Master Data Management

What Is Master Data Management?

Master data management (MDM) is the practice of creating a “golden record” for key business entities that is consistent across all systems. Master data includes the core reference data that describes:

• Customers: Who buys from you
• Products: What you sell
• Vendors/Suppliers: Who you buy from
• Employees: Who works for you
• Accounts: Your chart of accounts
• Locations: Where you operate

MDM ensures these entities are defined consistently everywhere.

Why Master Data Matters

The Problem: Inconsistent Master Data

Without MDM, the same entity exists differently across systems:

Customer “Acme Corp” appears as:

• “Acme Corporation” in CRM
• “ACME CORP” in ERP
• “Acme Corp.” in billing system
• “Acme” in spreadsheets

Consequences:

• Can’t calculate total customer revenue
• Duplicate records inflate customer count
• Marketing sends multiple communications
• Support can’t see complete history
• Analytics are unreliable

The Solution: Master Data Management

MDM creates one authoritative version:

• Single customer ID links all instances
• Consistent attributes across systems
• Changes propagate everywhere
• Analytics reflect true picture

Master Data vs. Transactional Data

Master Data	Transactional Data
Describes entities	Records events
Changes slowly	Changes constantly
Shared across systems	Specific to processes
Requires governance	Volume-focused
Examples: Customer, Product	Examples: Orders, Payments

Master data provides context for transactional data. An order (transaction) references a customer and products (master data).

MDM Architecture Approaches

Registry Style

Systems keep their own data; MDM provides cross-reference:

• Links records across systems
• Doesn’t store master data itself
• Lowest disruption to implement
• Limited ability to enforce standards

Consolidation Style

MDM aggregates data for analytics:

• Creates golden record for reporting
• Source systems unchanged
• Read-only master for analytics
• Doesn’t fix source quality

Coexistence Style

MDM and sources both maintain data:

• Changes can originate anywhere
• Synchronization between systems
• Balance of control and flexibility
• Complex to implement

Centralized Style

MDM is the authoritative source:

• All changes go through MDM
• Sources subscribe to master
• Strongest data quality
• Highest implementation effort

MDM Process

1. Data Profiling

Understand current state:

• What master data exists?
• Where does it live?
• What’s the quality?
• How do systems differ?

2. Data Matching

Identify same entities across systems:

• Exact matching (ID, email)
• Fuzzy matching (name similarity)
• Rule-based matching
• Machine learning matching

3. Data Merging

Create golden records:

• Survivorship rules (which source wins)
• Attribute-level decisions
• Conflict resolution
• Manual review for uncertain matches

4. Data Stewardship

Ongoing maintenance:

• New record creation
• Change management
• Exception handling
• Quality monitoring

5. Data Distribution

Share master data:

• Push to source systems
• API access for applications
• Reporting and analytics
• Integration with workflows

MDM Challenges

Organizational: Who owns customer data? Sales? Marketing? Finance?

Technical: How to match records reliably across systems?

Process: How to handle ongoing changes and exceptions?

Quality: How to clean up years of accumulated duplicates?

Adoption: How to get systems to use master data?

How Go Fig Addresses Master Data

Go Fig helps with master data challenges:

Cross-system matching: Identify same entities across connected systems

Unified view: See consolidated master data in one place

Semantic layer: Define consistent entity attributes

Data quality alerts: Flag master data issues automatically

Excel integration: Work with master data in familiar tools

While not a full MDM platform, Go Fig provides practical master data capabilities for finance teams who need consistent customers, vendors, and accounts for reporting.

MDM Best Practices

1. Start with high-value entities: Focus on customers or products first
2. Define clear ownership: Single owner per data domain
3. Establish governance early: Rules for creation and changes
4. Invest in matching: Quality matching prevents duplicates
5. Plan for exceptions: Not everything matches automatically
6. Measure quality: Track duplicate rates and accuracy
7. Build incrementally: Don’t try to boil the ocean