![]() We use DIKW as one of several ways to define, illustrate and explain the various forms of data, information etc. The long history of DIKW and views on it have made it easier to illustrate this article, that is for sure. 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. Some suggested to omit wisdom, others debated the exact definitions and the relationships between them and a few wanted to add a dimension of truth and moral sense to it, with the addition of something even higher than wisdom: “enlightenment”. Where is the knowledge we have lost in information? Beyond wisdom: enlightenmentĪs 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 ample changes to the precise terms that have been used, sometimes in a specific context. Where is the wisdom we have lost in knowledge? 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. ‘data-information-knowledge-wisdom hierarchy’, ‘Knowledge Hierarchy’, ‘Information Hierarchy’ and, almost done, ‘Knowledge Pyramid’. It’s an interesting paper as Jennifer revisits the DIKW hierarchy, a.k.a. 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. Just look at what we do with data lakes and turning data through big data analytics into decisions and actions. So, there is already wisdom and decisions going on before the capture starts.īut still, the essence stays the same. Some professionals also say it underestimates the value of data (at the bottom of the pyramid) and raw data. ![]() ![]() One of the main criticisms of the model is indeed 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 data and ways to capture them and turn them into action, sometimes bypassing the steps in DIKW ( think about self-learning systems). 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. And solving data management and information management challenges these days looks far more complex with the proliferation of data sources and types. Knowledge is much more than just a next stage of information. 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. As is the case with all models, DIKW has its limits. ![]()
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