A major barrier to successful enterprise search is ensuring the content is findable in the first place. Across the organization people generate a wide variety of data types—documents, videos and podcasts, presentations, images, email content, etc.—the pool of information grows every day. Unfortunately, the ability to make sure all this data is easy for others to access isn’t the top priority for the content creators most of the time.
For example, imagine that each year your company puts out a thorough annual report with helpful graphs, insights, and quotations. You know there’s one chart you’d love to include in your next team update but can’t remember which year it’s from. Unfortunately, when you go to search, the only thing that comes back are a bunch of documents with the same title: “Annual Report.pdf.” Without more context, the search engine can’t produce the information you’re looking for, even though it is certainly there.
Making content findable is critical to improving the function of the search engine and your end user’s experience. There are four main ways content can remain “hidden” in the data abyss:
Combined, these issues leave tons of institutional knowledge and intellectual property untapped. Moreover, it means customers and team members are spending time unproductively searching, walking away from your site feeling frustrated, or both.
Traditionally, going behind all the content creators to make the content findable by an enterprise search engine requires more manpower than most companies are willing to invest. It’s here that machine learning, like SearchBlox PreText™ Natural Language Processing (NLP), steps in to create a significant and immediate impact in the enterprise search experience.