Access and Feeds

Enterprise Search: Incorporating Machine Learning and Predictive Algorithms to Gain Relevance

By Dick Weisinger

Search for documents and data within the enterprise has been tough.  A survey by SearchYourCloud found that people would spend between five and 25 minutes everytime they needed to look for a document.

Much of the problem has been that tools for enterprise search have been lacking.  Either businesses didn’t invest in them, or the tools weren’t configured well enough to be useful, or the features of the enterprise search product used were poor.

A survey by AIIM found that a quarter of businesses said that they have not invested in any search tools.  38 percent said that while they had search tools, they made no attempt to optimize or tune them to improve search performance specifically for their environment.  And surprisingly, 8 percent said that they’ve purchased search tools but have either not installed or made the tool available to their users.

Donald Thompson, CTO of Maana, said that “enterprise search has always been hobbled. It’s not their fault, but they’ve been hobbled by the fact that they’re just looking at hits and documents.  Even Splunk, which has a great product, is a hits-in-documents kind of approach…To me, this is entirely unsatisfying.”

But enterprise search is evolving, even if perhaps slowly.

Laurent Simoneau, president and CTO of Coveo, listed for the publication Knowledge Management some of his predictions for 2016:

  • Enterprise search will begin adopting machine learning techniques to make search more intelligent.
  • Enterprise search will be able to provide more real-time and up-to-date data
  • Enterprise search will be able to incorporate prediction and provide recommendations to the user about things that may be important to them based on their search.




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