Why Is Fuzzy Matching Software a Key for Deduplication?

Identifying golden and unique records across or within datasets is crucial to prevent identity theft, meet compliance regulations, and improve customer acquisition. Banks, government organizations, healthcare providers, and marketing companies all require matching algorithms to identify and deduplicate redundant entries to enrich their master database.

Fuzzy matching is a known set of algorithms for measuring the distance between two similar entities. But certain limitations hinder its effectiveness to quickly find matches for larger, disparate datasets. 

Due to this reason, fuzzy matching software can instead prove to be a better alternative. Let us look at some of the reasons why. 

What Makes Fuzzy Matching Effective?

Fuzzy matching is thought to be a far more robust matching entities method in contrast to exact matching for record linkage, deduplication, or entity resolution scenarios. It can more efficiently pick up data anomalies such as typos, trailing and leading spaces, misspellings, and punctuation errors as part of its matching criteria to maximize match scores and reduce false positives. 

On the other hand, exact or deterministic matching is heavily reliant on unique identifiers, such as email address or social security number, to ascertain if two different entries refer to the same entity or not. If such data is not accurate, no matches will be found. For more info, read deterministic vs. fuzzy matching.

Benefits of Fuzzy Matching

Since fuzzy logic is based on identifying matches falling that lie between a 0 and 1 continuum, it can offer the following benefits: 

• Higher match accuracy: fuzzy name and address matching algorithms can lead to lower false positives especially where the slightest string variations such as misspellings, letters with numbers, numbers with letters, leading spaces, and more can go undetected using exact matching.
• Reliable when no unique identifiers exist – fuzzy algorithms don’t require social security numbers, emails, or other identifier data to be consistent across datasets to pick up matches.
• More flexible to use: there’s a degree of the tediousness of creating multiple exact matching rules to cope with the complexity of data quality issues within a dataset. Fuzzy logic rules, on the other hand, can accommodate several data variations within a single rule that can be much quicker to refine and execute for a specific matching scenario.
• Better suited to match dynamic data: finding matches in fields where data can quickly become out of date and must be updated periodically – such as job title or email address - is often a challenge using exact matching. Fuzzy list matching, on the other hand, can offer a much more practical method of recognizing variations to identify matches.

Limits of Manual Fuzzy Matching

The Disparity in Data Formats

It is not uncommon for organizations to have key fields such as names and addresses stored in multiple formats. Manual data entry errors, obsolete data, and lack of file naming conventions, for example, can create data irregularities in the form of missing, punctuation, or spelling anomalies. Such errors can complicate the business rules required to effectively run fuzzy algorithms. 

Indexing techniques such as blocking, which restrict the number of mismatched pairs in deduplication, are highly dependent on data quality. The lower the data quality, the easier it is to misplace data into incorrect blocks and face high false negatives. 

Manual Fuzzy Matching Is Time-Consuming

A spectrum of fuzzy matching algorithms – each with its own strengths and weaknesses – available to apply for a use case. However, unlike exact matching, fuzzy matching algorithms are more complex to set up and need continuous refinement to make sure it falls within an accepted similarity threshold.  

Key Benefits of Choosing a Fuzzy Matching Software

Accuracy for Complex Datasets

Dedicated fuzzy matching tools can help identify more accurate matches when dealing with large datasets. Organizations – especially healthcare providers and financial institutions - have data scattered across disparate sources from SQL databases and Excel files to legacy mainframe data and Hadoop-based repositories. Creating and refining numerous logic rules to cater to such diverse datasets can not only be time-consuming but also highly complex.

A fuzzy matching tool, however, has out-of-the-box native connectivity for several applications, databases, and CRMs and usually has a plethora of fuzzy logic algorithms that it can run within an intuitive, drag-and-drop interface. 

Scalability

Manually applying fuzzy logic rules can work best for small datasets. But anything beyond a few thousand records and it breaks apart. Organizations today need to identify matches across thousands of records – and doing so effectively can become mission-critical as data requirements scale.

A data matching software, on the other hand, can run matches across several million records that can ease the strain on users in refining logic rules for scalability purposes. This can enable organizations to automate matching processes and save unnecessary man-hours that could otherwise be spent on completing other projects.

Conclusion 

Fuzzy matching, as a set of algorithms, can provide far better match scores than exact matching in scenarios where unique identifiers are either inconsistent or missing and where multiple anomalies exist.

However, if organizations are looking to scale their fuzzy algorithms to process several thousand to millions of records or handle disparate data formats and sources, a fuzzy matching tool can be a better bet.