Eight years ago my career collided with reality.  By way of serendipity–following an unplanned career change–I discovered I had been living the last decade in a product development bubble. Two thousand and two was the year I had transitioned from building information management systems to managing a small storage industry analyst firm.

Up until 2002 I thought, perhaps arrogantly, that I thoroughly understood information management. After all, I had spent countless hours helping companies of all types implement systems to manage their digital information assets. I had no idea how little I understood until I began to learn more about storage infrastructure. All those years I had worked with other developers to build different types of business applications (e.g. enterprise content management systems and digital classrooms) with little regard for the applications’ impact on storage, mostly because I was not aware of their actual impact on storage. After all, storage was someone else’s problem we reasoned–a “black box” in which we stored our data.  There was no reason for us to truly understand how it all worked as long as we had enough space for our applications and files, right? Our customers could simply install our applications and databases, fire them up and begin collecting, aggregating, managing, manipulating and saving gigabyte upon gigabyte of data to their hearts’ content…or so we thought.

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I have always enjoyed speaking with David West. He’s one of the relatively few people within the storage industry’s sell-side who genuinely seeks to understand information management - the industry from which I leapt into storage.

After I read David’s recent blog post ILM: What’s Old is New Again, in which he wrote about the return of ILM, I responded with the following comment:

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In his recent Wikibon post following a July 28 Peer Incite: Prevent Unstructured Data from Fueling Business Risk, Dave Vallente warns CIOs of what he calls the “data management trap”.  Thankfully, Dave provided an overview for those of us who were unable to participate.

I agree with Dave that “the starting point for an information management strategy should not be the technology implementation”.  However, I would add that a CIO is not the appropriate person for the job.

In response to Dave’s post I wrote:

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Earlier this year I weighed in on the Donatelli/EMC non-compete drama with a brief post about the nature of non-competes.

Since then I have read several new articles and opinions about the topic of non-competes. I am currently following two: Boston.com’s Clause for Concern, and Bijan’s Revised non-compete legislation doesn’t go far enough.

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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

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

Information

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

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:

This discussion should help place things in perspective.]

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.