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Archive for April, 2004

The Data-Information-Knowledge Continuum

Friday, April 16th, 2004

Yes, another continuum.

Last month I presented the “structured-unstructured information continuum”—a high level explanation of the nature of structure within information assets. In order to simplify the discussion and focus on one specific dimension—its structuredness—I chose to use the word “information” loosely as a surrogate for data, information, and knowledge.

Now, let’s discuss another dimension, value, in the context of a “data-information-knowledge continuum”.

Let’s kick things off with a few definitions. Over the years I have encountered dozens of definitions for “data”, “information”, and “knowledge”. As you might expect, the terms are used differently in the literature of different fields of study. It wasn’t until 1999 that I finally encountered a set of definitions created by Davenport and Prusak that had broad applicability.1 And I’ve been using these definitions as a frame of reference and context for my discussions ever since.


Data is an unprocessed representation of facts, concepts, or instructions in a formal manner suitable for communication, interpretation, or processing by human beings or by computers. In essence, data is the essential raw material for the creation of information. Data:

  • is a set of discrete, objective facts about events
  • provides no judgment or interpretation
  • gives no sustainable basis for action
  • cannot tell you what to do
  • says nothing about its own importance or irrelevance


Unlike data, information has meaning. Data becomes information when its creator adds meaning by placing it within some context in order to convey meaning to others. We transform data into information by adding value in various ways:

  • Contextualised: we know for what purpose the data was gathered
  • Categorized: we know the units of analysis of key components of the data
  • Calculated: the data may have been analyzed mathmatically or statistically
  • Corrected: errors have been removed from the data
  • Condensed: the data may have been summarized in a more concise form


Knowledge derives from information as information derives from data. This transformation happens through such actions as:

  • Comparison: how does information about this situation compare to other situations we have known?
  • Consequences: what implications does the information have for decisions and actions?
  • Connections: how does this bit of information relate to others?
  • Conversation: what do others think about this information?

[Note: Why (re)introduce this continuum? For three reasons:

First, regardless of which definitions you choose to follow, the lines between data and information and knowledge are often blurred. One person’s data along the continuum is another’s information. A simple example would be the numbers 3.14159 and 6.626 x 10^-34. To the average reader these two values may lack context and meaning (data). Engineers and scientists should immediately recognize the two values as pi and Planck’s constant (information). The importance and relevance of data, information, and knowledge, can and does vary across individuals, communities, organizations, fields of study and cultures

Second, everywhere you look there seems to be an article about the “value of data”. Raw data does have a replacement cost, but most of the future value of data—as it evolves into information and knowledge—comes from the incremental value-add (and “value-subtract”) of people. In many cases, it is impossible to nail down the value (or change in value) of data or information without first knowing how it will be used. Companies do not have the luxury of another forty years of philosophical debate. They need a simple, practical baseline model for the value of data—one that they can apply today, and adapt over time.

Third, companies need to map value back to the structuredness of their assets in order to drive the right investment decisions.

1Thomas H. Davenport and Prusak Laurence. Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press, 1998

This post was originally published in Data Mobility Group’s first blog, “Perspectives on Storage”, on April 16th, 2004.

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