Graph data models are becoming a popular approach for data and metadata management due to their flexibility. This quality, combined with evolvability and a focus on r elationships, make graphs an excellent choice for applications in many area s, including social networks, recommendation/personalization, fraud detecti on, 360-degree views (correlating data from multiple sources), and asset ma nagement (digital assets, data catalogs, and governance).
\nTopBraid Enterprise Data Governance (EDG) lets organizations govern their data asset s, software applications, infrastructure, business capabilities, and proces ses by capturing information about them in a form of a knowledge graph.&nbs p;In this talk, we will describe and demonstrate how TopBraid EDG’s k nowledge graphs are:
\n\n Flexible – graphs are the most flexible formal data structures\n Evolvable – able to accommodate diverse data and metadata as it chang es and grows\n Semantic – the meaning of the data is stored alongside the data in th e graph, as semantic models\n Intelligent – semantics of data are explicit, self-descriptive, and i nclude formalisms for supporting inferencing and data validation\n\nThes e qualities make knowledge graphs an ideal and, arguably, the only viable f oundation for bridging and connecting enterprise metadata silos. By linking and connecting data sources in the overall enterprise context, TopBraid ED G enables enterprises to manage their data as assets and provides extensive capabilities for metadata management and data lineage.
\n DTSTART:20181009T120000 SUMMARY:Strategic Data Management with Enterprise Knowledge Graphs DTEND:20181009T122959 LOCATION: See Description END:VEVENT END:VCALENDAR