As data grows in volume, AI becomes increasingly vital for analytical tasks within organizations. However, for AI to provide reliable and meaningful insights, it must be built with a comprehensive understanding of this data. Effective data access controls must be deployed to ensure that data remains accessible yet secure. There are many ways that AI can be utilized to address an organization’s needs, but for these tools to be effective, they require a detailed understanding of the data they operate on. A comprehensive data catalog includes not only the raw data but also metadata, data lineage, and annotations from subject matter experts. Metadata—such as column names, data types, and measurement units—enables AI tools to interpret and analyze data accurately.
To build a data catalog that supports effective AI use while maintaining strict security, it’s essential to follow a structured approach that enriches data, tracks its origins, integrates expert insights, and controls access. In his article that appeared in InsideAI News, the CTO of Greystones Group, provides steps that outline the recommended practices to achieve a robust and reliable data catalog.
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