AI needs data. But that data is often sprawled across business units and functions, each with its own definitions and metrics. A semantic layer can unify conflicting data definitions by providing a ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. In my last article, we discussed how enterprise AI has pivoted from mere data retrieval to ...

Generative AI (GenAI) and large language models (LLMs) rocketed onto the scene in 2023, and boards now want returns from the technology yesterday. But AI disillusionment is brewing, with many projects ...

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Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic ...

When semantic layers emerged, their role was to provide business users with a consistent, governed view of data across BI tools and dashboards. Metrics were standardized, departments were aligned, ...

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 ...

AI is washing over organizations and their data environments, putting pressure on data managers to be able to pull data, almost instantaneously, to fuel AI agents and applications. To meet this need, ...

Generative AI (GenAI) is posed to be an utterly transformative technology that evolves the way business—and undoubtedly, the world—interacts with information. In a proprietary context, rapid access to ...

A VentureBeat survey of 101 enterprises found 68% traced a confident, wrong AI agent answer to bad context. Governed semantic layers catch it twice as often.