It is a very common finding in big organizations that the huge amount of data that gets piled up each day contains high degrees of duplication as well. Duplicated data only adds to the misery of the analysts because they not only have to go through everything to find out bits that are repetitive but also have to remove them manually in many cases.
A very commonly used automated technique to perform data deduplication is known as Fuzzy Matching. You must have heard about fuzzy equality, which is a system where quantification is done to know whether two pieces of data are the same or different. To understand the concept in much more detail, and to know why it is necessary for modern-day organizations, let us take an in-depth look at fuzzy matching in this blog.
Fuzzy Matching is a computer-aided translation technique that enables users to compare and find a match between two sections of data or even single sentences. Generally, fuzzy matching is used to compare a section of data with the database to find a match, which may not be a 100% fit, but is still above the threshold level set by the application.
What this means for the end-user is that when you give an input to a fuzzy matching system, it will analyze a part of the information and then come back with a response in the form of the percentage to which it can use fuzzy logic to accurately translate the given data. Now, even if the accuracy of the data converted in this way is not 100%, the sense of using fuzzy logic for this purpose is to assist human translation or data comparison and eventually make the entire process easier.
So, the end result would be that the fuzzy translator will help you in accurately translating at least a percentage of the data that you have given as input and would also let you know that certain sections require human intervention, which needs to be tweaked by the users themselves.
Is Fuzzy Matching Even Worth It?
The first thought that would come into the minds of a majority of readers would be that the utility of such a system will always be questionable because there are chances that it will go wrong. There is no doubt in the fact that fuzzy translators, which use fuzzy matching, do not always help in 100% accurate translations, but their need and utility cannot be undermined.
One must clearly understand that a fuzzy matching system is not designed to replace a human translator completely, but its job is to assist human translators effectively. From that point of view, even a portion of the automated translation done using fuzzy logic is a good deal for the end-user. Users will be made aware of the parts where automated translation doesn’t seem to work well, and could then work independently to get the best possible outcome.
How to do Fuzzy Matching in excel
With a free Fuzzy Lookup add-in for Excel, users of Microsoft Excel can enjoy the benefits of Fuzzy Matching. The fuzzy matching algorithm used in Excel searches for words in a given data that have the same characters. Since deduplication is the main purpose for which people use fuzzy matching, the Fuzzy Look-up add-in for Excel allows you to identify duplicate rows in a single table, or merge similar rows between two different tables.
Since Fuzzy Look-up is not a standard Excel feature, you will have to install it separately. Once you have downloaded and installed the Fuzzy Match Excel look-up add-in from the official website, the next time you open the application it will automatically start displaying and would also be ready to use.
How To Do Fuzzy Matching in Python
FuzzyWuzzy is one of the popular libraries in Python that supports Fuzzy Match. FuzzyWuzzy is an open-source data matching library. It involves the evaluation of the Levenshtein distance to conclude whether two strings or sentences are similar or different. Levenshtein distance accounts for character deletions, substitutions, etc. to support this matching process. Similarity scores are generated based on the token_sort_ratio function if the two sequences are present in differing order.
KlearStack Data De-duplication Tools
KlearStack has prepared a state-of-the-art advanced OCR tool that is backed by modern-day advanced Artificial Intelligence methodologies. With the support of Artificial Intelligence, data extraction is done such that machine learning models scan the entire data set for any errors and duplications automatically.
Fuzzy match is especially useful when your ML models have been trained with clean data, however in the production scenario one can encounter bad quality images. These images might result in incorrect raw OCR results. This incorrect OCR strings if supplied as is to the ML models, then the predictions might be inaccurate. Hence fuzzy matching techniques are useful in identifying similarity scores between these bad OCR strings vs what one might expect those to be. This way, the final output that users obtain is highly optimized and perfect for analysis. Further, the self-learning capabilities of the models allow a greater degree of generalization also.
To avail the benefits of KlearStack’s OCR solutions for deduplication of business data, contact our representatives now.