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The DIKW model for knowledge management and data value extraction

The DIKW model or DIKW pyramid is an often used method, with roots in knowledge management, to explain the ways we move from data (the D) to information (I), knowledge (K) and wisdom (W) with a component of actions and decisions.

Simply put, it’s a model to look at various ways of extracting insights and value from all sorts of data: big data, small data, smart data, fast data, slow data, it doesn’t matter. The DIKW model is often depicted as a hierarchical model in the shape of a pyramid and also known as the data-information-knowledge-wisdom hierarchy, among others.

The DIKW model: usage and limitations

As is the case with all models, DIKW has its limits. You’ll notice the DIKW model is quite linear and expresses a logical consequence of steps and stages with information being a contextualized ‘progression’ of data as it get more meaning.

Reality is often a bit different. Knowledge, for instance, is much more than just a next stage of information. Nevertheless, the DIKW model is still used in many forms and shapes to look at the extraction of value and meaning of data and information.

A traditional data-information-knowledge-wisdom pyramid - source Mushon
A traditional data-information-knowledge-wisdom pyramid – source Mushon

One of the main criticisms of the model is that it’s a hierarchical one and misses several crucial aspects of knowledge and the new data and information reality in this age of big data, APIs and ever more unstructured data and ways to capture them and turn them into action, sometimes bypassing the steps in DIKW (think about self-learning systems). However, the essence still stays the same. Just look at what we do with data lakes and turning data through big data analytics into decisions and actions.

If you want to learn all about the DIKW model, there is an excellent paper in the Journal of Information Science, entitled ‘The wisdom hierarchy: representations of the DIKW hierarchy’ (PDF) and written by Jennifer Rowley of the Bangor Business School.

It’s an interesting paper as Jennifer revisits the DIKW hierarchy, a.k.a. ‘data-information-knowledge-wisdom hierarchy’, ‘Knowledge Hierarchy’, ‘Information  Hierarchy’ and, almost done, ‘Knowledge  Pyramid’. Given the many names it received you can imagine the DKIW Pyramid has always been very popular in the broader space of information management – and beyond.

Beyond wisdom: enlightenment

As you can imagine, the DIKW Pyramid – as all models or ways of looking at things in a more or less structured way – has been discussed and looked upon from various angles with some suggesting to ommit wisdom, others debating the exact definitions and the relationships between them and a few to add a dimension of truth and moral sense to it, with the addition of something even higher than wisdom: “enlightenment”.

While it’s very interesting to discuss about things such as truth, right and wrong, enlightenment and so on, that’s not our purpose here. The long history of DIKW and views on it have made it easier to illustrate this article, that is for sure.

We use DIKW as one of several ways to define, illustrate and explain the various forms of data, information etc. in a business, transformation and customer/stakeholder perspective. We have nothing against enlightenment as a step beyond wisdom, usually defined as ‘evaluated understanding’ or ‘knowing why’, which we would then call truly understanding the purpose of information in a context of what people need and want, beyond the more factual knowledge. The enlightened business? Who knows.

In her paper, Jennifer Rowley mapped the DKIW model to different types of information management systems.

  • Data is related with transaction processing systems.
  • Information with, indeed, information management systems.
  • Knowledge with decision support systems.
  • Wisdom with expert systems.

Again, a highly interesting and smart read so check it out.

What matters: actions and decisions in DKIW

What we’re most interested in, is the action part. We’ve talked about ‘actionable data’ and ‘actionable information’ before and Jennifer Rowley refers in her paper to knowledge as being actionable information, based on the work of E.M Awad and H.M. Ghaziri, more specifically their 2004 book Knowledge Management.

Action. Decisions. That’s what we need. Because without action there is little sense in gathering, capturing, understanding, leveraging, storing and even talking about data, information and knowledge. We mean action as in business and customer outcomes, creating value in an informed way. But of course in the bigger picture, action can also simply be learning or anything else.

DIKW through the eyes of IoT company AGT as mentioned on Electronics 360
DIKW through the eyes of IoT company AGT as mentioned on Electronics 360 – a focus on decisions and actions

Fun fact to know: although a 1989 paper from Russel L. Ackoff, entitled ‘From data to wisdom’ is often cited in the context of the DIKW hierarchy, there seems to be a more poetic aspect about it too.

From the poem by T.S. Eliot, The Rock:
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

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