Absent a comprehensive federal AI framework, agencies should be guided by four governance priorities. While the federal ...

Open banking, embedded finance, and AI are changing where bank data flows. Data privacy now determines how far innovation can ...

When governance is engineered into how data is created and used, it accelerates access, experimentation, model development ...

The ongoing dive into modernity—and all the new technologies and hype trains that come along with it—requires a modern data architecture to support it. With this architecture comes a variety of other ...

The field of data and analytics is rapidly growing and evolving, requiring creativity, skill and a deep understanding of emerging technologies, particularly in data governance. Advanced strategies for ...

Data governance is transforming the world of business and IT as organizations increasingly acknowledge and embrace the importance of data in the modern world. And this transformation significantly ...

Data fabric is a powerful architectural approach for integrating and managing data across diverse sources and platforms. As enterprises navigate increasingly complex data environments, the need for ...

The implications of AI for data governance and security don’t often grab the headlines, but the work of incorporating this ...

India's AI framework proposes a layered, lifecycle approach. But how will it work in practice, and what challenges does it ...

More than any other factor, the hyperabundance of accessible data has powered today’s surge in AI adoption and generative AI capability. Collecting, cleaning, organizing, and securing that data for AI ...

CU Boulder collects, uses and maintains a significant amount of data. This includes, but is not limited to student, employee, research and finance data. Institutional data supports CU Boulder’s ...

How do indexing protocols support DAO governance? Learn how this Web3 middleware transforms raw data into actionable insights for tracking votes and monitoring smart contract security.