Trends in Data Governance for 2026

hasizing the need for human oversight to handle complex business rules and ensure data accuracy. AI can assist in identifying anomalies and patterns, but the final validation often requires human intervention.

Challenges Posed by AI

While AI offers opportunities, it also presents challenges, particularly in ensuring data quality and preventing errors. Nigel highlighted the risk of AI providing inaccurate answers due to a lack of nuanced business logic. Donna Burbank added that AI should not be seen as a "magic solution" but rather as a tool that requires well-documented business rules to function effectively. The need for a balance between automation and human oversight is important to mitigate risks such as data leakage and hallucinations.

Importance of Cross-Functional Collaboration

Effective data governance requires collaboration across different business functions. Donna emphasized that governance is about "collaboration and giving people a voice." Successful governance involves engaging stakeholders from various departments to ensure that data policies align with business needs. This approach helps in identifying and solving data-related issues more efficiently, as demonstrated in real-world examples shared during the session.

Organizational Structure and Governance

The session highlighted the importance of having a structured approach to data governance. Committees or centers of excellence can facilitate cross-functional discussions and decision-making. Donna suggested that roles should be clearly defined, with accountability for specific data areas. This structure helps in breaking down silos and ensuring that data governance is integrated into the organization's overall strategy. Nigel added that pre-work and identifying the right stakeholders are key to making governance forums effective.