There are several tools available these days to get simply your company’s data matching process. The process is widely known as “matching” across data management, but is also popular as deduplication, joining, linking, etc. Therefore, matching is a method where you can find out a connection between two or more isolated data records.
Some of the examples would be like a relevancy within a household relationship. For example, associating two or more people living at the same address. You can also look for any individual relationship such as, finding two or more information linked to one person. You can find examples in other relations as well such as, franchise, corporates, product and customer associations.
Data matching is perhaps the most crucial process in data quality. Every other process results in finishing with standardizing, validating, and enhancing, and all of these are important for data. However, these are also the key constituents to the perfect matching process possible. So, how does data matching work?
What is Data Matching?
It’s basically, the process to identify duplicate/identical records in huge data sets. These identical records could be either individuals with several entries in one or more databases, or any details in stock systems. With this process, you can find out duplicates or likely duplicate entries, and further you can merge two exactly same entries into one. You can also detect non-duplicates with data matching which are equally important as you should know if the two similar entries are actually same or not.
How Does Data Matching Work?
Firstly, we must take some basic steps for efficiency. If there’s huge data like in millions, you need not match every record with every other record. This will not just be taxing to process but, will also not be sensible to compare such huge data considering that majority of the records has no connection with most of the other records. The easiest way out is to avoid this taxing process and rather categorize records depending on some basic common features.
For example, while for matching address you need not compare, you need match records from different states, you also need not match records from different area codes together to match records depending on name and phone number. Instead, just the zip code or the first character and the first consonant of the name should be good to remove duplicate data and compress the file.
Once we have created a logical set of records organized in a group that’s ready to compared with each other, we must identify which of the fields are sensible enough to find a match. This is something we have already spoken about earlier that is, the relationships. For instance, data for companies, households, individuals, and products.
Select the required fields to match based on what you are looking for. If it’s contact details then it will be possibly a mix of name, address, and topographical details. Usually you would like to match some finer details like street names, street types, or numbers, considering that these were already standardized during processing at the previous stage.
If we have already arranged data as per sectional center, then it does not require the state as a field to compare because they are already organized at a secondary level compared to state. To match products, we can use components like size, color, shape type, units, or any other descriptions.
While you are matching elements in a group, you are often using a kind of character comparison to analyse the spelling of two different information. So, for instance, if one of the records features the name of a person as “Janis”, and the other record says “Janes”, those are quite close when matched.
Majority of the matching data are vying for some common typo errors, missing letters, doubled characters, and so on. These differences are typical and are the common mistakes that keep records from getting matched with each other without applying the fuzzy matching skills that justify such typo errors.
There’s another thing to watch out for when matching data is the type and the length of the field. Both of these are explained with company names. A company name usually requires longer space which can include more specifications. In the process, you may also find abbreviations that are not yet standardized for instance, HTC vs High Tech Computer. Issues like these can be managed by standardization and others by more flexible matching standards.
Tips to Improve Data Quality
The most basic way to improve data quality is by making sure that they are rectified right at the start. However, most businesses manage data quality issues by modifying the analytics but totally ignore to alter the original database. So, when you are changing providers or seeking another system with the help of same data source, it becomes a problem. Luckily, these concerns can be amended by applying the below tips.
The three major steps to perform for improved data is to audit the data quality, incorporate data matching process, and illustrating a master record. Find poor-quality data from the data sources, compare (match) data by selecting multiple unique values to create an identifier and thus, lowering the number of duplicate entries and improving data quality, and, illustrate a master record using powerful analytics tool which is a database containing correct and latest values that can push for changes to other data sources featuring duplicate entries and other irrelevant information.
Quality of Results
Data quality alone cannot assure that the resulting data will offer any important business insights. To address this, companies must analyse the usefulness of data and the understanding of the software.
The entire purpose of data matching is to match algorithms in order to filter out the best quality data in the end. So, understand the process, focus on it and implement it right from the start, albeit using a reputed software that can help your business get the top-quality data.