Semantic layers stand out as a key approach to solving business problems for organizations grappling with the complexities of managing and understanding the meaning of their data. A semantic layer, ...

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Today's enterprises need unified semantic layers that seamlessly connect traditional BI, GenBI applications, and emerging AI workloads. As organizations face challenges in making enterprise data truly ...

One of the bottlenecks in getting value out of generative AI is the difficulty in turning natural language into SQL queries. Without detailed contextual understanding of the data, the text is ...

Semantic layers have been around for a long time - but with underwhelming results. Is the concept outdated? No, but something it missing. In the big data era, we need to raise our data abstraction ...

Snowflake and Salesforce initiated the Open Semantic Interchange project in collaboration with industry partners to solve the problem of the fragmentation of enterprise system data definitions. There ...

A growing number of businesses are embracing data models -- abstract models that organize elements of data and standardize how they relate to one another. But as the data analytics and AI boom drives ...

Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic, and definitions among user groups. A universal semantic layer resolves those ...

It’s a story about trust. Because as systems grow more intelligent, trustworthy data becomes the electricity that powers them ...

Applications are continuing to move from process-centric to data-centric, meaning that rather than data being locked inside applications in silos, the business logic will be embedded in the data and ...

Semantic reasoning tools for databases aim to close that gap. They introduce an abstraction layer that understands business ...