Enterprise Data Governance Transformation
€400K engagement, 25+ stakeholders, 7 data domains mapped across 13 countries
The Problem
Company Context
A Swedish specialty pharmaceutical company with operations across 13 European markets, multiple brands acquired through M&A activity, e-commerce channels (Amazon, Shopify, direct), and complex supply chain with CMO and 3PL partners.
Growth Through Acquisition Created Data Chaos
| Symptom | Business Impact |
|---|---|
| 25% of ERP data outdated | Wrong product information reaching customers |
| 8+ systems with inconsistent data | Manual reconciliation consuming analyst time |
| No clear data ownership | Nobody accountable when data is wrong |
| Excel-based processes | No audit trail, key-person dependencies |
| Regulatory gaps | IDMP/EUDAMED 2025 deadline at risk |
Why Now?
- Regulatory deadline: EMA IDMP/EUDAMED compliance required by 2025
- E-commerce growth: Scaling online channels requires reliable product data
- M&A integration: Recent acquisitions need data harmonization
- Operational cost: Manual data reconciliation consuming too many resources
The Solution
Scope
| Dimension | Scale |
|---|---|
| Engagement value | €400,000 |
| Duration | 6 months |
| Data domains | 7 |
| Stakeholders | 25+ |
| Countries | 13 |
| Systems mapped | 8+ |
Six Governance Pillars
┌─────────────────────────────────────────────────────────────┐
│ DATA GOVERNANCE FRAMEWORK │
├──────────────┬──────────────┬──────────────┬───────────────┤
│ Organization │ Data │ Compliance │ Technology │
│ & Account- │ Quality │ & │ & Tools │
│ ability │ Management │ Governance │ │
├──────────────┼──────────────┼──────────────┼───────────────┤
│ Skills │ Process │ │ │
│ & │ Improvement │ │ │
│ Training │ │ │ │
└──────────────┴──────────────┴──────────────┴───────────────┘| Pillar | Business Value |
|---|---|
| Organization & Accountability | Clear ownership—someone is responsible |
| Data Quality Management | Metrics and thresholds—problems caught early |
| Compliance & Governance | Regulatory readiness—EMA/FDA/GDPR compliance |
| Technology & Tools | System clarity—what data lives where |
| Skills & Training | Team learns to maintain this themselves |
| Process Improvement | Governance that gets better over time |
7 Data Domains Mapped
| Domain | Data Owner | Key Systems | Status |
|---|---|---|---|
| Product Master Data | Operations MD | ERP, PIM, Serialization | Validated |
| Product Innovation | Innovation Lead | ERP, Excel, Project Management | Sent for review |
| Product Business Logic | Business Control | ERP, Excel, BI | Validated |
| Regulatory & Compliance | Regulatory Team | Veeva, ERP | Validated |
| Customer Master Data | TBD | ERP, 3PL systems | WIP |
| Customer Pricing | Global RGM | ERP, Excel | WIP |
| Supply Chain & Logistics | Ops | ERP, 3PL systems | Established |
Implementation
My Role
| Responsibility | Activities |
|---|---|
| Technical leadership | Framework design, methodology, deliverables quality |
| Stakeholder coordination | 25+ stakeholders across 6 departments |
| Workshop facilitation | Domain definition, validation, reference groups |
| System analysis | Data flow mapping, integration architecture |
| Deliverable production | Documentation, RACI matrices, roadmaps |
Team Structure
- Project Lead: Senior consultant
- Technical Lead: Me
- Consultant: Supporting analyst
Stakeholder Ecosystem
This wasn’t a technology project with stakeholders—it was a stakeholder project with technology:
Executive Sponsor (CFO)
│
▼
Steering Committee (5 business unit leaders)
│
├── Data & Analytics (6 people)
├── Operations (3 people)
├── Finance (3 people)
├── Commercial (3 people)
├── Digital (3 people)
├── Scientific Affairs (3 people)
└── Marketing (1 person)
│
▼
Domain Experts (20+ specialists)Workshop Cadence
| Workshop Type | Frequency | Purpose |
|---|---|---|
| Domain definition | Weekly | Define data families, ownership |
| System analysis | Weekly | Map data flows, integrations |
| Reference group | Bi-weekly | Cross-functional validation |
| Steering committee | Monthly | Progress, decisions, escalations |
Key Challenges Navigated
Multi-Country Complexity
Problem: Different markets had different product codes, naming conventions, and processes. Approach: Focus on core product master data first, acknowledge regional variations, establish global standards with local flexibility.
System Fragmentation
Problem: 8+ systems with no single source of truth. Approach: Document system responsibilities, identify system-of-record per data element, design reconciliation processes.
Resource Competition
Problem: Stakeholders had day jobs—governance was additional work. Approach: Executive sponsorship, structured workshops (time-boxed), documentation that didn’t require re-work.
Regulatory Compliance Dimension
| Regulatory Body | Requirements | Our Contribution |
|---|---|---|
| EMA (IDMP/EUDAMED) | Standardized product identification data by 2025 | Gap analysis, remediation roadmap, data quality requirements |
| FDA (GxP) | Audit trail, change control, validation | Mapped GxP requirements to data controls, identified gaps |
| GDPR | Processing activity documentation, retention policies | Customer data domain included GDPR processing considerations |
Results
Deliverables
| Deliverable | Business Use |
|---|---|
| 7 domain definitions | Clarity on data scope and boundaries |
| 13 data family catalogs | Detailed data element documentation |
| System landscape map | Integration architecture visibility |
| Data flow diagrams | Understanding how data moves |
| RACI matrices | Clear ownership and accountability |
| Implementation roadmap | Prioritized next steps |
Business Impact
| Outcome | Impact |
|---|---|
| Ownership clarity | Every data domain has an assigned owner |
| Compliance roadmap | Clear path to IDMP/EUDAMED readiness |
| System documentation | First comprehensive view of data landscape |
| Issue identification | Critical gaps surfaced and prioritized |
Innovation POCs
While primarily a governance engagement, I developed two AI POCs demonstrating automation potential:
| POC | What It Does | Projected Impact |
|---|---|---|
| Document Intelligence Agent | AI analysis of 247 policy documents | 270x ROI projection |
| AI Governance Platform | Automated compliance checking | Pharma-specific validations |
Both handed over to team—showing that good governance makes AI automation possible.
Lessons Learned
-
Governance is people, not just process. 25+ stakeholder coordination was the primary challenge, not technical complexity.
-
Data ownership requires executive backing. Without CFO sponsorship, domain owners couldn’t prioritize governance work.
-
M&A creates persistent data debt. Each acquisition brought incompatible data models requiring ongoing reconciliation.
-
Healthcare adds regulatory complexity. GxP, IDMP, GDPR requirements layer on top of standard governance.
-
Excel is a symptom. Manual Excel processes indicate governance gaps—the fix is process, not tool replacement.
Want to discuss data governance?
If you’re facing similar challenges—data spread across too many systems, regulatory deadlines looming, nobody sure who owns what—I’d be happy to share what worked here. Get in touch.