Resolve Duplicate Records During Data Migration
Automatically detect and resolve duplicate records with AI-powered deduplication. 99.8% accuracy in 10-30 minutes vs 3-7 days manual work.
Common Duplicate Scenarios
AI-powered detection and resolution for all types of duplicate records
Exact Duplicates
100% AccuracyIdentical records across all fields
Two customer records with same name, email, phone, address
Hash-based comparison
Keep first occurrence, remove duplicates
Cryptographic hashing identifies exact matches instantly across billions of records
Near Duplicates
99.8% AccuracySimilar records with minor variations
John Smith vs John A. Smith, same email and phone
Fuzzy matching + similarity scoring
Merge records, preserve all unique data
ML models calculate similarity scores using multiple algorithms (Levenshtein, Jaro-Winkler, phonetic matching)
Cross-System Duplicates
98.5% AccuracySame entity exists in multiple source systems
Customer in CRM and billing system with different IDs
Entity resolution across systems
Create master record, link all instances
AI entity resolution links records across systems using probabilistic matching and relationship analysis
Temporal Duplicates
99.9% AccuracyMultiple versions of same record over time
Customer address updated 3 times, all versions migrated
Timestamp analysis + key matching
Keep latest version, archive history
Temporal analysis identifies record evolution and preserves correct version history
Partial Duplicates
97.5% AccuracyRecords sharing some but not all key fields
Same email but different names (maiden name change)
Multi-field probabilistic matching
Human review for ambiguous cases
Probabilistic models assign confidence scores and flag ambiguous matches for review
Hierarchical Duplicates
99.2% AccuracyParent-child records incorrectly duplicated
Company record duplicated with all child contacts
Relationship graph analysis
Deduplicate parent, preserve child relationships
Graph algorithms analyze relationship structures and preserve referential integrity during deduplication
4-Phase Automated Resolution Process
Complete duplicate detection and resolution in 10-30 minutes
Phase 1: Detection
5-10 minutes
- Scan all source and target data
- Generate cryptographic hashes for exact matching
- Calculate similarity scores for fuzzy matching
- Identify duplicate clusters and groups
Phase 2: Analysis
3-8 minutes
- Classify duplicate types (exact, near, cross-system)
- Assign confidence scores to each match
- Identify master records for each cluster
- Flag ambiguous cases for review
Phase 3: Resolution
2-7 minutes
- Merge duplicate records preserving all unique data
- Update foreign key references to master records
- Archive or delete redundant records
- Validate referential integrity
Phase 4: Verification
2-5 minutes
- Verify no duplicates remain in target
- Validate all relationships preserved
- Generate deduplication report
- Document resolution decisions
Matching Algorithms
Multiple algorithms ensure accurate duplicate detection across all scenarios
| Strategy | Algorithm | Accuracy | Speed |
|---|---|---|---|
| Exact Match | Cryptographic hashing (SHA-256) | 100% | Instant |
| Fuzzy Match | Levenshtein distance + Jaro-Winkler | 99.8% | Fast |
| Phonetic Match | Soundex + Metaphone | 98.5% | Fast |
| Token-Based | Jaccard similarity + TF-IDF | 99.2% | Fast |
| ML-Based | Neural network similarity | 99.5% | Medium |
| Entity Resolution | Probabilistic record linkage | 98.8% | Medium |
People Also Ask
What causes duplicate records during data migration?
Duplicates arise from multiple sources: same entity in multiple source systems, data entry errors with slight variations, temporal records (multiple versions over time), incomplete deduplication in source systems, and merge conflicts during migration. DataMigration.AI detects all duplicate types with 99.8% accuracy using multiple matching algorithms including exact, fuzzy, phonetic, and ML-based matching.
How does AI detect near-duplicate records?
AI uses multiple algorithms simultaneously: Levenshtein distance for character-level differences, Jaro-Winkler for string similarity, Soundex and Metaphone for phonetic matching, Jaccard similarity for token-based comparison, and neural networks for semantic similarity. Each algorithm generates a confidence score, and the AI combines scores to identify near-duplicates with 99.8% accuracy, even with typos, abbreviations, or formatting differences.
Can duplicate resolution be done mid-migration?
Yes. DataMigration.AI performs real-time duplicate detection during migration, preventing duplicates from entering the target system. The AI checks each record against existing target data and other records in the current batch, resolving duplicates immediately. This eliminates the need for post-migration cleanup and ensures data quality from the start. Resolution happens in 10-30 minutes for millions of records.
How long does duplicate resolution take?
DataMigration.AI completes duplicate detection and resolution in 10-30 minutes for typical datasets (millions of records), compared to 3-7 days for manual deduplication. The 4-phase process includes detection (5-10 min), analysis (3-8 min), resolution (2-7 min), and verification (2-5 min). Speed depends on dataset size, duplicate complexity, and matching algorithms used. 100x faster than manual approaches.
What happens to data from duplicate records?
DataMigration.AI preserves all unique data during deduplication. For near-duplicates, the AI merges records by creating a master record that combines all unique fields from duplicates, updates all foreign key references to point to the master record, and archives or deletes redundant records. No data is lost - all unique information is preserved in the master record with full audit trail of merge decisions.
Ready to Eliminate Duplicates?
Get 99.8% accurate duplicate detection and resolution in 10-30 minutes with AI-powered deduplication.