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Author: 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.

Introduction to the 4 Pillars of Digital Transformation

In the wake of World War I, French Premier Georges Clemenceau advised the French people that “War is too important to be left to the generals”. Paraphrasing his words I would say that “Digital Transformation is too important to be left to the marketing and sales departments”- Why? Because they are infatuated with the client and it is right because it is their main objective and priorities.

While the customer is very important, I will say paramount, I believe the causes of so many pitfalls and failures in the implementation of DT (Digital Transformation) are the obsession of marketing and salespeople on the customer the hyper concentration in the customers disregarding what I believe are the foundation of DT: The Four Pillars of Digital Transformation.

Even before any consideration of the digital part (Software and Hardware) of the DT equation we need to take care of what I call the 4 pillars of Digital Transformation.

  1. Culture
  2. Process and Policies
  3. Data
  4. Security

They exist in a hierarchical cycle so while some overlapping is possible, the same that when you wear your shoes, you first need to put your socks on. In the four pillars, Culture comes first, then Processes, Data and Security.

Following the diagram of The Four Pillars of Digital Transformation:




For marketing and sales, a customer is an external agent, a person that buys the company’s goods (products and services) for the DT practitioner. The concepts should be broader, instead of customer we should think about USERS.

Please, do not read me wrong. The Sales and Marketing people are paramount for the success of your DT but are not the only ones, in my humble opinion. DT is a matter of life and death for your company and if the CEO and all the C-Level are not deeply involved in the DT projects the probability of success is null, zero, nada.

I am using data as a general term because what we call data is often confused with Information and Knowledge, other two important blocks of the ILC, as I explained in my article “Do we know what are Data, Information and Knowledge?” on this website.

In my other model, “The Intelligence Life Cycle” which I used to discover the AI limitations, I explained what Data, Information and Knowledge really are and created a model of the intelligence Life Cycle based on 4 axioms or postulates in the style of the ancient Greek mathematician Euclid’s. I am going to present the ILC and the Limitations of AI at the PMBA Conference in Orlando next year.

Data is not the New Oil as the hyper propaganda instigated by the media and some data scientists in search of fame, support and money claim, and as you can see from the above diagram, occupied a 3rd position in importance.

You can get more details by watching my 4 Pillars of the Digital Transformation at the Virtual BA and PM conference in Dec this year.

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.