SetMatch
Designed to replace slow, resource-intensive, and unsuitable for large-scale dataset systems that rely on sequential searches
Bulk Dedupe
Challenges in traditional deduplication
Resource Intensive
Sequential searches are slow and demand extensive computational power
Retail
Processing large datasets such as 10 million data points takes months
Telecom
Cannot manage variations in names, addresses, and other non-unique attributes
Network Clogging
High data transfer and I/O operations strain resources
Scalable deduplication for modern enterprises
Operational Efficiency
Processes large datasets in record time, freeing up resources for other critical tasks.
Data Accuracy
99.5% accurate matching and deduping, improving decision-making and customer insights.
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Scalable
Accommodates any volume, variety, and velocity of data
The Science Behind SetMatch’s Accuracy
Advanced Algorithms
Combines statistical methods, set theory, and machine learning for accurate matching, increased performance, and efficiency when linking or deduplicating.
Efficient Clustering
Groups voluminous data into multiple sets of clusters based on shared attributes for super-fast matching, significantly reducing comparison time.
Persistent Caching
Essential inputs are cached as persistent objects, minimizing database operations.
Dynamic Cluster Management
Supports splitting, merging, and realignment of clusters using nested sets to optimize accuracy and performance.
Matches Partial Identities
Performs trillions of comparisons of names with addresses, date of birth, or phone numbers to find duplicate records through specialized deduplication.
Why SetMatch excels
Customize matching rules to fit specific business requirements.
Achieve high recall and precision with cluster rules based on match scores and assigned weights.
Easy-to-use interface to manage clusters, navigate data, and verify results with maker-checker policies.
Enables manual merging and fine-tuning for iterations in complex datasets for proper cleansing and standardization.
Integrates data from disparate sources and merges and refines it to create one master record for each customer.
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