Results from an independent university study compare the highest performing fuzzy matching tools.
The above chart reviews and compares various fuzzy matching software software tools. An independent study, found that Data Ladder’s fuzzy matching software outperformed several major companies such as IBM and SAS.
Note – Other software companies offering fuzzy matching tools scored lower on the study, including: QualityStage, FEBRL, The Link King, FRIL, LINKS, HDI, BigMatch, and CDL-python-lite.
|Fuzzy Matching Software: Definition Fuzzy Matching is defined as the process of identifying records on two or more data sets that refer to the same entity across various data sources such as databases and websites. fuzzy matching is required when combining data sets that don’t have a common identifier, such as an identification number. It can be performed for different purposes, such as data collation or building lists. Identifying and correcting common data quality issues is a challenge for most organizations, regardless of their size.For accuracy both the number of found matches vs. possible matches, and number of false matches were taken into account. This is an essential part of evaluating match accuracy.DataMatch Enterprise|
The clear winner was DataMatch Enterprise, Data Ladder’s fast and accurate fuzzy matching software. Through proprietary fuzzy matching algorithms, Data Ladder’s DataMatch software suite helps the user:
> Detect and link records within and between data sets with multiple customizable fuzzy matching and phonetic matching techniques.
With the substantial growth in data linkage activities in industries such as healthcare and education over the last several years, there has been increasing demand for high performing linkage software tools. West Virginia University was recently tasked with assessing the long-term impacts of certain medical conditions on patients over an extended period of time. Through using Data Ladder’s fuzzy matching software, researchers were able to link two groups of records together to make the determination on whether previous medical conditions affected long-term health and patient care.
Data Ladder also worked with Zurich Insurance on their fuzzy matching activities. In the insurance industry, it is critical to have payee names aggregate and match for the functioning of various payment processes. The constant need to monitor data requires clean, usable data due to the stringent requirements of the industry.
Read some of Data Ladder’s case studies across various industries.