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Data Cataloging with Knowledge Graphs
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Today, data catalogs are increasingly recognized as a central mechanism for data management. They have become a critical building block for helping organizations find, inventory, connect and analyze their diverse and distributed data assets — in order to optimize their business use and value. Knowledge graphs are a key technology for data cataloging because they can meaningfully capture and connect the vast variety of enterprise data sources. They can eliminate data and metadata silos, delivering high-value business applications such as complete end-to-end data lineage and “Google-like” semantic search over metadata. In this webinar we will discuss:
- Why flexibility, extensibility and open APIs are a must for data catalogs
- What enterprise data catalogs (internal to an organization) and open data catalogs in government (e.g., using the standards-based DCAT vocabulary) share in common
- The role of knowledge graphs in capturing information about diverse and siloed data assets and in creating semantic relationships between them
- How assets in a data catalog can be contextualized by connecting them to relevant processes, policies and other business information
- How inferencing and machine learning can automate and simplify the process of tagging and connecting data assets
- Why data catalogs are an important step in supporting enterprises’ move towards data lakes
Categories
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Data Governance52
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Vocabulary Management9
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Knowledge Graphs34
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Ontologies14
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Data Fabric8
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Metadata Management11
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Business Glossaries6
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Semantic File System6
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Reference Data Management7
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Uncategorized2
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Data Catalogs13
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Datasets9
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Taxonomies2
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News3
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Policy and Compliance1
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Life Sciences6
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Automated Operations6
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Financial Services8
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AI Readiness7