One KPI, many definitions
Debates consume energy on the number itself; decisions slow down.
Data Governance & Management
Turn data from “everywhere and trusted by no one” into discoverable, understandable, accountable assets — governance is a prerequisite for AI and BI, not an add-on.
Challenges
Symptoms of missing governance
Unclear ownership
Producers, consumers, and platform teams lack closed-loop quality responsibility.
Compliance vs sharing
Over-lockdown or uncontrolled sharing — both block cross-domain analytics and AI.
Approach
Governance that lands
Start from critical decision domains; establish standards, roles, and processes; then expand tools — avoid one-shot mega programs.
Scope & priority
Pick domains and critical data products; define a minimum viable governance set.
Standards & org mechanisms
Metric dictionary, master-data rules, data owners, and change process.
Tools & ongoing ops
Catalog, quality rules, lineage, and tickets — governance in daily work.
Capabilities
What we deliver
Governance blueprint & playbook
Roles, processes, priorities, and acceptance criteria.
Metrics & master data
Aligned definitions and change management.
Quality & catalog
Discoverable, traceable, alertable asset views.
Start governance in a critical domain
Book Discovery with priority domains and current definition conflicts.