Custom data matching
Cost-effective data matching for complex data challenges
Custom data matching
Cost-effective data matching for complex data challenges
Cost-effective data matching for complex data challenges
Cost-effective data matching for complex data challenges
At customdatamatching.com, we specialize in data matching and entity resolution, leveraging over 30 years of experience in computer programming and data mining. Our expertise lies in matching disparate datasets using advanced techniques, ensuring accurate connections between names, addresses, text, invoices, phone numbers, and more. With in-depth knowledge and a proven track record, we deliver reliable data integration solutions for your business needs.
We specialize in complex data matching and entity resolution using a variety of tools including: SAS, Excel, SQL, and Python. Our services cover: removing duplicate records, consolidating records into one entity or ID, name and address matching, and matching disparate datasets together. Our services go beyond what typical data matching software offers to fit your unique data needs.
Successfully completed data-matching projects include:
Successfully completed a complex accounts payable vendor file cleanup using extensive fuzzy-matching and hierarchical link analysis (parent/child organization of records).
Won a competitive bid to engage with the World Bank to conduct an audit of their Accounts Payable Vendor Master File. Identified duplicate vendors, consolidated rows where appropriate, all using advanced fuzzy-matching methodology.
Served as Lead Architect to merge Medicare & Medicaid data together. Created SAS solution that fuzzy-matched providers and beneficiaries on name, address, specialty, phone numbers, Medicaid IDs, and NPIs.
Developed a fuzzy-matching solution to merge asylum-seeker names in one database to another. Many national and non-standard names were present, making the data-matching more complex.
Data matching is a process where 2 or more text strings are compared via fuzzy-matching functions, such as Levenshtein, GED, Jaro-Winkler, PctTriGram, and more. It is a critical step in improving data quality & data integrity. Contact us to learn more!
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