Whitepaper

How best-in class fuzzy matching solutions work: Combining established and proprietary algorithms

Inside you’ll learn:

While data matching used to be an easy activity, over the years, the type, format, and complexity of raw data has changed. Today it’s no longer enough to run a match between two fields with similar spellings to weed out duplication, nor is it enough to deploy one kind of algorithm to find matches.

Today, the best-in-class data matching tools use a combination of algorithms + proprietary algorithms to weed out even the most difficult data fields to help companies achieve their data matching objectives. This whitepaper will explore the challenges of matching, how different types of matching algorithms, how a best-in-class software uses these algorithms to achieve data matching goals.

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Merging Data from Multiple Sources – Challenges and Solutions

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8 principles of data management

An average enterprise – with 200-500 employees – uses about 123 SaaS applications to digitize their business processes. With large amounts of data being generated

8 principles of data management

An average enterprise – with 200-500 employees – uses about 123 SaaS applications to digitize their business processes. With large amounts of data being generated