eBook
Data, Analytics and AI Governance
How to build an effective governance strategy for your data lakehouse
Data, analytics and AI governance is perhaps the most important yet challenging aspect of any data and AI democratization effort. For your data analytics and AI needs, you’ve probably deployed two different systems — data warehouses for business intelligence and data lakes for AI. And now you’ve created data silos with data movement across two systems, each with a different governance model.
But data isn’t limited to files or tables. You also have assets like dashboards, ML models, and notebooks, each with their own permission models, making it difficult to manage access permissions for all these assets consistently.
The problem gets bigger when your data assets exist across multiple clouds with different access management solutions. What you need is a unified approach to simplify governance for all your data on any cloud.
Read this eBook to find out:
But data isn’t limited to files or tables. You also have assets like dashboards, ML models, and notebooks, each with their own permission models, making it difficult to manage access permissions for all these assets consistently.
The problem gets bigger when your data assets exist across multiple clouds with different access management solutions. What you need is a unified approach to simplify governance for all your data on any cloud.
Read this eBook to find out:
- Why your organization needs a modern approach to data governance that covers the full breadth of data use cases — from BI to ML
- What the key components are for a successful data governance framework
- Data governance best practices for a data lakehouse
- How you can unify governance for your data, analytics and AI use cases with the Databricks Lakehouse Platform
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