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software-development
Company Master Data Management — MDM System
A company master data management system that consolidated organisation records, removed duplicates, improved data quality, and created a trusted source of company information.
The goal
Create a clean and reliable company master dataset by resolving duplicates, standardising fields, enriching records, and establishing governance workflows.
What we did
- ✓Audited existing company data sources and quality issues
- ✓Designed canonical company record structure
- ✓Built matching logic for duplicate and related entities
- ✓Standardised names, domains, industries, locations, and identifiers
- ✓Added enrichment workflows for missing company attributes
- ✓Created confidence scoring for entity resolution decisions
- ✓Developed review workflows for ambiguous matches
- ✓Prepared the master dataset for CRM, analytics, and AI use cases
The result
The MDM system improved the reliability of company data across operational and analytical workflows. Teams gained a cleaner source of truth for accounts, reporting, segmentation, enrichment, and automation.
Company data is foundational to sales, marketing, finance, support, analytics, and AI systems. When company records are duplicated, incomplete, or inconsistent across platforms, teams lose confidence in reporting and automation becomes harder to trust.
This project focused on building a company master data management system to consolidate fragmented company records into a reliable source of truth. The work began with an audit of existing data sources, including CRM exports, spreadsheets, enrichment providers, billing records, and operational databases. Common issues included duplicate accounts, inconsistent naming, missing domains, outdated industry classifications, and conflicting location data.
A canonical company record structure was then designed to define what a trusted company profile should include. This covered core fields such as company name, website domain, industry, size, location, ownership status, account identifiers, lifecycle stage, and source metadata. Entity resolution logic was developed to identify likely duplicates using domains, names, aliases, addresses, and other signals.
Because automated matching can create risk when records are ambiguous, confidence scoring and review workflows were introduced. High-confidence matches could be resolved automatically, while lower-confidence cases were flagged for human review. This balanced efficiency with data governance.
The resulting MDM system created a cleaner and more usable company dataset. It improved CRM reliability, reduced duplicate handling, strengthened reporting, and created a better foundation for segmentation, account scoring, recommendation systems, and AI-powered workflows.
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