Data cleansing is the process of ensuring accuracy and consistency in a set of data. It involves correcting errors, deleting stale records, and filling in missing information. It is important for efficiency in data-dependent businesses and when merging datasets. It can be done manually or with computer programs. The goal is to minimize errors and make the data as useful as possible.
Data cleansing, also known as data cleansing, is the process of ensuring that a set of data is correct and accurate. During this process, records are checked for accuracy and consistency and, if necessary, are corrected or deleted. This can occur within a single recordset or across multiple datasets that need to be merged or will work together.
Simple process
In its simplest form, data cleansing involves a person or people reading a series of records and verifying their accuracy. Typos and spelling errors are corrected, mislabeled data is correctly labeled and filed, and incomplete or missing entries are completed. These operations often delete stale or unrecoverable records so that they do not take up space and cause inefficient operations.
Complex process
In more complex operations, data cleansing can be performed by computer programs. These programs can control data with a variety of user-defined rules and procedures. You can set up a schedule to purge any records that haven’t been updated in the previous five years, correct any misspelled words, and eliminate any duplicates. A more complex program might be able to fill in a missing city based on a correct zip code or change the prices of all items in a database to another currency type.
Benefits
Data cleansing is very important to the efficiency of any data-dependent business. If some customers in a database don’t have accurate phone numbers, for example, employees can’t easily contact them. If a customer’s email addresses aren’t formatted correctly, as another example, an automated email system wouldn’t be able to send out the latest coupons and special offers. The job of data cleansing is to ensure that the data within a system is correct, so that the system is able to use the data. Inaccurate or incomplete records aren’t much use to anyone.
Whenever two data systems need to work together, data cleanliness is even more important. If a company has two branches that work with many of the same customers, not only must the data in each branch be complete and accurate, but the two branches must also have matching data. When a customer updates their phone number with one branch, the other branch’s data must be updated with the same information to ensure maximum efficiency. Data cleansing works to not only make sure your data is accurate, but also that it’s consistent across different records.
Whenever a lot of data is stored, errors are bound to creep into the system. The goal of data cleansing is to minimize these errors and make the data as useful and meaningful as possible. Without this process done regularly, mistakes and mistakes can add up, leading to less efficient work and more complications.
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