How can KM and analytics help each other?

I have attended many conferences and workshops in knowledge management (KM) and analytics, respectively, in recent years, and it’s a rarity that the two disciplines co-exist in one conference. Usually, the conference focuses on either knowledge management or analytics, and you hardly find any sessions that bridge the two areas. I feel that it’s time for these two communities to co-locate and synergize, so that the whole becomes greater than the sum of its parts.

In years past, we saw the AI (artificial intelligence) and database communities focus on their own disciplines, but later on, each community saw that they could learn from each other. In the same vein, the knowledge management and e-learning communities still often have separate conferences, but for practical purposes, these two disciplines can learn from each other (in the spirit of knowledge management and knowledge sharing). For example, the e-learning community can advance from learning objects to “embedded knowledge objects,” whereby the KM community can contribute to improving the e-learning state-of-the-art in this area. In fact, I co-edited a book [1] that looks at how best to synergize these areas.

As the analytics field continues to evolve and gain in popularity, let’s not forget about how best to leverage knowledge management to enhance the current state of analytics and vice versa. Here are some ideas for accomplishing this goal.

First, the KM community has been applying social network analysis over the past 10 years, borrowed from the sociology and education disciplines, to often map knowledge flows and gaps in organizations. Social network analysis (SNA) is based on graph theory and link analysis, which ultimately is part of the analytics toolkit. In order to advance SNA to “value network analysis” – that is, how are the knowledge flows or gaps affecting the “bottom line” or strategic goals/objectives of the organization – we may be able to use advanced analytics to help provide some of this value network analysis.

Second, when we look at “knowledge repositories,” we could be using more advanced text or even data mining techniques, borrowed from the analytics field, to look for hidden patterns and relationships.

Third, to apply KM to analytics, we could develop rules and heuristics based on experiential learning that may guide which analytics approach to use in a given situation or even be triggered as an analytics solution is being sought.

Last, KM can also be used to create “analytics online communities of practice” (CoPs) in order to share ideas, ask questions and increase innovation among the community members.

In my opinion, the new master’s degrees in analytics that are being created, as well as those degrees in knowledge management, should each include a course that deals with the other field. That is, the analytics program should include a KM course, and the KM program should include at least one analytics course. In this manner, we can better educate future scholars and practitioners on the co-relevance of each area.

You probably have other ideas that you would like to share. If so, feel free to include them as a post to this editorial or email them to me at [email protected]. I look forward to your thoughts.

REFERENCES

1. Liebowitz, Jay, 2010, “Knowledge Management and E-Learning,” Taylor & Francis.