been fascinated by systems thinking, ever since reading The Fifth Discipline, Peter Senge, when it was first released. The idea that complex systems behave in ways … Continue reading Sneak Peek at My New Book—Time Molecules →

Trophic Cascades of AI

As I mentioned in a previous post, Sample From My Talk - NFA, I will be delivering two sessions at the Data Modeling Zone 2025 (DMZ) in Phoenix. It will be happening from Tuesday, March 4, 2025 through Thursday, March 6, 2025. That post included a preview one of my two sessions, Beyond Ontologies and Taxonomies—focusing on … Continue reading Trophic Cascades of AI →

Embedding Machine Learning Models into Knowledge Graphs

Think about the usual depiction of a network of brain neurons. It’s almost always shown as a sprawling, kind of amorphous web, with no real structure or organization—just a big ball of connected neurons (like the Griswold Christmas lights). But this image misses so much of what makes the brain remarkable. Neurons aren’t just randomly … Continue reading Embedding Machine Learning Models into Knowledge Graphs →

The BI Counterpart to AI Infinite Context

This post maps the ideas in the recent paper Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention (Munkhdalai, T., Faruqui, M., & Gopal, S. 2024) to concepts from my book, Enterprise Intelligence, including the Tuple Correlation Web (TCW), Insight Space Graph (ISG), and cubespace as a semantic layer. In business intelligence (BI) and … Continue reading The BI Counterpart to AI Infinite Context →

Deductive Time Travel – Prolog in the LLM Era – Thanksgiving Special

The content discusses the significance of historical context in learning and decision-making, emphasizing the value of Prolog as a tool to understand complex logical rules over time. It explores how expert knowledge and decision-making evolve, and how modern technology can facilitate the integration of historical insights into artificial intelligence, enabling enriched decision-making today.

Charting The Insight Space of Enterprise Data

This blog advocates investing time in the foundational book "Enterprise Intelligence," emphasizing its painless approach to developing an integrated understanding of business intelligence. It elaborates on the book's themes, including BI's role in transformative AI, the Insight Space Graph, and the Tuple Correlation Web. The book is detailed and offers a 25% discount for readers.

AI Winter? Bad or Good Timing for My Book?

The author recounts the challenges of pitching a product during the dot-com bubble burst, paralleling it with current skepticism towards AI. Despite the doubts, the book "Enterprise Intelligence" argues that AI, though not without flaws, can greatly enhance enterprise analytics. It emphasizes a balanced approach and offers practical knowledge for leveraging AI in business.

Analytics Maturity Levels and “Enterprise Intelligence”

This blog revisits the traditional "analytics maturity model," providing a new perspective from the book "Enterprise Intelligence." The four analytics maturity levels: Descriptive, Diagnostic, Predictive, and Prescriptive, are explained along with their impact in the AI era. The blog emphasizes the importance of understanding these levels and their role in enterprise intelligence.

KPI Status Relationship Graph Revisited with LLMs

Introduction Back in 2006, I developed an ambitious idea that, though ahead of its time, now poses exciting possibilities in our data-driven era. The concept was simple yet potent: to construct a graph mapping various elements (such as database columns and parameters) used in formulas across Key Performance Indicator (KPI) statuses. The aim was to … Continue reading KPI Status Relationship Graph Revisited with LLMs →