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John literally helped define the concepts of Internet marketing and SEO. A true Internet pioneer, John founded one of the first interactive agencies in 1995. He currently handles Finance & Operations at AudetteMedia. More about John here.

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Frank Zappa wrote:

Information is not knowledge,
Knowledge is not wisdom,
Wisdom is not truth,
Truth is not beauty,
Beauty is not love,
Love is not music,
and Music is THE BEST.

The Data > Information > Knowledge > Wisdom (DIKW) curve, which has been extensively written about by poets, musicians and academics, is more pertinent in an internet environment than ever before. Before the internet (anyone remember that far back?), it was possible to live at the lower end of the DIKW curve and still realize value for data and information. The internet changed all that. It gave everyone a printing press and the ability to gather data and information and to deliver it to millions of people quickly and cheaply.

At which point the Economy 101 principle of supply and demand did its thing and the value of data and information decreased dramatically. A cliché in the early days of the internet is that information wants to be free. And now a lot of it is.

With ubiquitous data and information offered for free by many, and with users growing ever more sophisticated, it’s more challenging to monetize at the lower end of the curve than it used to be. But, as always, challenges create opportunity. And the opportunity in this case is to add value by moving your product or service higher up the DIKW curve. Knowledge is seldom free – and people expect to pay for it. Wisdom is esteemed.

Image originally published in the December 1982 issue of THE FUTURIST by the World Future Society

Here are two examples of how you can benefit from moving higher on the DIKW curve:

Monetizing Content

The further you move your content along the Data -> Information -> Knowledge-> Wisdom continuum, the more valuable it becomes. Data and information have value as they serve visitors (including spiders from search engines). But internet users have come to expect that most data and much information is free and it can be difficult to monetize something that someone else is giving away for free. One of the purest ways to add value on the internet is to move the content to knowledge or, ultimately wisdom. As you do that, you add value and the potential to monetize your content.

Services Company Value-Add

We’re a services company and our fundamental value-add as an agency here at AudetteMedia, in addition to doing the grunt work (there’s a lot of hard, not very glamorous work with internet marketing), is to apply our experience and knowledge to move along the Data -> Information -> Knowledge-> Wisdom continuum.

In 1934 the poet T.S. Eliott wrote the following:

Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

Moving Up The Curve

My view of the steps is as follows:

1. Data: Raw information - starting point.
2. Moving to Information: Compiled data put into context. For example, a database is data - a relational database is information.
3. Moving to Knowledge: You use inductive reasoning to take an aggregate of information and translate it into contextually appropriate action. To do this effectively requires experience.
4. Moving to Wisdom: A more introspective level, answers the questions of why and when.

The experts explain this a lot better than I do. Some of the following is a little academic, but it’s worth spending a few minutes to review it.

Here’s what Wikipedia says about DIKW:

DIKW is the proposed structuring of data, information, knowledge and wisdom in an information hierarchy where each layer adds certain attributes over and above the previous one. Data is the most basic level; Information adds context; Knowledge adds how to use it; and Wisdom adds when to use it.( As such, DIKW is a model that can be useful to understanding analysis and the importance and limits of conceptual works .

The DIKW model assumes the following chain of action:

• Data comes in the form of raw observations and measurements.
• Data is transformed into information as soon as it is imbued with meaning e.g. by analyzing relationships and connections between the data. It is capable of answering simple “who/what/where/when/why” style questions. Information is a message, there is an (implied) audience and a purpose.
• Knowledge is created by using the information for action. Knowledge answers the question “how”. Knowledge is a local practice or relationship that works. In essence, “knowledge derives from information as information derives from data”
• Wisdom is created through use of knowledge, through the communication of knowledge users, and through reflection. Wisdom answers the questions “why” and “when” as they relate to actions. Wisdom deals with the future, as it takes implications and lagged effects into account.

And getting a bit more technical, here is how the DIKW process is described by Russell Ackoff, a systems theorist and professor of organizational change, as quoted from an article titled “Data, Information, Knowledge and Wisdom”.

Data

Data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

Information

Information is data that has been given meaning by way of relational connection. This “meaning” can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge

Knowledge is the appropriate collection of information, such that it’s intent is to be useful. Knowledge is a deterministic process. When someone “memorizes” information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the “times table”. They can tell you that “2 x 2 = 4″ because they have amassed that knowledge (it being included in the times table). But when asked what is “1267 x 300″, they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level… understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding

Understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between “learning” and “memorizing”. People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom

Wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).

The Bottom Line

The old cliché on the internet is that information wants to be free. But knowledge is seldom free – and people expect to pay for it. Knowledge adds value as you move up the Data -> Information -> Knowledge-> Wisdom scale. It works!