Will machine learning revamp enterprise search software?

Poor enterprise search performance is a common pain point, but advances in machine learning could provide a more effective and intuitive alternative.

As machine learning and artificial intelligence continue to develop, users should be able to rely more on the machine to know what we're looking for and find it, delivering a much broader sense of relevant content than a traditional index search.

These advancements are in the future and easier said than done. But there is a lot of potential to build tools that will help us search across vast data and content repositories, mixing structured and unstructured data sources, finding all kinds of information and presenting in ways beyond the list of results you're likely to find from a traditional search.

I think we'll see more advanced tools with the ability to anticipate the kinds of information we'll need to help make better business decisions. For example, it can provide information to help employees determine what they should be doing on a given day, based on what's going on elsewhere in the business.

I think we're going to see tools where the machine can automatically generate results, based on what the user is working on. The information could perhaps populate onto a split screen, suggesting additional information that could potentially be helpful for the user, and then apply machine learning to the user's response.

This would likely draw on internal data sources -- and external data sources, such as the Web -- to pull together different types of information and present it in charts or graphs so that users can make sense of that information and apply it to what they're trying to do.

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