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 ...

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 ...

Conventional data management systems are fundamentally ill-suited for the world of data as it exists today. These systems, based with few exceptions on the relational data model, are broken because ...

These are not “nice-to-haves”; they are prerequisites for distributed cognition, operating at machine speed. As Tatipamula and Cerf point out, the network can no longer simply host intelligence. It ...

Semantic data helps teams understand what their information represents. It gives data a clear meaning so people know how different pieces connect. When teams use semantic data, they see not only the ...

If your AI feels slow, expensive or risky, the problem isn’t the models — it’s the data, and cognitive data architecture is ...

Data has evolved over the years. Complex data structures, unstructured data, real-time processing, growing data volumes, and new varieties of data are all part of the evolution. Platforms have changed ...