Filtering the knowledge-verse

By Marc Resnick

In previous generations, the limitations on most knowledge workers was access to knowledge.  You could be a brilliant scholar, but still spend hours, days, and months, trying to track down the results of a study or a theory you had heard about.

Now, we have access to a deluge of information at the tips of our fingers, and the challenge is finding the good stuff.  At home, companies like Netflix, Facebook, and Youtube have been innovating in many ways to maximize the value of what you are shown.  Collaborative filtering techniques using things you have already looked at or what your friends have looked up add some precision to their algorithms.  Explicit preference surveys can help too and are frequently used by news sites.

But at work, this is harder.  The information that would help you most at being more productive on this project or that process is harder to pin down and more important to get a very high precision.  Knowledge networks, Intranet collaboration networks, and other models are being experimented with by companies such as IBM, Cisco, and NASA.  I am currently working on a project that is still under the covers but focuses on doing this for independent consultants and researchers to share ideas, results, and best practices.  The promise of these kinds of filtering systems that combine algorithms, insights, and social networking is tremendous.  Hopefully it won’t turn into one of those ideas that is 10 years from really working, and always will be.

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