Managing data is a critical survival skill for any organization. Companies are investing in new data architectures and solutions—such as data mesh, data fabric, data access governance, and data observability—to keep pace with expanding business appetite for data. But the key to managing data at scale is metadata. Metadata answer among others the following questions:  What is the physical name of the database where the data is stored? Where is the data located? (i.e. platform (or dbms), server, etc.) What are the names of the tables in the database? What columns are on the tables? What is the primary key? What is the data model of the database?  What are the definitions of the business entities? What attributes make up these entities? What is the business meaning of the attributes? Do the attributes have restrictive domains? etc

MetaSense is an engine that gives answers to these questions. MetaSense is built on the top of the OpenMetadata an open standard with a centralized metadata store and ingestion framework supporting connectors for a wide range of services. MetasSense adds AI based modules to the OpenMetadata to detect and suggest missing information, such identifying and evaluating potential master data. Automatically generating a master data model, mapping data entities and configuring a MDM hub. Suggesting actions for matching and merging to establish a single source of truth, based on usage patterns, trust scores and data steward input, identifying and resolving data quality issues. Suggesting data quality rules based on existing datasets and running them. Automating ongoing data quality checks and advanced data profiling.