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 ...

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 ...

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, ...

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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 ...

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 ...

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 ...

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, ...

Modernization, in many ways, is often synonymous with centralization. Creating a unified framework from which all enterprise systems derive ensures frictionless operations through a sense of ...

Cube Dev Inc., the creator of an open-source semantic layer that simplifies access to data from disparate systems, is launching an “agentic analytics” platform that uses artificial intelligence to ...