Turning fragmented customer and product records into trusted master data
An AI assisted master data management solution that identifies duplicate and related records across enterprise systems, improves entity matching and supports creation of trusted golden records. The solution combines machine learning, business rules and human review so critical master data remains governed and accountable.
The same customer, product or supplier existed as multiple records across the organization.
Enterprise data often grows through acquisitions, new applications, regional systems and digital channels. Without a consistent identity for each entity, analytics, operations and customer processes work with incomplete or conflicting information.
What we found
- Customer records were duplicated across CRM, commerce, loyalty and service systems.
- Product information differed across ERP, ecommerce and merchandising systems.
- Supplier records were maintained independently by different business units.
- Names, addresses, phone numbers, product descriptions and identifiers were stored in inconsistent formats.
- Rule based matching handled obvious duplicates but struggled with variations and incomplete information.
- Data teams spent significant time investigating possible matches and resolving conflicts.
- Duplicate and inconsistent records reduced confidence in enterprise reporting and downstream applications.
What the business needed
- A common approach for identifying the same entity across multiple systems.
- AI assisted matching that could handle variations beyond exact field comparisons.
- Clear confidence thresholds for automatic and manual resolution.
- A trusted golden record with traceability back to contributing source records.
- Business rules to determine which source should win when attributes conflict.
- Steward workflows for ambiguous or high impact records.
- A scalable master data foundation for analytics, customer experience and AI applications.
We combined machine learning with business rules to create a controlled entity resolution process.
The solution was designed to automate high confidence matches while keeping uncertain decisions with accountable data stewards.
AI assisted master data resolution
The platform creates a consistent identity for customers, products and suppliers without requiring every record to follow the same format.
- Ingested master data from approved CRM, ERP, commerce, loyalty, supplier and operational sources.
- Standardized fields such as names, addresses, phone numbers, identifiers and product attributes.
- Created candidate pairs using deterministic rules and similarity based matching.
- Used machine learning to score the likelihood that records represented the same entity.
- Applied different confidence thresholds for automatic match, manual review and no match outcomes.
- Used source priority and business rules to resolve conflicting attributes.
- Created golden records while retaining links to contributing source records.
- Captured steward decisions so approved matches could improve future matching processes.
A governed master data layer connects source systems to analytics and business applications.
The architecture separates source data from the mastered view and preserves the evidence used to create each golden record.
A six stage operating model for building trusted master data
The implementation starts with a focused domain such as customer or product data and expands as matching quality and governance controls are proven.
Assess
Identify master data domains, source systems, duplicate patterns, ownership and business priorities.
Standardize
Normalize attributes, identifiers, formats and reference values across contributing sources.
Match
Generate candidate matches using deterministic rules and machine learning based similarity.
Resolve
Apply confidence thresholds, survivorship rules and source priorities to determine the master record.
Govern
Route uncertain cases to data stewards and retain decisions, ownership and traceability.
Publish
Make approved golden records available to analytics, applications and downstream data products.
The impact comes from reducing duplicate records and the manual work required to resolve them.
Fewer duplicate records
AI assisted matching can identify and consolidate duplicate entities that are difficult to detect through exact rules alone.
Lower manual matching effort
High confidence matches can be processed automatically while teams focus on ambiguous records.
Faster remediation
Structured candidate matching and steward workflows reduce the time required to investigate data conflicts.
Higher match accuracy
Similarity based models can improve identification of related records where names, addresses or attributes vary.
Faster onboarding
New source systems can be mapped into the master data process using standardized matching and resolution patterns.
Source traceability target
Each mastered record can retain links to contributing source records and the decisions used to resolve conflicts.
Trusted master data improves every process that depends on customer, product and supplier information.
A reliable entity view becomes a shared foundation for analytics, reporting, customer experience, supply chain operations and AI use cases.
Better Customer View
Customer interactions from different channels can be connected to a more consistent identity, improving segmentation and customer analytics.
Reliable Product Analytics
Consistent product identities make sales, inventory, pricing and assortment analysis more reliable across channels and locations.
Lower Data Operations Effort
Automated matching reduces repetitive investigation and allows data teams to focus on exceptions and higher value data quality work.
Stronger Data Foundation
Golden records with ownership, lineage and source traceability provide a dependable foundation for enterprise analytics and AI.
Build a trusted view of your customers, products and suppliers.
Combine AI based entity resolution, business rules and data governance to create reliable master data across your enterprise data estate.
Discuss your master data program