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