The data governance challenges that have vexed agencies in recent years are becoming even more pressing with the adoption of artificial intelligence-based solutions. The data needed to fuel AI tends to be spread across various systems and stored in a wide variety of formats. This makes it difficult both to make that data readily accessible and to apply consistent privacy, security and other policy controls.
Because of the volume and complexity of the data involved, data lakes and other centralized repositories often prove unfeasible. Fortunately, another approach is possible: a universal semantic layer makes it possible to apply strong data governance while managing the data where it resides.
“If I package up the enterprise and make it simplistic, then everyone can participate. There’s this idea of ‘Here’s the enterprise. It is stable, it is consistent, … the security is managed, have at it,” explained Terry Dorsey, Senior Data Architect and Evangelist at Denodo.
In this video interview, Dorsey discusses rethinking data management in the age of AI. Topics include:
- How has AI changed data governance needs?
- What are the challenges when more users have access to enterprise data?
- How does a semantic layer facilitate safe use of AI?
