Trusted data is even more critical as AI use increases. These eight steps will help data leaders build a strong foundation for effective analytics and AI applications.
Before adopting AI-powered BI applications, organizations need stronger business context, trusted workflows and users prepared to assess AI outputs.
AI systems move too fast for data governance that waits until the end. Enterprises are replacing outdated review methods with earlier oversight and automated controls.
Awareness enabled by appropriate data and business logic is one of the differences between agentic applications that properly perform and those that never reach production.
Cyberattacks on Springfield Public Schools and Everett City Hall show what it takes to keep operating when an intrusion cuts off access to core systems.
Older technology cannot be swapped out quickly due to costs and complexity, but organizations can strengthen recovery capabilities while modernization efforts continue.
Business and technology leaders are revising their data protection plans as global conflicts challenge current resilience and risk management plans.
The failure pattern of a leadership transition reveals what data strategy institutionalization requires. Without it, a data strategy framework is a dependency, not a strategy.
The AI auditability gap isn't an AI problem. It's decades of data management debt, finally coming due, exposed by autonomous decision-making.