We constantly adjust the way we communicate based on the context. In our house versus the office. To a friend versus a potential customer. We’re always framing our language and tone based on where we are and who’s listening.

Natural language processing (NLP) is no different. Only when your domains and models match the work your organization is trying to get done—or the problems you’re solving for your customers—can your NLP journey truly take off.

When your organization’s data is changing constantly, you can’t afford to work with it manually. That means: No historical batch analysis on structured data that’s already out-of-date by the time it’s processed. No running the same query or looking at the same dashboard repeatedly throughout the day, hoping for a different answer. No data entry Read the full article…

Collaborative knowledge graphs are an enormous step forward for knowledge workers operating as “knowledge graphs of one,” wrestling with SQL queries in an endless struggle to make sense of your organization’s data lake, or finding themselves stuck in repetitive cycles of asking technical staff to build new infrastructure to support yet another data format and Read the full article…