Information Technology is a dynamic field, one often driven by buzzwords and fleeting trends. Sometimes these trends continue for decades, other times they fade somewhat quickly. One trend that experienced that fate seems to be Knowledge Management (KM). I recall first hearing about it in the early 2000’s and at that time it seemed to encompass several classes of subordinate technologies including but not limited to the following:
- Document Management
- Content Management
- Search technology (various)
- Business Intelligence (A.K.A. Decision Support or Analytics)
- Metadata Management
- Learning Management
It also tended to include more specialized tools such as knowledgebases or FAQ generators and it potentially seemed to include more integrative content-focused technologies such as Wikis. For a time KM seemed poised to also include a wide range of Semantic technologies as well. However, in the last two years or so in particular, the term Knowledge Management has seemingly dropped off of the map. I was wondering to myself why this may have happened and whether anything in particular had replaced it.
To be sure, there are a couple of related trends that have dominated the IT landscape in the past two or three years; the most significant of those being Data Science & Big Data. However, neither of these seems to fulfill the role KM was being groomed for over the previous decade. In fact, in some ways these more recent trends have become much less specific in regards to their expectations or scope (which has actually become a problem for both of them). This situation may actually help to explain what happened to Knowledge Management - perhaps the original scope was too expansive? But was it just a scope issue?
I want return again to the core or implied premise associated with Knowledge Management, that there ought to be some sort of enterprise-wide ability to help unify all of these knowledge related functions or processes and resources. The problem with this premise is related to the scope, in that there isn’t one product group of technologies associated with it, but rather a fairly large set of technologies – some of them not closely related at all other than in a philosophical sense – in other words that they could be construed as part of a larger knowledge ecosystem. So, we seem to be missing an industry impetus, but we also seem to be lacking any sort of agreed upon knowledge process or framework that would necessarily help to tie all of these diverse technologies together. This latter problem takes us deep into the heart of the larger philosophical question which KM seemed to be begging – e.g., what is the difference between information and knowledge? That isn’t an easy question to answer – and in some sense parallels my recent discussion on Artificial Intelligence versus Artificial Thought. In fact, AI could even be considered as part of KM depending on how you look at it.
So back to the tough question, what makes data or information become knowledge? Is that dependent on adding value to the data or information though specific types of processes or is it merely in the integration and analysis of such source data that the source transcends itself to become knowledge? Or is knowledge only something we can consider in a collective sense; with the sum total of all data assets being knowledge potential of some sort? These types of questions may have been raised during the years that KM was discussed actively but I think never properly answered.
You might have noticed from my initial list of related technologies that I seemed to have left out Data Management or Information Management. Either of those could potentially be considered to be part of a knowledge management framework – however the reason I left them out is that for those fields, there is a much better operational understanding of how those type of technologies work. In fact, the view from with one of these areas, Data Management overlaps quite a bit with some of what I’ve attributed to Knowledge Management (one need only look at the DMBOK to see this illustrated). Thus Data Management as a trend has continued (for decades now), primarily concerned with operational maintenance of a number of unrelated technologies without the implied necessity to integrate it all into something transcendent across the enterprise.
What if we did wish to settle the deeper question though, regarding the differing expectations between operational management of source data and knowledge exploitation? It is now 2017, are we in a position to define an integrative knowledge framework and if so what would the philosophical foundation consist of? Moreover, would coming up with this type of framework help to redeem the dimming trends of Big Data and Data Science? I think it’s worth trying to answer the question and also worth taking a shot at defining the missing knowledge framework. I will tackle both parts of this problem in two upcoming posts…
Copyright 2016, Stephen Lahanas
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