There’s one thing that Tom Reamy, chief knowledge architect at KAPS Group, says is a continual refrain among enterprise business users: Search sucks. IT regularly attempts to make things better by buying new search engines and for awhile, everything’s good – until content grows and things start to go downhill again.
Enterprise search, he explained to an audience at this week’s Enterprise Search & Discovery summit, “is never going to be solved by search engine technology” alone. It needs a helping hand from a number of different corners to improve the experience. Good governance and taxonomies can help, for example. But there are challenges in their use, such as the fact that the people who write documents for enterprise repositories can be very creative at avoiding tasks they don’t consider their jobs, such as categorizing documents for others to find during their searches, and even if they’re willing to do it, figuring out what a document is about is a very complex decision.
And, as beautiful a structure as a taxonomy may be to behold, marrying it to millions of documents is itself complex in scale and purpose for both authors and librarians who may have had nothing to do with its creation and so can’t be counted on to apply it well.
Less recognized for the role it can play in rescuing enterprise search is text analytics.