Enterprise Data Governance Transformation

The Problem

Company Context

A Swedish pharmaceutical company selling specialized products across 13 European markets. They’d grown through acquisitions, sold through multiple online channels (Amazon, Shopify, direct), and relied on a network of manufacturing and logistics partners.

Growth Through Acquisition Created Data Chaos

Symptom	Business Impact
25% of core business data outdated	Wrong product information reaching customers
8+ systems with inconsistent data	Staff spending hours manually cross-checking
No clear data ownership	Nobody accountable when data is wrong
Excel-based processes	No audit trail, key-person dependencies
Regulatory gaps	EU compliance deadlines at risk

Why Now?

1. Regulatory deadline: New EU regulations required standardized product data by 2025
2. E-commerce growth: Scaling online channels requires reliable product data
3. M&A integration: Recent acquisitions need data harmonization
4. AI readiness: Clean, governed data is the prerequisite for any meaningful AI initiative
5. Operational cost: Manual data reconciliation consuming too many resources

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The Solution

Scope

Dimension	Scale
Engagement value	€400,000
Duration	6+ months (initial phase)
Data domains	7
Stakeholders	25+
Countries	13
Systems mapped	8+

Six Governance Pillars

Pillar	Business Value
Organization & Accountability	Clear ownership - someone is responsible
Data Quality Management	Metrics and thresholds - problems caught early
Compliance & Governance	Regulatory readiness — meeting EU and international requirements
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

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Implementation

My Role

Responsibility	Activities
Technical leadership	Framework design, methodology, deliverables quality
Stakeholder coordination	25+ stakeholders across 7 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 Manager
• Technical Lead: Me
• Consultant: Supporting analyst

Stakeholder Ecosystem

This wasn’t a technology project with stakeholders - it was a stakeholder project with technology:

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

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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: Map each system’s responsibilities, identify which system owns each piece of data, and design processes to keep them in sync.

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.

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Regulatory Compliance

Pharmaceutical companies face overlapping regulations that all demand clean, traceable data:

Regulation	What It Requires	What We Did
EU product regulations (IDMP/EUDAMED)	Standardized product identification data by 2025	Identified gaps, built a remediation roadmap
Manufacturing quality standards (GxP)	Full audit trail and change control	Mapped requirements to data controls across systems
GDPR	Documentation of how customer data is processed and retained	Built privacy considerations into the customer data domain

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Results

Deliverables

Deliverable	Business Use
7 domain definitions	Clarity on data scope and boundaries
13 data family catalogs	Detailed documentation of every data element
System landscape map	Visual overview of all systems and how they connect
Data flow diagrams	Understanding how data moves between systems
RACI matrices	Clear ownership — who’s responsible, who approves, who needs to know
Implementation roadmap	Prioritized next steps

Business Impact

Outcome	Impact
Ownership clarity	Every data domain has an assigned owner
Compliance roadmap	Clear path to meeting EU regulatory deadlines
System documentation	First complete picture of how data flows across 8+ systems
Issue identification	Critical gaps surfaced and prioritized

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Lessons Learned

1. Governance is people, not just process. 25+ stakeholder coordination was the primary challenge, not technical complexity.

2. Data ownership requires executive backing. Without CFO sponsorship, domain owners couldn’t prioritize governance work.

3. M&A creates persistent data debt. Each acquisition brought incompatible data models requiring ongoing reconciliation.

4. Healthcare adds regulatory complexity. Pharmaceutical regulations (GxP, IDMP, GDPR) layer on top of standard governance — every data decision has compliance implications.

5. Excel is a symptom. Manual Excel processes indicate governance gaps - the fix is process, not tool replacement.

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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.