Pervasive Software Unveils Data Matching Solution Based on Pervasive DataRush

Pervasive Software has introduced Pervasive DataMatcher, a highly accurate and high-performance data matching solution based on the Pervasive DataRush technology. DataRush is an embeddable software platform for data intensive processing applications, and is optimized for use on multi-core processors. Its massively parallel-processing engine has been combined with Pervasive's data matching capabilities to create the new DataMatcher product. DataMatcher is designed to help organizations detect fraud, money laundering, and corruption as well as enable threat detection and law enforcement applications, compliance monitoring, and master data management (MDM).

Organizations of many types suffer huge costs from the combinations of fraud, corruption and non-compliance issues. These organizations regularly need to process large amounts of data from multiple data sources that are often inaccurate, inconsistent and contain duplicate records. The seemingly straightforward task of data matching on large disparate datasets is a daunting challenge for many IT organizations. Even with a fairly small dataset, comparing each record to every other record can generate an overwhelming amount of data. For example, comparing every record of a 100,000-row dataset would involve nearly 5 billion record comparisons. Pervasive DataMatcher can perform these comparisons very rapidly, detect data redundancies and correlate records to deliver fast, precise analytic results with rapid ROI.

"One example of fraud prevention for which DataMatcher can be used is in healthcare insurance claims processing," Mike Bryars, general manager of Pervasive DataRush, tells 5 Minute Briefing. "The objective here is to detect duplicate billings, and DataMatcher enables claims processors to do this much more effectively. Prior to using DataMatcher, one processor was only able to analyze claims in small batches, and was not able to use very much historical data. With DataMatcher, they were able to compare much larger historical data sets from four to five different sources, thereby increasing their ability to detect duplicate claims."

Pervasive DataMatcher is designed to scale seamlessly on large, complex datasets with the ability to match on any or all fields in a dataset, including fuzzy matching. Fuzzy matching enables duplicate entries or records to be identified when they refer to the same information but are not identical in spelling or syntax. DataMatcher is also able to handle multilingual and international character sets with full Unicode support.

For additional information about Pervasive DataMatcher, go here.