Match
Identify related records across your data sources using intelligent matching algorithms. The Match module finds duplicates within systems and links records across systems to build a complete view of each entity.Why Matching Matters
Organizations typically have the same people, accounts, or entities represented in multiple systems. Without matching:- Records are fragmented across operational, billing, and administrative systems
- Duplicate records create confusion and operational gaps
- Analytics undercount or overcount unique individuals
- Teams lack the complete information needed for effective coordination
What You Can Do
Configure Match Rules
Define which fields to compare and how much weight each field carries in match scoring.
Choose Match Algorithms
Select from exact matching or fuzzy matching approaches based on your data quality needs.
Review Match Candidates
Examine potential matches with detailed comparison views before accepting.
Set Match Thresholds
Configure score thresholds for auto-match, manual review, and non-match decisions.
Matching Approaches
Exact Matching
Match records on exact field values like SSN, account numbers, or unique identifiers. Best for high-confidence identifiers.
Fuzzy Matching
Handle variations in names, addresses, and other fields using similarity algorithms. Best for real-world data with typos and inconsistencies.
Match Attributes
Name Matching
Handle nicknames, misspellings, name changes, and cultural naming conventions
Address Matching
Match despite formatting differences, abbreviations, and address changes
ID Matching
Exact matching on SSN, account IDs, and unique identifiers with handling for typos
Date Matching
Match dates across different formats with tolerance for data entry errors
Phone/Email
Match contact information with normalization and validation
Custom Fields
Include any field in your matching strategy based on your data
Key Capabilities
Match Scoring
Every potential match receives a confidence score based on how well records align across configured fields. Higher scores indicate stronger matches.Transitive Matching
If Record A matches Record B, and Record B matches Record C, skyMDM recognizes that all three may represent the same entity.Block and Compare
Efficiently process large datasets by first blocking records into candidate groups, then running detailed comparisons only within blocks.Match Audit Trail
Every match decision is logged with the score, contributing fields, and timestamp for compliance and quality review.Business Impact
Complete View
Link records across systems for a comprehensive view of each individual or entity.
Accurate Counts
Know exactly how many unique individuals or accounts you serve.
Better Coordination
Connect teams with complete information for better decision-making.
Who Benefits
- Operations Teams: Access complete records for planning and coordination
- Quality Teams: Accurate attribution for quality measures and reporting
- Finance Teams: Proper counting for revenue and cost analysis
- Compliance Teams: Maintain accurate records for regulatory reporting