The Top 3 Ways to Implement a Semantic Layer

Over the last decade, we have seen some of the most exciting innovations emerge within the enterprise knowledge and data management spaces. Those innovations with real staying power have proven to drive business outcomes and prioritize intuitive user engagement. Within this list are a semantic layer (for breaking the silos between knowledge and data) and of course, generative AI (a topic that is often top of mind on today’s strategic roadmaps). Both have one thing in common—they are showing promise in addressing the age-old challenge of unlocking business insights from organizational knowledge and data, without the complexities of expensive data, system, and content migrations.   In 2019, Gartner published research emphasizing the end to “a single version of the truth” for data and knowledge management and that by 2026, “active metadata” will power over 50% of BI and analytics tools and solutions to provide a structured and consistent approach to connecting instead of consolidating data.