Turning data into knowledge is the challenge

Alex Bennet, co-founder of the Mountain Quest Institute in Frost, W.Va., is a recognized expert in knowledge management. She was chief knowledge officer and deputy CIO for the Navy Department, and led the Federal Knowledge Management Working Group. Bennet is the recipient of the Distinguished Public Service Award, the Presidential Hammer Award, and the National Knowledge and Intellectual Property Management Award for distinguished service and exemplary leadership. She has written extensively on KM and is co-author of Organizational Survival in the New World: The Intelligent Complex Adaptive System.

GCN: Why has data become so important?

BENNET: Data has always been important. But as we head towards the end of this decade, when the computational power of supercomputers is expected to equal the computational power of the human brain'what [Ray] Kurzweil calls the singularity point'we are focusing less on technology and more on the availability of and access to the data, information and knowledge enabled by technology. Actually, I might just as easily have used a capital 'A' on 'access,' meaning that not only are we interested in being able to get the best data and information, but we are interested in understanding it and knowing when, where and how to best apply it. You will note that the 'knowledge' word has become more and more prevalent as global access and connectivity have increased.

GCN: Has technology matured enough to support data sharing as a fundamental system requirement?

BENNET: Certainly. It has matured enough to support information sharing as a fundamental system requirement, and to provide the metadata and meta-information to ensure that data and information can support knowledge growth in decision-makers. In other words, the progress we are making is finally beginning to focus on the user and useability.

GCN: Does the idea of 'build once/use many' apply to data? In what ways?

BENNET: Not that easy. Data, information and even knowledge are context sensitive. There's no inherent goodness and badness, so to speak. The goodness and badness comes with the context and how it is used in a specific situation. For example, we could say that the terrorists that attacked the World Trade Center on Sept. 11, 2001, used good knowledge management in the sense that they had enough data and information and used it to succeed at some level in what they set out to do. Now, that same data might prove pretty useless for other purposes.

So data, information and knowledge become artifacts, and even noise, perhaps effectively used one or more times but not necessarily important or significant in different situations or as time goes by, which pretty much negates the 'build once/use many' concept.

The good news is that we're beginning to become more aware of patterns. That means that the type of data used for one decision might be of use to future decisions, so we design in live feeds to authoritative data sources. Or the same data itself might be useful across different applications. Or it might mean that the patterns from data we've 'built once' in a system might provide clues for addressing future emergent issues and problems. And, of course, our advances in artificial intelligence technologies helps us find those patterns.

So the 'build once/use many' term really applies to systems and patterns of data, not necessarily to the specific data itself.


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