Gartner suggests that 40% of all business initiatives lose value because of incorrectly linked, or messy data. Fuzzy matching software links the data automatically, using sophisticated matching logic, regardless of spelling errors, unnormalized data, or incomplete information.
Fuzzy Matching is a technique to identify two elements of text, strings, or entries that are approximately similar but are not exactly the same. For example, If you want to identify duplicate information and make a single customer view, you will have to perform ‘data matching’. The ability to identify all records that point to the same entity in different data sources is called Fuzzy Matching.
Take a look at the following example:
You can see that the products are the same in slightly different ways. Fuzzy Matching identifies two pieces of text that are approximately similar and gives you the facility to match the same product listing although their descriptions that aren’t exactly the same.
Speed and efficiency are key to success for businesses. The faster firms can process large amounts of data, the quicker they can expand their businesses. Some times data completeness does not keep pace with increasing transaction volumes. As a result, process and settlement data do not consistently match, making transaction matching a painful task. This is where fuzzy matching can be useful, especially for data sets that lack standard and exact identification and description, such as product data.
The fuzzy matching approach is priceless for creating a single source of data set for business analysis or for forming the foundation for master data management (MDM), which helps organizations merge data from different sources across the organization, while maintaining accuracy and limiting manual review to a minimum.
Therefore, the decision that a firm makes for using tools with fuzzy matching can determine its ability to quickly and accurately process data, reduce risk and accelerate its success.
Traditional logic is binary, meaning that a statement is either correct or incorrect. In contrary, fuzzy logic indicates the extent to which a statement is correct. Let's say you want to create a single customer view (CSV) or product data. In theory, if all the data were 100% clean (no duplicates, no misspelled words, etc.), you could perform the merge operation easily. However, real-world data is often very raw, messy and imperfect. For example, consider a customer with the name "Bob Smith". If the name "Bob Smith" is misspelled like "Bb Smit" in some datasets, the resulting merge will be inconsistent. One of the most important use cases of fuzzy matching is when we want to merge tables using the text data.The merge requires a set of rules that can handle slight variations in the text field. These rule sets are called fuzzy rules and we call this process "Fuzzy Name Matching". the technique of fuzzy matching let businesses have a single, clear data set.
Something that you have to consider when using this technique is that you are the captain of this intelligent ship. The identifiers that you choose and the values that you assign to them constitute the basis of the fuzzy matching. If the identifiers are too broad, you will certainly find more matches and upon manual review, you will find that some of these matches are not what you intended to do.
Taking the example of the mentioned table, imagine that the ml of the perfumes are important for a business to be in different categories. This is where Fuzzy Matching techniques get tricky. Fuzzy logic offers all of them as the same info if you do not choose the correct identifiers. Therefore, it may not realized the goal of the business in such a situation. However, it is still one of the most efficient techniques one can use to normalize the data.
- Generally, it is worth to invest a reasonable amount of time and resources in setting up the fuzzy matching software to fit your unique use case.
- Rigorous unit testing is useful, especially if your use case requires a lot of precision. Once the software is setup, most of the workflow can be automated. Taking the example of creating a unified customer view, the fuzzy matching software only needs to be run at regular intervals to ensure that your customer view is updated.
- Almost all fuzzy matching algorithms are susceptible to have mistakes. This usually means manual error checking. You need to balance the amount of manpower you can devote to error checking, and the effect that mistakes would have on your business.
- After all rigorous testings, still possibility of ending up with some mistakes is inevitable. Be careful not to use fuzzy software to process sensitive data.
- Fuzzy matching is the subject of numerous research projects and new algorithms/software are regularly published. Stay up to date.
- Fuzzy matching is the most cost-effective solution when you have a large amount of data that if matched correctly, it will make you a fortune, while small mistakes does not matter so much.
In YZR we provide you, with our no-code tool, the data normalization process in the easiest and most interesting way. Our tool uses fuzzy matching techniques to clean your data in a way that you want so that you can have the most reliable data analysis. Therefore, with no need of any technical skills to use our tool, you normalize and qualify your data.
Interested to know more? Don't hesitate to contact us.