The concept of Screen, time and source modified by ingest analysis and consume results in a capture and reuse infrastructure for the end points. The reality of the back end is something to be considered very carefully. Where with the end point we are deeply concerned in our design with the screen and consumption capabilities for the end user, or SCRaaS (screen as a service) for the back end we actually are as much concerned with the source, its validity and the ingestion.
In considering a system like this you have two distinct technology presentations that you have to consider. The first system like this I was involved in began as a series of communities. People, producing information, sharing information and distributing shared information. It was less effective than it could have been because there wasn’t a true sharing culture (knowledge hoarders) and there wasn’t an effective search creating Dumpster Divers. A dumpster diver is someone that uses the KM system as a mass retrieval system. They search for terms, and then download everything they find. They then search the “dumpster” created to find what they need. That is not a good behavior as they end up with lots of out of date information.
We merged communities with search in our second attempt. It got pretty close but the search technology we had failed in the end. It could not go beyond natural language search to adaptive search. Adaptive search understands that when I say dogleg left, it’s a golf term and don’t present a bunch of pictures of left legs of various dogs. Search engines of today actually create a window presenting initial findings and a “Did you mean this…” line at the very top. Better, but still not fully adaptive enough for a true system of value. Hence the need for SME’s. The SME’s would be a mix of automation and human thinking. The automation being trending the human thinking being the adaptive search terms posted on the home page of the system. From that second attempt at a KM system I came up with the Knowledge Scale shown. Asked and many options returned, asked a few options returned, asked and my question answered. The scale shows the value of a system that adapts via a SME and automation to the information available and the problem being asked.
My third attempt at building a system like this went a different way. We created the SME static information for users to consume. We created communities of interest around the concept sand topics focused on solving problems. We mixed in training and built a considerable training infrastructure that was unique to the problem we were solving. This last system encompassed everything but adaptive search and we got around that by creating the community of experts.
All three systems were ahead of their times. None of them had adaptive search. But they had many parts that encompass what an inter-generational system has to have. First off, inter-generational knowledge transfer is not a new concept. In the last century we moved away from an IGKT system known as the apprentice system. Why? Well it was a focused one on one knowledge transfer system that worked, one on one, or one on a small group. The reality was the move to Universities and away from the apprentice system to create a greater uniformity of professions. If you went to a doctor you were going to a professional adhering to set and known standards. You were not going to someone that spent two years learning at the knee of their Uncle and then started a medical practice. The rise of professions beyond what had been was a cause of the birth of university training and a reduction in the inter-generational knowledge transfer system known as an apprentice system. But what we are talking about now is beyond an apprentice system. We are talking about the creation of a knowledge system that allows for the ingestion, analysis and consumption of data in a manner that benefits the user, the system owner and the subject matter experts.
 “Dumpster Diving” a KM term coined by Bob Forgrave ICE team.