"Knowledge management" has been oversold in the last few years, with a lot of grand visions, most of them involving the social re-engineering of organizations. Hastings Research sees KM in a more specific way: putting high-quality, timely information in the right people's hands, when they want it. Then, and only then, do we call it "knowledge."

Our criteria for a successful knowledge management system:

1. Make the system simple enough so people will use it.
2. Use plain English.
3. Rank the information by accuracy, completeness, and usefulness. This is critical to the success of a knowledge management program. In one area a user might want the quick answer; in another they might need the complete answer. And regardless of length, accuracy of information is vital.
4. Balance information "push" and "pull." Some things need to be read by everyone. Most don't. Offer workers too much information, and they start ignoring it. Balance is also vital.

Hastings is well-placed to organize your company knowledge, because we have backgrounds in information sciences, linguistics, and cognitive psychology – as well as the code skills to build a system. This means we can generally produce a better, faster, simpler solution.

For example:
A San Francisco Bay area company recently paid a knowledge management consultant for six months to discover that a) users use different keywords from those doing the cataloging, and b) catalogers, when in the position of searching, will use different keywords from when they are cataloging. Not to discredit the consultant – he was smart to figure this out – but it was a pretty expensive and time-consuming piece of research.

Unecessary, too: this was all written up at the UCLA Information Science department in 1968, and a client calling Hastings Research will have that information in 1 hour.

There has been a tremendous amount of good research done in the last 40 years, not only by UCLA, but by the universities of Indiana; Southhampton and Nottingham (U.K.); Edinburgh (Scotland); and Stanford organizations such as the Intelligence Augmention Lab and the Archimedes Project. We find that mining a billion dollars worth of solid research (all available for free) produces better KM systems than experimenting with bleeding-edge technologies.