The Data-Information Hierarchy, Part 2

Data, as has been established is the organic, elemental source quantities. Data, by itself, does not produce cognitive information and decision-making ability. But without it, the chain Data –> Information –> Knowledge –> Understanding –> Wisdom is broken before it starts. Data is recognized for its discrete characteristics.

Information is the logic grouping and presentation of the data. Information, in a more general sense, is the structure and encoded explanation of phenomena. It answers the who, what, when, where questions ( but does not explain these answer, nor instill a wisdom necessary to act on the information. “Information is sometimes associated with the idea of knowledge through its popular use rather than with uncertainty and the resolution of uncertainty.” (An Introduction to Information Theory, John R. Pierce)

The leap from Information through Knowledge and Understanding into Wisdom is the objective sought be data / information analysis, data / information mining, and knowledge systems.

What knowledge is gleaned from the information; how can we understand the interactions (particularly at a system level); and how is this understanding used to create correct decisions and navigate a course to a desired end point.

Wisdom is the key. What good is the historical volumes of data unless informed decisions result? (a definition of Wisdom)

How do informed decisions (Wisdom) result if the data and process are not Understood?

How do we achieve understanding without the knowledge?

How do we achieve the Knowledge without the Information?

But in a very real sense, Information is not the who, what, when, where answers to the questions assimilating data. Quantified, Information is the measure of the order of the system; or conversely the measure of the lack of disorder, the measure of the entropy of the system.

Taken together, information is composed of the informational context, the informational content, and the informational propositions. Knowledge, then, is the informed assimilation of the information. And the cognitive conclusion is the understanding.

Thus Data –> Information –> Knowledge –> Understanding –> Wisdom through the judicious use of:

  • data acquisition,
  • data retention,
  • data transition,
  • knowledge mining,
  • cognitive processing, and
  • the application of this combined set into a proactive and forward action plan.

Related Posts:

The Data-Information Hierarchy, Part 1

The Data-Information Hierarchy, Part 3

About Mark Reynolds 45 Articles
Master of Engineering professional experienced as engineer, architect, manager, mentor and evangelist. Accomplished Architect demonstrating proactive application of digital technologies and agile methodologies balanced between engineering, development, automation, adaptive processing, and distributed systems. Solution Evangelist with recognized command of processes, distributed systems, analytics, machine learning, and multiple digital technologies. Mentor, Professor and Lifelong Learner teaching internal educational projects, university computer science, industry conferences.