Over the years, we've seen a couple of different organizational models for delivering analytics to the business. While both models have their advantages, each model has some severe drawbacks that make ...

Metadata is increasingly driving semantic data modeling, said Suresh Nair, New York-based vice president and chief architect, financial services, at IT processing services company Mphasis, who was ...

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 ...

Sometimes, you can enter into a technology too early. The groundwork for semantics was laid down in the late 1990s and early 2000s, with Tim Berners-Lee's stellar Semantic Web article, debuting in ...

Semantic data helps teams understand what their information represents. It gives data a clear meaning so people know how ...

Together with Snowflake, Sigma and other industry leaders are driving a new open standard for semantic data, ensuring organizations can define metrics once, govern centrally, and analyze everywhere ...

Microsoft’s Semantic Kernel SDK makes it easier to manage complex prompts and get focused results from large language models like GPT. At first glance, building a large language model (LLM) like GPT-4 ...