ERP
Master Data That Doesn’t Melt Down: An ERP Governance Playbook Meta
Why ERP Master Data Keeps Falling Apart
ERP is only as good as its master data. Duplicate vendors cause overpayments, messy item masters complicate planning, and inconsistent customer records skew reporting. Many organizations attempt a one-time “data cleanse” before an ERP go-live, only to watch quality decay again within a year.
A sustainable approach requires an operating model for master data, not just a cleanup project. That means clear ownership, governed processes, and metrics that keep everyone honest.
Understanding ERP Master Data Domains
Start by mapping out your critical domains:
- Customer master: sold-to, bill-to, ship-to, credit terms, tax IDs, channels.
- Vendor/supplier master: legal names, banking details, payment terms, risk flags.
- Item/material master: descriptions, units of measure, classifications, lead times, sourcing data.
- Chart of accounts and cost centers: financial reporting backbone.
- Plant, warehouse, and location data: for logistics and manufacturing.
Each domain affects multiple processes. Poor item data, for instance, impacts purchasing, warehousing, production planning, and pricing. That cross-functional impact is why governance matters.
Defining the Golden Record and Standards
A golden record is your single, authoritative version of an entity. To define it, you need:
- Mandatory fields for each domain (e.g., tax ID and country for vendors, lifecycle status for items).
- Field-level rules like allowed values, reference codes, and formats.
- Naming conventions and description standards to make search & analytics work.
Document these standards and make them easily accessible—a “master data handbook” that users and stewards reference daily.
Master Data Stewardship: Who Owns What
Assign clear owners for each domain. A typical structure includes:
- Data Owners (often business leaders) accountable for policy and quality.
- Data Stewards handling day-to-day creation, change, and approvals.
- MDM/IT support maintaining tools, workflows, and integration with other systems.
Stewards should sit close to the business, not just in IT, so they understand the impact of bad data on operations.
Designing Controlled Creation & Change Workflows
Most data decay starts at creation time. Replace ad-hoc emails and spreadsheets with structured workflows inside ERP or an MDM hub:
- Request forms with validation rules and mandatory attachments.
- Automated checks for duplicates (name, tax ID, bank details, or fuzzy logic on descriptions).
- Approval routing based on domain and risk (e.g., vendor bank changes require segregation of duties).
Every new or changed master record should pass through the same governed process, with status tracking and audit logs.
Integrating External Reference Data
Improve quality by leveraging external standards:
- Address validation services for customer and supplier locations.
- Standard classification codes (UNSPSC, HS codes, industry codes).
- Credit and risk data from bureaus or risk platforms.
Integrate these into your creation workflows so stewards don’t have to manually look them up.
Preventing Data Drift Across Systems
In most organizations, ERP is not the only system with master data. CRM has customers, procurement has suppliers, PLM has products. Decide which system is the system of record for each domain and synchronize others via integration.
Implement data replication rules and change capture so updates flow reliably. Avoid letting multiple systems edit the same attributes independently—this creates reconciliation nightmares.
Data Quality Metrics That Actually Matter
Measure quality in ways that tie directly to business outcomes:
- Completeness: % of records with all mandatory fields populated.
- Uniqueness: duplicate rate for vendors, customers, and items.
- Validity: compliance with format rules and reference data.
- Timeliness: time from request to approved master record.
- Error impact: number of invoice failures, shipment errors, or planning exceptions caused by bad master data.
Report these metrics monthly to data owners and leadership so master data stays on the radar.
Handling Legacy Data and Clean-Up Projects
Many organizations start with a large backlog of messy data. Approach clean-up as a project, but design it to flow into your ongoing governance model:
- Profile data to identify patterns—missing fields, invalid codes, obvious duplicates.
- Prioritize domains and records that impact critical processes (top customers, strategic suppliers, high-value items).
- Clean in waves and lock in new governance rules as you go so issues don’t reappear.
Use this effort to test your standards and workflows: if they’re too complex, stewards and users will find ways around them.
Supporting Analytics, AI, and Automation
High-quality master data unlocks downstream capabilities:
- More accurate demand forecasting and inventory optimization.
- Better customer segmentation and margin analysis.
- Reliable input for AI models that depend on clean dimensions and reference data.
- Less “exception handling” in automated workflows.
When stakeholders see that master data improvements enable advanced use cases they care about, support for governance grows.
Common Pitfalls and How to Avoid Them
- Over-engineering: Too many fields and rules that slow business down. Start with the essentials.
- No accountability: If everyone can create data, no one owns quality. Assign stewards.
- One-time clean-ups: Without process changes, old habits return. Govern daily, not just at go-live.
- Ignoring user experience: If forms are confusing, users will bypass them. Make it easy to do the right thing.
A Phased Approach to Master Data Governance
- Phase 1: Define domains, owners, and standards.
- Phase 2: Implement creation/change workflows and duplicate checks.
- Phase 3: Run focused clean-up for critical records.
- Phase 4: Integrate external data and synchronize systems.
- Phase 5: Embed metrics, dashboards, and continuous improvement.
Final Thoughts
ERP master data governance isn’t glamorous, but it’s foundational. When you treat data as a product—with owners, SLAs, and quality targets—your ERP stops melting down and starts supporting the analytics and automation your business needs.
- Top Expert
Nathan Rowan
Program Research, Editor, Expert in ERP, Cloud, Financial Automation
