An Alternative to the DIKW Pyramid

My 2012 post on the Co-Production of Data and Knowledge offered a critique of the #DIKW pyramid. When challenged recently to propose an alternative schema, I drew something quickly on the wall, against a past-present-future timeline. Here is a cleaned-up version.

Data is always given from the past – even if only a fraction of a second into the past.

We use our (accumulated) knowledge (or memory) to convert data into information – telling us what is going on right now. Without prior knowledge, we would be unable to do this. As Dave Snowden puts it, knowledge is the means by which we create information out of data. 

We then use this information to make various kinds of judgement into the future. In his book The Art of Judgment, Vickers identifies three types. We predict what will happen if we do nothing, we work out how to achieve what we want to happen, and we put these into an ethical frame.
 
Intelligence is about the smooth flow towards judgement, as well as effective feedback and learning back into the creation of new knowledge, or the revision/reinforcement of old knowledge.

And finally, wisdom is about maintaining a good balance between all of these elements - respecting data and knowledge without being trapped by them.

What the schema above doesn't show are the feedback and learning loops. Dave Snowden invokes the OODA loop, but a more elaborate schema would include many nested loops - double-loop learning and so on - which would make the diagram a lot more complex.
 
And although the schema roughly indicates the relationship between the various concepts, what it doesn't show is the fuzzy boundary between the concepts. I'm really not interested in discussing the exact criteria by which the content of a document can be classified as data or information or knowledge or whatever.

Update (July 2020): I have posted an alternative schema that identifies Three Types of Data. And for an alternative view of Wisdom, see my post on Reframing (February 2009).

Note: As an alternative to the word data (données), Bernard Stiegler (2019, p7) suggests the Husserlian word retention, and associates its opposite (protention) with desire, expectation, volition, will, etc. Thus lumping data and memory together, as well as lumping together Vickers' three types of judgement.

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Dave Snowden, Sense-making and Path-finding (March 2007)

Bernard Stiegler, The Age of Disruption: Technology and Madness in Computational Capitalism (English translation, Polity Press 2019)

Geoffrey Vickers, The Art of Judgment: A Study of Policy-Making (1965)

Wikipedia: Retention and Protention

Related posts: Wisdom of the Tomato (March 2011), Co-Production of Data and Knowledge (November 2012), Three Types of Data (July 2020)

Co-Production of Data and Knowledge

Following Russell Ackoff, systems thinkers like to equate wisdom with systems thinking. As Nikhil Sharma points out,

"the choice between Information and Knowledge is based on what the particular profession believes to be manageable".

When this is described as a hierarchy, this is essentially a status ranking. Wisdom (which is what I happen to have) is clearly superior to mere knowledge (which is what the rest of you might have, if you're lucky).

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Here's an analogy for the so-called hierarchy of Data, Information, Knowledge and Wisdom (DIKW).

• Data = Flour
• Information = Bread
• Knowledge = A Recipe for Bread-and-Butter Pudding
• Wisdom = Only Eating A Small Portion

Note that Information isn't made solely from Data, Knowledge isn't made solely from Information, and Wisdom isn't made solely from Knowledge. See also my post on the Wisdom of the Tomato.

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That's enough analogies. Let me now explain what I think is wrong with this so-called hierarchy.

Firstly, the term hierarchy seems to imply that there are three similar relationships.

• between Data and Information
• between Information and Knowledge
• and between Knowledge and Wisdom

 as well as implying some logical or chronological sequence

• Data before Information
• Information before Knowledge
• Knowledge before Wisdom

while the pyramid shape implies some quantitative relationships

• Much more data than information
• Much more information than knowledge
• Tiny amounts of wisdom

But my objection to DIKW is not just that it isn't a valid hierarchy or pyramid, but it isn't even a valid schema. It encourages people to regard Data-Information-Knowledge-Wisdom as a fairly rigid classification scheme, and to enter into debates as to whether something counts as information or knowledge. For example, people often argue that something only counts as knowledge if it is in someone's head. I regard these debates as unhelpful and unproductive.

A number of writers attack the hierarchical DIKW schema, and propose alternative ways of configuring the four elements. For example, Dave Snowden correctly notes that knowledge is the means by which we create information out of data. Meanwhile Tom Graves suggests we regard DIKW not as layers but as distinct dimensions in a concept-space.

But merely rearranging DIKW fails to address the most fundamental difficulty of DIKW, which is a naive epistemology that has been discredited since the Enlightenment. You don't simply build knowledge out of data. Knowledge develops through Judgement (Kant), Circular Epistemology and Dialectic (Hegel), Assimilation and Accommodation (Piaget), Conjecture and Refutation (Popper), Proof and Refutation (Lakatos), Languaging and Orientation (Maturana), and/or Mind (Bateson).

These thinkers share two things: firstly the rejection of the Aristotelian idea of one-way traffic from data to knowledge, and secondly an insistence that data must be framed by knowledge. Thus we may validate knowledge by appealing to empirical evidence (data), but we only pick up data in the first place in accordance with our preconceptions and observation practices (knowledge). Meanwhile John Durham Peters suggests that knowledge is not the gathering but the throwing away of information. Marvellous Clouds, p 318

Among other things, this explains why organizations struggle to accommodate (and respond effectively to) weak signals, and why they persistently fail to connect the dots.

And if architects and engineers persist in trying to build information systems and knowledge management systems according to the DIKW schema, they will continue to fall short of supporting organizational intelligence properly.

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For a longer and more thorough critique, see Ivo Velitchkov, Do We Still Worship The Knowledge Pyramid (May 2017)

Many other critiques are available ...

Gene Bellinger, Durval Castro and Anthony Mills, Data, Information, Knowledge, and Wisdom (Systems Thinking Wiki, 2004)

Tom Graves, Rethinking the DIKW Hierarchy (Nov 2012)

Patrick Lambe, From Data With Love (Feb 2010)

Nikhil Sharma, The Origins of the DIKW Hierarchy (23 Feb 2005)

Kathy Sierras, Moving up the wisdom hierarchy (23 April 2006)

Dave Snowden, Sense-making and Path-finding (March 2007)

Gordon Vala-Webb, The DIKW Pyramid Must Die (KM World, Oct 2012) - as reported by V Mary Abraham

David Weinberger, The Problem with the Data-Information-Knowledge-Wisdom Hierarchy (HBR, 2 February 2020)

DIKW Model (KM4dev Wiki)


Related posts: Connecting the Dots (January 2010), Too Much Information (April 2010), Seeing is not observing (November 2012), Big Data and Organizational Intelligence (November 2018), An Alternative to the DIKW Pyramid (February 2020)

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Updated 8 December 2012
More links added 01 March 2020
Also merging in some material originally written in May 2006.