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Accelerate Data Innovation with
Enterprise Knowledge Graphs

TDWI Solution Spotlight

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Virtual Solution Spotlight: Enabling Data Self-Service with Security, Governance, and Regulatory Compliance

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There’s a quiet revolution going on among data-savvy professionals who are looking for an edge in analytics, AI/ML, and data management. To search for and analyze complex data relationships more effectively, organizations need models and systems that store relationships and make them discoverable. Enterprise knowledge graphs and graph databases specialize in providing these capabilities and can produce uncommon data insights. Learn how you can apply these solutions today to analytics and AI/ML, data catalogs, and emerging data integration concepts such as data fabrics, data virtualization, and data mesh.

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  • David Stodder

    Sr. Director of Research for Business Intelligence at TDWI

    The Role of Enterprise Knowledge Graphs for Uncovering New Data Insights

    To create new competitive advantages, improve resilience, and protect themselves from fraud, abuse, and regulatory exposures, organizations need to uncover and analyze increasingly complex data relationships. This is proving difficult with traditional business intelligence, analytics, and data management as data volume and variety explode, data democratization adds many more users, and analytics and AI/ML workloads become more dynamic and demanding. Enterprise knowledge graphs, semantic models, and graph databases are enabling many organizations to move beyond the constraints of traditional technologies to gain faster insights into data relationships, including through powerful data visualization.

    This TDWI presentation will discuss issues driving interest in enterprise knowledge graphs, semantic models, and graph databases. Looking at TDWI research, we will examine the importance of graphs for solving agility and speed-to-insight challenges with analytics, AI/ML, data catalogs, governance, and data integration.


    Navin Sharma

    VP, Product , Stardog

    Accelerating Analytics and AI with Enterprise Knowledge Graphs

    Knowledge graphs are on the rise at enterprises hungry for greater automation and intelligence. The flexibility of the graph model, along with its explicit storage of data relationships, makes it not only easy to manage data arriving from diverse sources, but search and explore data to reveal new insights that would otherwise be very difficult to discover.

    An Enterprise Knowledge Graph fills that critical gap in existing data management tech stacks. It fits nicely between where data is stored, catalogued, and consumed to eliminate data access barriers, add meaning to data through semantic models, and promote a culture of self-service and self-sufficiency.

    Join this session to learn how Stardog’s Enterprise Knowledge Graph platform can:

    • Streamline access to your data with virtualization
    • Enrich your data with business meaning using semantic standards
    • Identify new connections and insights through inference
    • Deliver better data to your existing analytics tools

    Speaker Bio: Navin Sharma is vice president of product at Stardog, a leading enterprise knowledge graph technology platform provider. He is a highly regarded data management expert and seasoned product management executive and has helped organizations achieve significant growth with new production innovation and adoption. Among other positions, he has served as VP of product at both Precisely (formerly Syncsort) and Pitney Bowes, Inc.

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