4 Data Governance Best Practices for AI Success

In the age of AI, the need for new data governance measures is at an all-time high. One of the top concerns is the growing data volumes across organizations: 64% manage at least 1 petabyte (PB) of data, while 41% manage 500 PB of data.

Data volumes are anticipated to increase further as organizations adopt AI; Gartner predicts generative AI will account for 10% of all data produced by 2025, up from less than 1% today.

That means now is the time to determine the best strategies to govern your data and workspaces as you integrate AI into your organization.

Our checklist provides a detailed run through of the best actions to take to ensure you can make the most out of your AI tools, including:

• Ensure data quality
• Enhance data security
• Establish a data governance framework
• Implement lifecycle management

Download the checklist now to equip your organization with the strategies essential for AI success.

Potential risks in your environment can include inactive guest users, orphaned users, over-permissioned users, etc. You can understand these risks through an analysis tool, which can help you take action on these possible vulnerabilities.

Without proper data management, organizations may experience data sprawl, especially with the increasing contribution of AI-generated data, as predicted by Gartner, which can introduce new risks into the organization.