Data governance insights and strategy

Part 2 of The Three Failures That Will Define Who Survives AI. Why treating data as a technology concern instead of its own strategic pillar is the root cause, and what Foundation First looks like in practice.

Over 80% of AI projects fail to reach production. The problem is not the technology. Three predictable failure modes are turning enterprise AI into the most expensive technology failure in corporate history.

Healthcare AI pilots stall before reaching production. The model is rarely the issue. The gap between training data and production data is what breaks deployment.

Data quality tells you if your data is clean today. The Organizational Malleability Score tells you whether your organization can keep it trusted as the business changes. Most leaders treat these as the same question. They are not.

Why cataloging everything is the fastest way to ensure your data initiative delivers nothing. A practical approach to scoping data catalogs for healthcare organizations.

Data debt names what went wrong. Data Malleability names the capability that prevents it. This article introduces a new framework for building data that absorbs change instead of fracturing under it.

When 40% of your analytics team’s time goes toward hunting for data instead of analyzing it, the cost adds up fast. For one healthcare system, that hidden waste reached $800,000 a year. Here’s what actually fixed it.

Following a Student Information System migration, a mid-sized community college faced persistent data reliability issues. A structured data quality program using an AI-powered platform established a scalable capability within six months.

A response to Tom Davenport’s call to slow AI development. The problem isn’t that AI is too fast—it’s that organizations are too unprepared. The answer is better foundations, not less innovation.