Long ago when scaling database capacity wasn't as easy as upping (or downing) configuration settings through a user-friendly admin Web page (like the "T-shirt size" with Snowflake), highly-skilled DBAs were a luxury at smaller enterprises, and people at work used to go to Happy Hour in droves, a funny thing might happen at pau hana … Continue reading Correlation is a Hint Towards Causation
Category: Enterprise Intelligence (Book)
The Ghost of OLAP Aggregations – Part 1 – Pre-Aggregation
If your entry into the business intelligence (BI) game began after around 2010, it could be difficult to appreciate the value of OLAP cubes (referring mostly to SQL Server Analysis Services MD, ca. 1998-2010), those aggregations, and that MDX query language. In fact, you may have a negative impression of those relics, those "cubes", based … Continue reading The Ghost of OLAP Aggregations – Part 1 – Pre-Aggregation
BI-Extended Enterprise Knowledge Graphs
Enterprise Intelligence and Time Molecules link through the Tuple Correlation Web. Introduction The main takeaway of this blog is an explanation of how my two books, Enterprise Intelligence (June 21, 2024) and the more recently published Time Molecules (June 4, 2025), connect. Time Molecules is a follow-up to my first book, Enterprise Intelligence. It connects … Continue reading BI-Extended Enterprise Knowledge Graphs
Beyond Ontologies: OODA Loop Knowledge Graph Structures
Introduction: Structuring OODA Loop for Real-World Decision Intelligence Decision-making isn’t just about research—it also involves adapting to change faster than the competition, seizing what can be ephemeral openings for opportunity. The OODA loop—Observe, Orient, Decide, Act—is a well-known model for this, originally developed for military strategy but applicable everywhere from business intelligence to AI-driven automation. … Continue reading Beyond Ontologies: OODA Loop Knowledge Graph Structures
Key Takeaways from My Two Upcoming DMZ 2025 Sessions
I'm in the middle of prepping for my two sessions at Data Modeling Zone 2025 (March 4-6, 2025). Both sessions are very tightly packed with still so much more to say. So I thought I'd write a blog on key takeaways for attendees to read prior to the sessions. One of my sessions is essentially … Continue reading Key Takeaways from My Two Upcoming DMZ 2025 Sessions
I’m Speaking at DMZ 2025 -Sample from my Talk – NFA
I’m excited to announce that I will be speaking at the Data Modeling Zone 2025 (DMZ) in Phoenix. It will be happening from Tuesday, March 4, 2025 through Thursday, March 6, 2025. I have two sessions lined up: Enterprise Intelligence Overview (Tuesday, March 4, 2025 9:00am-12:30pm): A three-hour live-version of my book Enterprise Intelligence. This … Continue reading I’m Speaking at DMZ 2025 -Sample from my Talk – NFA
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
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.
Knowledge Graphs vs Prolog – Prolog’s Role in the LLM Era, Part 7
The integration of Prolog with large language models (LLMs) is explored, highlighting Prolog’s unique role in AI architecture alongside knowledge graphs (KGs). Prolog's ability to handle complex logical reasoning and rule-based systems is compared to the capabilities of KGs, emphasizing their complementary roles. KGs provide a scalable and semantically rich organizational framework, while Prolog excels in precise logical processing. Ultimately, their combined strengths enhance the robustness and intelligence of AI systems. Integration with LLMs adds broad, context-driven insights to create versatile AI systems capable of deep reasoning and broad understanding. The potential of combining Prolog, KGs, and LLMs for the future of AI is highlighted, emphasizing the benefits of leveraging their unique strengths.
Levels of Intelligence – Prolog’s Role in the LLM Era, Part 6
This episode of Prolog’s Role in the LLM Era explores parallels between human intelligence and Prolog, applying the concept to biological systems and artificial systems encoded in Prolog. It explores four levels of intelligence—simple, robust, iterative, and decoupled recognition and action—and relates them to biological and artificial recognition and decision-making processes, showcasing the potential power of using Prolog in AI systems.
