Poor data governance can lead to a myriad of issues that include data interpretation inconsistencies, security ...

As companies deploy agentic AI, CIOs and data leaders face a critical mandate: deliver governed, trusted data that AI systems can understand.

Data governance needs to shift from inefficient, unreliable, quickly outdated manual processes that prevent teams from capturing and leveraging the value of their data and move toward data-driven ...

A compliance program should be able to clearly articulate the criteria for evaluating success, highlighting the technology or processes required.

Data governance is essential for effective data management. It establishes a framework for structuring and organizing information—including how it’s defined, cataloged, stored and accessed ...

Imagine this all-too-familiar scenario: a complaint is served, discovery proceeds, and your client is left scrambling trying to understand which data they have, where they have it, who has it, how ...

As data continues to grow in complexity, business leaders must rethink their approach and embrace a unified model for security, compliance and governance. This is not just a shift in technology ...

Crucially, data governance isn’t a one-time project. It’s an ongoing discipline. When embedded into transformation roadmaps from day one, it becomes a strategic enabler, not a barrier.

Data Governance and Provenance: Two words that are critical to the future of generative AI Using "borrowed" material for training. Fair use or not? By Bob O'Donnell February 29, 2024, 11:16.