Data matching is the name of the process that helps businesses to keep duplicate records within limits. It includes identifying and merging duplicate data records to ensure that the matched data is properly aligned.
Different data matching techniques are there that help data processing experts to match records quickly. Let's talk about them in this blog.
✦ Value of data matching in business
Having an integrated database is a dream of many businesses to date. Did you know that more than 82% of businesses face data integration hurdles at present?
Mostly, businesses face issues when they migrate their data to a centralized database. Duplicate records pop out at that time, which takes away unnecessary space and makes the data condition worse. It adversely affects business operations. One lead gets targeted several times, which costs businesses a loss in both ways, monetarily and reputation-wise.
So, data matching is the way to track all data and to reduce duplicate entries. Let's see what types of data matching there are.
✦ Types of data matches
Look carefully at the following three sets of data to understand how complicated the data matching process is.
Look carefully at the above records; there are three sets of customer records. Records 1 and 2 are similar except for the phone number. Based on the name, all databases are matched. But, according to the collected data, they are different person with the same name. However, most probably, records 1 and 2 can be the same person who uses two numbers. Based on that assumption, record 3 is a distinct one. The address as well as the phone number are new in this record, which can indicate that it's a separate dataset.
With the application of data matching techniques, we can find more specific matches in less time. Let's have a look at them.
✦ Methods of data matching
Exact matching
Like the name suggests, exact matching of the database implies finding the exact match of the datasets. This is the simplest data matching technique that ever existed, and it works extensively on quality datasets. However, the risk of losing important information while exactly matching the database is high here.
Suppose you have two separate sets of databases, one named Jim Halpert and the other named Jimothy Halpert. At the time of checking the database, you noticed two records with the surname "Halpert". To deduplicate the name database using exact matching, you have to count these two records as separate entities. Even though the contact details and location for these two databases are the same. A manual check is needed to confirm the accuracy of this data matching process.
Fuzzy matching
With the application of fuzzy matching, you can match similar records but not identical instances. For example, fuzzy matching can help find incomplete data, typos, and spelling variations. This method has sub-techniques like the Levenshtein distance. This technique counts the number of one-character edits needed to change one word to another.
The English language followed in the UK and the US, is different at various points. For example, spellings like the "analog" are followed in the US, while the same spelling goes like "analogue" in the UK.
Unlike exact matching, this matching technique does not produce duplicate records at all. It can detect similar matches, which best suit the machine learning training datasets. The only issue with fuzzy matching is that it can produce false negatives and false positives sometimes. If you're working on machine learning training data, then this matching technique can help you the most.
Probabilistic matching
After fuzzy matching, the most advanced matching technique is probabilistic matching. It uses statistics to determine the percentage of matching. The chances of matching data between two records can be shown with this matching technique. This technique shows an output of matching ranges from 0% to 100%. Here, 0% means no match, and 100% means a full match or that the records are identical twins.
If the same case repeats here (i.e., "analog" and "analogue"), the match percentage would come as 98% or something. Because both words indicate the same meaning and have no major alphabetical difference.
Overall, probabilistic matching is based on various factors and makes more sense than any other matching technique. This matching technique works best for arranging email or address/location data. This technique works the best for delivering matching percentages unless you make any mistake calculating weights.
ML-based matching
Machine learning models can also help match databases. But for that, you have to teach the algorithms how different data entity is connected to each other. It included data labeling on a large scale for matching and non-matching pairs. This matching technique is also known as the supervised data matching technique. The accurate rate of data matching depends on the value of the data you put on the inputs.
A significant amount of training data is required to develop an ML-based data matching system. Once it gets ready, you can predict the likelihood of a match for new record pairs. However, the complexity of developing this model is high, as the match algorithm finds complex match patterns that you cannot expect in the previous three models.
Hybrid matching
Like the name indicates, hybrid matching is a combination of all the above four matching techniques. All the matching techniques can be applied sequentially or in parallel in order to maximize the chance of finding the right match. Not necessarily have to put everything in one place to match your datasets. You can find good combinations using these four data matching techniques to deduplicate your data records.
For example, you can run data matching using the supervised (ML-assisted) techniques and then go through a fuzzy matching to ensure the algorithm didn't miss any instances of matching. Clubbing matching techniques together greatly helps maintain the integrity of the data.
