Enterprise Data Governance
& Strategy

The Challenge

A Fortune 500 manufacturing conglomerate with operations in 22 countries was generating vast quantities of data across its supply chain, finance, HR, and commercial functions — but had no coherent way to trust, find, or govern it. Data lived in 34 distinct source systems, each with its own definitions, formats, and quality standards. The concept of a "customer" meant different things in CRM, ERP, and logistics systems.

The reconciliation burden had become crushing. Finance teams spent an estimated 30% of their working week reconciling discrepancies between systems — comparing revenue figures from the ERP against the data warehouse against management reports, each showing different numbers. Analysts had developed personal shadow spreadsheets as the only sources they trusted. A board-level request for a cross-divisional performance comparison required two weeks of manual data gathering from seven different teams.

Three previous attempts to implement a data governance programme had failed — not due to technology choices, but because they had been positioned as IT projects rather than business-led initiatives. The data ownership question had never been resolved: who was responsible for the definition and quality of "net revenue" across 22 country operations was genuinely unclear, and no technology solution could resolve that ambiguity without an organisational change programme running alongside it.

Our Approach

Vipra Software's approach treated data governance as an organisational programme with a technology delivery component — not the reverse. We embedded data governance consultants with the business stakeholders before any technical work commenced, establishing data ownership, domain definitions, and quality standards as business decisions rather than engineering constraints.

• Data Domain Mapping & Ownership (Weeks 1–4): Facilitated working sessions with 18 business domain leads to establish canonical definitions for 220 critical business entities. Assigned Data Owners (business-side accountability) and Data Stewards (operational quality management) for each domain. This phase was entirely non-technical.
• Data Quality Profiling (Weeks 5–7): Automated quality assessment across all 34 source systems using Python-based profiling against the agreed canonical definitions. Produced a Data Quality Scorecard by domain — the first objective measurement of data quality the organisation had ever produced. Average initial score: 61%.
• Enterprise Data Catalogue (Weeks 8–12): Deployed a data catalogue solution (Collibra) as the system of record for business glossary, data lineage, and asset discovery. Populated 3,400+ data assets, linked to their business definitions, source systems, and Data Owners. Built automated lineage from source systems through the EDW to BI dashboards.
• Data Quality Framework (Weeks 13–17): Implemented automated data quality monitoring using Python-based DQ rules deployed as SQL assertions in the EDW pipeline. 840 DQ rules across 18 domains, with alerting to Data Stewards on quality threshold breaches. Built a Data Quality Dashboard for business-facing quality transparency.
• Self-Service BI Enablement (Weeks 18–23): Redesigned the EDW semantic layer using a governed dimensional model. Rebuilt 45 Power BI report templates against the certified data model. Deployed row-level security by business unit. Trained 80 business analysts as "data champions" responsible for peer training within their divisions.
• Governance Operating Model (Week 24): Established a Data Governance Council with quarterly cadence. Published governance policies, escalation paths, and the ongoing Data Quality Scorecard review process. Handed over to the internal Data Governance Office.

Technical Architecture

The technical architecture centres on a governed Enterprise Data Warehouse (EDW) built on SQL Server with a certified dimensional model. Source data flows from 34 operational systems via a combination of SSIS and Python-based ingestion pipelines into a staging layer, through data quality validation gates, and into the governed EDW. Any record failing DQ rules is quarantined and routed to the relevant Data Steward for remediation rather than silently populating downstream reports.

Collibra serves as the enterprise data catalogue, providing the business glossary, data asset inventory, and lineage visualisation. Integration between Collibra and the EDW enables automatic propagation of certified data definitions to the BI semantic layer — changes to business definitions in Collibra trigger review workflows for the corresponding EDW objects, preventing definitional drift between the catalogue and the physical data model.

Power BI Premium serves as the self-service BI layer, with report templates built against the certified semantic model. Row-level security is implemented at the semantic layer, ensuring users can only access data for their business unit without requiring separate report instances. The Data Quality Dashboard is built directly on the DQ monitoring output tables, giving Data Stewards and domain leads real-time visibility into quality metrics for their domains.

Business Impact

Manual reconciliation time dropped by 40% within the first quarter of the governance framework going live — the direct result of single certified definitions replacing the divergent system-specific interpretations that had driven reconciliation overhead. Finance teams reported recovering an average of 6 hours per week previously spent on cross-system comparisons.

The board-level cross-divisional performance comparison that had previously required two weeks of manual data gathering was delivered in 4 hours using the self-service BI environment in the month following launch. This was cited by the CFO as the clearest demonstration of governance programme value in the post-implementation review.

Data quality scores across the 18 governed domains improved from an average of 61% to 84% over the six months following launch, driven by the combination of automated DQ monitoring, steward accountability, and source system remediation work triggered by the quality visibility the programme created. The programme has since been extended to cover 6 additional business domains not included in the initial scope.