Presented by Lulit Tesfaye and Urmi Majumder
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.
By employing semantic components and standards (through metadata, business glossaries, taxonomy/ontology, and graph solutions), a semantic layer arms organizations with a framework to aggregate and connect siloed data/content, explicitly provide business context for data, and serve as the layer for explainable AI.
This session will present case studies that take a deep dive in the technical architecture of a Semantic Layer, exploring the components that enable semantic capabilities, such as metadata management, data catalogs, ontology/knowledge graphs and AI infrastructure. The presentation will emphasize how these components interconnect organizational knowledge and data assets, enhancing systems like recommendation engines and semantic search and explore the top three common approaches we are seeing at play in order to weave this data and knowledge layer into the fabric of enterprise architecture, highlighting the applications and organizational considerations for each.
