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Do we understand what Data, Information, and Knowledge are?

“Data is everywhere, but it requires CONTEXT and accessibility to be useful…”

 This compelling statement by Symphony Logic immediately caught my attention. It resonates with my model of “The Intelligence Life Cycle,” whose first axiom, or postulate, is “Data is measured in context”—a notion that I expanded upon with my second axiom, “Information is organized data with a purpose.”

At first glance, it might seem trivial, but currently, there’s significant confusion in the semantics, ontology, and taxonomy of the three terms that form the building blocks of Intelligence.

Data, Information, and Knowledge are often used interchangeably as though they are synonymous, but they’re not. This confusion compromises the quality and analysis of our data.


The Delphi study titled “Knowledge Map of Information Science,” conducted between 2003 and 2005 sought to explore the foundational elements of Information Science. 130 definitions of data, information, and knowledge are documented in this study. The international panel consisted of 57 leading scholars from 16 countries, representing (almost) all the major subfields and essential aspects of the field.

Working with 130 different definitions for terms as vital as DATA, INFORMATION, and KNOWLEDGE seems excessive, and rather than providing clarity, it obscures and leads to confusion.

Therefore, I took it upon myself to find or create simple yet accurate definitions for these pivotal terms using an axiomatic approach, similar to the one used by Euclid in his fundamentals of Geometry.

Axiom 1: Data are measured in context.

Axiom 2: Information is organized data with a purpose.

Axiom 3: Knowledge is the discovery of patterns and their relationships.

Axiom 4: Wisdom is the effective use of knowledge. As Professor Drucker put it, effectiveness is doing the right thing, as opposed to efficiency, which is doing things right.

Fortunately, I did not need to introduce a fifth axiom.




I applied these axioms to develop a model that I call The Intelligence Life Cycle, which has helped me identify the limitations of AI and numerous pitfalls in Big Data models and architectures. I presented my theory about the ILC in July 2023 at Nova Southeastern University in South Florida during a presentation titled “The Intelligence Life Cycle and the Limitations of AI” at the SQL Saturday event.

More recently, I also spoke at USF during DevFest to a select audience about the ILC and the Limitations of AI, and I introduced my other model, “The 4 Pillars of Digital Transformation.” Here, I argued that Data is not the new oil nor the first block of importance; instead, it is a third-level block in a hierarchy of importance, preceded by the Cultural and Procedures and Policies Pillars.

You can learn more about The Intelligence Life Cycle and Limitations of AI in my LinkedIn article.

Frank Quintana

Doctor Quintana has been involved in optimization and math modeling since the 80’s. He is a former IT University professor at the Technological University of America in Florida where he taught Databases, Data Mining and Knowledge Discovery. He worked as a research engineer at the Memorial University of Newfoundland. He also worked as a consultant in the role of a technical team leader and software architect for EDS responsible for critical parts of the SDLC for multi-million dollar projects. He was one of the Software Architects that design and build the "Benefit System for the Veteran Affairs of Canada". In 2004 he started his consulting practice in South and Central Florida.