One of the speakers at the Co-Dev 2015 conference on Open Innovation highlighted the problem of “Too Much Information” (TMI). Innovative companies who are seeking innovation partners have to sift through immense sets of data in order to find and take advantage of relevant (to their innovation challenge) nuggets of knowledge.
There were numerous software vendors and service providers present who have developed some very powerful search engines that are tuned to innovation and technology scouting. Each of these organizations has a unique set of search techniques, data sources, algorithms and/or human-aided processes for searching smarter, i.e. yielding a higher percentage of higher-relevance “hits” across a larger search space for less investment in time and effort. This is all good stuff, necessary, but not sufficient.
I approach this problem from the other (knowledge consumer) side, i.e. by first creating a knowledge pull that highlights important unknowns that need to be known. These unknowns are embodied in a set of decision patterns for Enterprise Strategy and Product Development. By explicitly framing the innovation challenge as a Decision Breakdown Structure (DBS) based on a proven pattern, you decompose the innovation problem into discrete, loosely coupled “questions that demand answers”.
Each decision “question” is a thirsty beast, demanding knowledge about the stakeholders (end users, potential customers) values, expressed in measurable terms as a criteria pattern. The decision “frame” bounds the problem to be solved and screens out irrelevant criteria and alternatives. The intersection of criteria and potential solutions (alternatives) demand estimates concerning alternatives’ effectiveness (performance) at meeting stakeholder expectations. Knowledge about technology maturity, risks, R&D projects, roadmaps, etc. all find a well-structured home in the Decision Driven® information model.
I see a big open innovation opportunity to work with these service providers to marry their great search engines with decision patterns so that all search results would be rooted (firmly associated with) in the appropriate decision context. What if your technology scouting process was truly Decision Driven®? What if every search result was automatically placed in context of the decision that it informed and made “ready to use” in the decision analysis process? What if irrelevant (to the decision context) data was filtered out?