Creating a trusted view of customers, products and suppliers across the enterprise
A master data transformation that consolidated fragmented records, improved data quality and created governed golden records for critical business entities.
The same customer, product or supplier existed differently across multiple systems.
Years of business growth and application changes had created fragmented master data. Different systems used different identifiers, naming conventions and business rules, making it difficult to establish one trusted enterprise record.
What we found
- More than 40 systems holding customer, product and supplier information.
- Duplicate records across operational and analytical systems.
- Different identifiers for the same business entity.
- Inconsistent naming, addresses, product attributes and classifications.
- Manual reconciliation between systems and business teams.
- Limited visibility into the source and quality of master records.
- Downstream reports producing different results because of inconsistent master data.
What the business needed
- A single trusted view of critical business entities.
- Common definitions and identifiers across systems.
- Automated matching and duplicate detection.
- Clear rules for which source should be trusted for each attribute.
- Controlled creation and modification of master records.
- Better data quality across analytics and operational processes.
- A scalable foundation for future data products and reporting.
We established a governed master data layer that created consistent enterprise records without forcing every source system to change.
The approach combined data profiling, standardization, matching, survivorship and governance. Master records were created centrally and then made available to downstream applications and analytics.
Master data modernization approach
The team created a repeatable framework for identifying, matching, governing and distributing trusted master records.
- Profiled source systems and identified critical master data attributes.
- Defined common business identifiers and attribute standards.
- Built matching rules to identify duplicate and related records.
- Applied survivorship rules to determine the trusted value for each attribute.
- Created golden records for customer, product and supplier domains.
- Published mastered data to analytics and downstream business processes.
A central master data layer connecting multiple enterprise sources to trusted business entities
The architecture separated source systems from mastered data while providing common controls for quality, security, lineage and downstream consumption.
A six stage process for turning fragmented records into trusted enterprise master data
Each master domain followed a controlled process so that data quality improved without disrupting operational systems.
Profile
Assess source systems, attributes, duplicates, ownership and data quality.
Define
Establish business definitions, identifiers, attributes and domain ownership.
Standardize
Normalize formats, classifications, addresses, names and reference values.
Match
Identify duplicate and related records using defined matching rules.
Master
Apply survivorship rules and create trusted golden records for each domain.
Distribute
Publish mastered data to analytics, reporting and downstream business processes.
The master data program improved quality, consistency and the efficiency of downstream operations.
Fewer duplicate records
Matching and survivorship reduced duplicate customer, product and supplier records.
Higher data quality
Standardization and validation improved completeness, accuracy and consistency across critical attributes.
Less reconciliation effort
Business teams spent less time comparing and correcting records across systems.
Records mastered
The framework was applied across a large enterprise master data portfolio.
Source systems connected
Multiple operational and analytical systems were brought into a common master data process.
Faster new record onboarding
Standard workflows reduced the time required to validate and establish new master records.
The organization gained a trusted foundation for customers, products and suppliers across business processes.
The transformation improved the quality of information used by operations, analytics and leadership while reducing the manual work required to reconcile different systems.
One Trusted Business Entity
Golden records created a consistent enterprise representation of customers, products and suppliers.
Better Business Decisions
Analytics teams could use consistent master data instead of reconciling different versions across reports.
Lower Operational Effort
Automated matching and standardization reduced manual correction and reconciliation work.
Stronger Data Foundation
Mastered data created reusable building blocks for analytics, reporting, data products and future transformation programs.
Create one trusted view of the business.
From source assessment and data quality to matching, survivorship and golden records, a structured master data program can improve the reliability of enterprise information.
Discuss your master data program