ERP Duplicate Data Identification and Resolution

Detection Techniques and Matching Algorithms

Duplicate detection requires more than exact-match comparisons. Real-world duplicates involve spelling variations (Acme Corp vs. ACME Corporation), transposed digits (ZIP 10001 vs.

10010), abbreviations (St. vs Street), and merged/split records. Fuzzy matching algorithms quantify the similarity between records, enabling detection of duplicates that exact matching would miss.

The choice of algorithm depends on the data domain and the types of variation present.

• Exact matching: baseline comparison using key fields (tax ID, DUNS number, email)—catches only 30-40% of true duplicates
• Fuzzy string matching: Levenshtein distance, Jaro-Winkler, and Soundex algorithms for name and address comparison
• Machine learning classifiers: trained models that learn duplicate patterns from manually reviewed examples (precision >95%)
• Blocking strategies: group records by common attributes (ZIP code, first 3 letters of name) before detailed comparison to reduce processing
• Composite scoring: combine multiple matching algorithms into a single confidence score with configurable match thresholds

Survivorship Rules and Merge Strategy

Once duplicates are identified, the merge process must determine which data values survive into the merged record. Survivorship rules define field-by-field logic: most recent value, most complete value, value from the authoritative source system, or business-user decision for ambiguous cases. A poorly designed merge can destroy valuable data, so survivorship rules require business steward approval.

• Most recent rule: use the most recently updated value—appropriate for contact information and addresses
• Most complete rule: retain the record with the most populated fields as the primary survivor
• Source priority rule: prefer values from the designated system of record for each attribute
• Aggregate rule: combine values from duplicates (e.g., merge all transaction history, sum all open balances)
• Manual review queue: route ambiguous merges (equal confidence scores, conflicting critical fields) to data stewards

Prevention and Ongoing Deduplication

Deduplication is not a one-time project—without prevention controls, duplicates re-accumulate at 2-5% per year. Implement real-time duplicate detection at the point of data creation in the new ERP. When a user creates a new customer or vendor record, the system should automatically search for potential matches and present them before allowing a new record to be saved.

• Real-time matching: configure duplicate detection rules that fire during record creation in the new ERP system
• Scheduled batch dedup: run monthly deduplication scans across all master data domains to catch duplicates that bypass real-time checks
• Training and awareness: educate data entry users on duplicate prevention—search before create, use standard naming conventions
• Quality metrics: track duplicate creation rates per department and user to identify training or process improvement needs