From data governance to organizational resilience: how data reduce business risk

What it is about

Ana G. Tavarez, Director of Data and Analytics at Humano Seguros, presented at the Fourth Annual Risk Conference (September 18, 2026) why an organization resilience is built in how it governs its data every day before the crisis.

The 20 slides start from a story: after a storm floods Santo Domingo, an insurer receives 12,000 incident notifications, with three versions of the same information and four hours to answer a five-minute question. They demystify data governance as the traffic code of information, with four elements (ownership, quality, traceability, access and control), show two cases and take one more step toward artificial intelligence governance, with four questions for any AI system in operation.

Objectives

• Explain why data risk should be its own category on the risk map.
• Present the four elements of data governance and how they apply with examples.
• Extend the same framework to artificial intelligence governance.

Key takeaways

1. Resilience is not avoiding the crisis but absorbing the shock, continuing to operate and adapting without losing solvency, reputation or the relationship with the regulator and the customer; it depends on reliable information.
2. The four elements of data governance are ownership (someone with a name and surname for each critical data item), quality, traceability, and access and control.
3. In a financial sector case during COVID, an organization with siloed data took months and another with a catalog and identified owners took days: the difference was governance, not technology.
4. In a bank fraud example, a single standard to identify customers and products allowed the dots to be connected in time and the scheme to be stopped before it escalated.
5. AI governance is the same data governance one level up: each model needs an owner, validation, continuous monitoring, explainability and a person with real authority to pause it.