About three years ago, Steven Adler was running the privacy consulting practice at IBM. As he talked with IBM customers, he discovered that many of the large customers had some pretty complex IT ...

Dataiku’s field chief data officer for Asia-Pacific and Japan discusses how implementing AI governance can accelerate ...

To govern AI safely and keep its speed advantage, enterprises must move from static, rule-based control systems to adaptive, ...

For financial institutions, threat modeling must shift away from diagrams focused purely on code to a life cycle view ...

Abstract: The increasing reliance on artificial intelligence (AI) and advanced analytics to gain competitive advantages has elevated the importance of robust data governance frameworks. This article ...

Angela Virtu, a professor of business analytics and A.I. at American University’s Kogod School of Business, examines why most ...

If you’re reading this, there’s a very good chance your organization’s approach to data governance is the exact opposite of what it should be for the AI era. If you’ve read my prior articles, you know ...

Real-time insights and self-service analytics have become critical for survival in a rapidly evolving data landscape.

It’s a time when large datasets are being leveraged for real-time analysis. Tried-and-true approaches to cobbling together technologies and policies to achieve workable data governance and security ...

In today’s rapidly evolving digital landscape, the value of data cannot be overstated. Data has become the lifeblood of innovation, driving decisions, shaping industries, and transforming how we live ...

Most data governance models weren’t built for AI. They were designed to ensure compliance, not to support real-time decision-making. They helped manage audits and reports but were never intended to ...

State and local governments are embracing data modeling and governance strategies to advance efficiency, sharpen decision-making, and elevate their service delivery. In so doing, they’re helping ...