Establishing trusted, controlled and accountable data across the enterprise
A data governance transformation that established ownership, business definitions, data quality controls and lineage across critical enterprise data domains.
Data was available across the enterprise, but responsibility and meaning were not consistent.
Business and technology teams were using the same data in different ways. Definitions, ownership and quality controls varied by function, creating uncertainty around which data could be trusted for reporting, analytics and operational decisions.
What we found
- Critical data assets spread across multiple platforms and business functions.
- Limited ownership for important data elements.
- Different definitions for common business terms and measures.
- Data quality issues identified manually after reports were produced.
- Limited visibility into upstream and downstream data dependencies.
- Policies existed but were not consistently connected to day to day data operations.
- Data teams spent significant time investigating where data came from and who owned it.
What the business needed
- A practical governance model that business teams could operate.
- Clear ownership and stewardship for critical data.
- A common business glossary and agreed definitions.
- Data quality rules tied to business critical data elements.
- End to end lineage for important reports and datasets.
- Governance controls embedded into the data platform lifecycle.
- Measurable governance coverage rather than policy documents alone.
We moved data governance from a policy exercise to an operating model connected to everyday data delivery.
The approach combined governance structure, business ownership, cataloging, quality management and lineage. Controls were connected to the data environment so teams could use governance as part of normal delivery.
Governance transformation approach
The team established a governance framework that connected business accountability with technical controls across the data lifecycle.
- Identified critical data domains and critical data elements.
- Assigned data owners and stewards for priority domains.
- Created a common business glossary and data definitions.
- Implemented data quality rules and scorecards for critical elements.
- Established lineage across source systems, data platforms and key reports.
- Defined governance checkpoints for new datasets, data products and analytics assets.
Governance controls were connected across the data lifecycle
The architecture placed governance around data from source through engineering, storage, analytics and consumption.
A six stage model for making governance measurable and operational
Governance was implemented domain by domain, starting with the data that mattered most to business reporting, analytics and operational decisions.
Prioritize
Identify critical domains, data elements, reports and business processes.
Assign
Define owners, stewards, responsibilities and decision rights.
Define
Establish business definitions, classifications and common data standards.
Control
Implement quality, access, policy and lifecycle controls for governed assets.
Trace
Establish lineage from source data through transformation to business consumption.
Measure
Track governance coverage, quality, ownership and remediation over time.
The governance program improved accountability, data quality and visibility across critical enterprise data.
Critical assets owned
Critical data elements were assigned accountable owners and stewards.
Fewer unresolved issues
Defined quality rules, ownership and remediation processes reduced persistent data issues.
Faster lineage discovery
Cataloging and lineage made it easier to trace data from source to report.
Assets governed
Critical datasets, tables, reports and data products were brought under governance controls.
Faster issue resolution
Clear ownership and escalation reduced the time required to investigate data problems.
Glossary coverage
Priority business terms and critical measures were documented with agreed definitions.
Data governance became part of how the organization manages and delivers data.
The program gave business and technology teams a common framework for understanding, owning, controlling and improving enterprise data.
Clear Accountability
Business owners and data stewards had defined responsibility for critical data domains and quality outcomes.
Greater Trust in Data
Quality rules and common definitions improved confidence in data used for reporting and decision making.
Faster Investigation
Lineage and ownership information reduced the effort required to trace data issues and understand downstream impact.
Scalable Governance
The operating model could be extended to new domains, datasets, data products and analytics workloads.
Make enterprise data trusted, accountable and ready for business use.
From governance assessment and operating model design to data quality, lineage, ownership and platform controls, a practical governance program can turn data management into a measurable business capability.
Discuss your data governance program