Microsoft is moving more of the data governance workload to users, but says that this will lead to greater accountability and transparency.

1. Centralized. The birth of the data warehouse in the 1980s changed everything. By storing data in a single, curated location, everyone could find and query their data with confidence.

Understanding the Big Picture Data modeling isn’t the sole pillar bearing the weight of the data challenges. In fact, it often comes later in the data journey, after several other foundational pillars ...

Lastly, unlike relational data models that require us to anticipate all the data we will need to accommodate in advance, the semantic data model can be defined as the data arrives. This allows new ...

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 entails putting data objects, their relationships and the rules governing them into visual form. Because it calls on diverse expertise, data modeling is rarely a one-time solo effort.

The A16Z model implies that organizations could assemble a fabric of home-grown or single-purpose vendor offerings to build a semantic layer.

Our industry tends to favor open protocols like BACnet and the use of semantic data modeling such as Project Haystack because these make the job of normalizing data for analysis easier to ...

Companies use Cube Cloud to build that semantic layer and connect it to their various apps and utilities, employing role-based access controls, data caching, single sign-on, and scaled-up ...

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 ...