A New Framework for Knowledge Management

Several weeks ago I wrote two posts on the topic of Knowledge Management (KM):

1. Whatever happened to Knowledge Management
2. Redefining Knowledge Management

This post represents the third in this series and tackles what a new framework for Knowledge Management might look like.

At the highest level, the Framework that I’m proposing for KM acknowledges several new technologies that weren’t readily available back when the notion of KM was first promoted within IT. The Framework also clearly acknowledges that KM is part of a larger ecosystem of related but separate technologies that cooperate to achieve a variety of Knowledge-related goals. Those goals could be characterized in the following manner:

• The capture of insights on multiple levels, e.g. Individual, Organizational and Community. In this context, Community represents a community of practice (probably global in nature) but could also be a market of some sort.
• The ability to both define knowledge expectations as well to discover hidden knowledge.
• Support the assimilation of source information within “levels of context” (e.g. personal, organizational and community).
• The ability to capture and reuse Knowledge Relationships & Learning Paths (I’ll describe these in more detail in a bit). This particular goal goes to the heart of one of the original value propositions behind KM in the old days – the idea that knowledge capital ought to be captured in a fashion that allows it to be reused such that if individual knowledge holders were to leave the organization it wouldn’t suffer a true “Brain Drain.”

I wanted to briefly explain what I meant by “Learning Paths” and “Knowledge Relationships.” Learning Paths are more or less Dynamic Curricula, in other words self-defining paths that traverse specific learning topics in the context of both individual and organizational learning. Let’s say you have access to 1,000 learning resources and choose to build your own learning program using say 20 of them to learn (for the sake of argument) Node.js. The chosen topics and their sequence can become a learning path which could be reused by other individuals or the organization as a whole. A Knowledge Relationship on the other hand is a little more complicated because it can be manifested in more than one way.  A Knowledge Relationship could be as simple as terms combined through metadata, or terms listed within a shared taxonomy or as complex as a SQL query or defined relationships using RDF. Knowledge Relationships is an area where the initial promise of Semantic technology has fallen a bit short of expectations but will likely continue to improve in the coming years

The most immediate realization when considering both the proposed framework and its potential goals is the fact that there isn’t now nor is there likely to be one tool that accomplishes all of what we consider to be part of a larger KM process. Maybe someday this will change, but for the foreseeable future, establishing and taking advantage of KM within an organization will require a mix of tools (many of which are likely already in place). Here is a conceptual view of the proposed framework:

There are several principles associated with this proposed KM Framework:

1. The idea that learning drives knowledge assimilation
2. The idea that analytics drive discovery and that discovery also drives knowledge assimilation
3. The idea that AI or AT can make both Learning & Discovery more effective
4. The Idea that AI or AT can empower Search capabilities in new ways and that Search drives both Analytics and knowledge discovery
5. The idea that knowledge can be layered up from the individual to the community level, thus supporting a variety of collaborative knowledge capture and assimilation capabilities  
6. The idea that underlying all these knowledge layers can reside a defined identity – this can take the shape of shared semantics, business rules and more
7. The idea that the source information (content, databases etc.) can be acted upon simultaneously from several related processes to produce meaningful insights which in turn can be captured and built upon

In many ways, I think this type of a framework is actually preferable to dependence on single class of tools in that it has a certain flexibility more or less built in. As we’ve witnessed over the past decade, there have been not one but many disruptive new technologies which can be applied to Knowledge Management including but not limited to Mobile technology, AI and Big Data. There are likely several more trends waiting on the wings that could enhance or otherwise contribute to KM in the future. This type of framework can easily accommodate any such advances.  

I will write at least one more follow-up post on this theme and in that article will explore what a real-world next generation KM process and architecture might look like within a typical enterprise and how it might be exploited in a variety of real-world scenarios.

Copyright 2017,  Stephen Lahanas