Data matching refers to the process of comparing two different sets of data and matching them against each other. The purpose of the process is to find the data that refer to the same entity. Many times the data come from two or more different sets of data and have no common identifiers. But data matching is also useful to detect duplicate data within a database.
How Does Data Matching Work?
The problem data matching tries to solve is knowing that two "entities" are, in fact, the same "entity"? There are many ways to perform data matching. Often, the process is based on a data matching algorithm or a programmed loop, where each piece of the data set is compared and matched against each piece of the other data set.
There are two main ways of linking data:
- Deterministic record linkage, based on several matching identifiers.
- Probabilistic record linkage, based on the probability of several identifiers matching.
The most common is probabilistic data matching, as deterministic linking tends to be too inflexible.
First, the data need to be sorted, or blocked, into similar-sized blocks with the same attribute. These should be attributes that are unlikely to change, such as names, dates of birth, color, or shape. Then the matching takes place. Matching can be done in many ways. Names, for example, can be matched phonetically as well as by letter.
Next, the relative weight for each attribute is calculated to measure its importance. Then it's time to calculate the probability for matching. Finally, an algorithm adjusts the relative weight for each attribute to get the Total Match Weight. That is then the result: the probabilistic match for two things.
Simplified, the process is as follows:
- Standardize data.
- Pick attributes unlikely to change.
- Sort data into blocks.
- Match via probabilities.
- Assign value to matches.
- Summarize to get the total weight.
With time, the goal is to keep fine-tuning the data matching algorithms to obtain better results.
What Is the Need for Data Matching?
Data matching can serve many purposes. For example, it is a way to avoid duplicate content. Data matching is also useful in different kinds of data mining. Data matching can also serve the purpose of identifying links between two data sets.
Data Matching Use Cases
The applications for data matching and database matching are numerous. Below are a few examples:
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E-commerce: In e-commerce, an everyday use case is all the platforms comparing prices. They use data matching to locate identical products from different stores, even if they don't have the same description.
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Mailing lists: Data matching can help clean up email lists to get rid of duplicates and dirty data.
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Healthcare: Matching medical records with other data to study the effect of things like drugs, treatments, and the environment.
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Fraud detection: Data matching can help identify suspicious transactions, behaviors, and individuals.
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Computing: Data matching can help optimize computing processes. By detecting duplicate data, deduplication algorithms help reduce storage need and network data transfer.
Benefits of Data Matching
When handling large amounts of data, data matching allows you to perform more precise and accurate searches and analyze data at a more advanced level and with more reliable results. Data matching allows for data to be compared, patterns to be identified, and for irregularities to be flagged. In short, data matching and database matching help increase accuracy, efficiency, and compliance within a wide range of industries and contexts.