Multiple IFS Instances
Each subsidiary operated its own ERP environment, storing supplier, customer, and prospect records independently.
Manufacturing
Boomi
Master Data Management
Each subsidiary operated its own ERP environment, storing supplier, customer, and prospect records independently.
Data was entered via CSV uploads, manual updates, and legacy applications with minimal validation controls.
Duplicate records (e.g., “Sony Limited” vs. “Sony Ltd”), polluted data fields (incorrect values in phone and address fields) and missing mandatory information.
When consolidating data across all IFS instances, duplicate and one-to-many relationships increased exponentially.
Looking to unify and cleanse master data across multiple ERP instances? Partner with NeosAlpha to build a governed, analytics-ready data foundation.
Book a Free Consultation CallNeosAlpha engaged sales and customer management teams to understand duplicate scenarios and business-specific rules.
Designed a consolidated IAL view to unify data from all subsidiary IFS instances into a centralised structure.
Implemented two dedicated MDH processes for supplier and customer master data -defined intelligent matching rules, configured survivorship logic, executed multiple dry runs in staging and fine-tuned duplicate detection criteria.
Configured Boomi’s Quarantine dashboard for business-led resolution of flagged records without IT dependency.
Designed forward-looking pipelines to push golden records into a data warehouse, enabling advanced analytics and BI reporting.
Processed 12,000+ supplier and 75,000+ customer records, identifying 78% supplier golden records, 22% genuine customer duplicates, and 5% data quality issues.
Enabled business users to independently resolve data conflicts using Boomi’s Quarantine dashboard, reducing IT dependency.
Empowered sales teams with clean, trusted master data to run targeted up-sell and cross-sell campaigns.
Implemented scheduled integrations to automatically validate and cleanse new or modified records, ensuring sustained data quality.
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