A world of insight is hidden in the content your teams are generating. Don’t let bad content slow you down: fix it with AI.
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:
- The meta data descriptions are lacking: in order to “match” searches with results’ metadata needs to be specific and differentiated from other forms of content on similar topics. In addition to being too vague or general, metadata often simply doesn’t exist, because creating it isn’t part of the protocol (or was ignored) when new content was added to the network.
- Language across the organization isn’t the same: what one department calls a “report” another may call a “data sheet.” It’s also common for a subject matter expert to have or use technical terminology or jargon while the general user searches in plain English. It’s challenging to index content in a way that ensures access for all.
- The content is not in text form and can’t be scanned, indexed, and searched by traditional search engine tools.
- The content doesn’t exist in the first place! Unless the end user reports every time they come to a dead end in search, there’s no easy way for knowledge managers to identify what users are searching for but not finding.
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.
Our AI-driven pipeline processing can automatically fix your content to make it searchable:
Automatically read or watch content—yes, even video!—and generate titles, descriptions, and metadata. It can also generate data to index specific parts of documents, so they can be matched to a search.
Use natural language processing to generate new titles based on how people typically search for that piece of information, not just how the original author thought about the content.
Use AI to continually provide insights about gaps in content. More than reporting, PreText™ NLP creates an automatic log of content requests so you can prioritize developing new content, adjust rankings for what’s most relevant, and optimize for trends in the search experience.
Today’s AI-powered search tools offer more than information retrieval. Taking advantage of natural language processing to fix their content, our customers are breaking down information silos and equipping everyone in the organization to make better decisions, faster.