Data governance & compliance

Councils with binding authority and accountability improve data quality measurably, while information-only meetings become theater within 12 months.

Named accountability replaces the destructive 'everyone is responsible' fiction and ties data quality to executive performance metrics.

Institutional data knowledge lives in human relationships; tools are empty without the network that populates and maintains them.

Profiling reveals hidden defects and visible scores make quality accountable, the first step to fixing multimillion-euro poor-quality costs.

Consolidating duplicate records into Golden Records unlocks accurate CLV and cuts marketing waste, but only after answering use-case questions first.

Catalogs surfaced in Slack and BI tools with curated key datasets drive adoption; standalone tools gather dust as governance theater.

One record system with multiple regulatory lenses avoids costly rebuilds and manages global patchwork risk more cheaply than manual per-regulation compliance.

AI Act obligations require knowing which systems fall into which risk tiers and who is accountable, a foundational task missing from most organizations.

Compliance is the floor; an independent board reviewing novel or sensitive data uses protects institutional trust beyond legal minimums.

Contracts with enforced schemas and change policies reduce pipeline incidents from schema drift and clarify producer-consumer accountability.

Practiced teams meet the 72-hour GDPR notification deadline and expose process gaps before a real breach does under regulator scrutiny.

Seventy percent of breaches involve internal actors; limiting blast radius and flagging abnormal access catches attacks in days, not months.