Data cleansing, also known as data scrubbing, 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 they are either corrected or deleted as necessary. This can occur within a single set of records or between multiple sets of data that need to be merged or that will work together.
Simple Process
At its most simple form, data cleansing involves a person or persons reading through a set of records and verifying their accuracy. Typos and spelling errors are corrected, mislabeled data is properly labeled and filed, and incomplete or missing entries are completed. These operations often purge out-of-date or unrecoverable records so that they do not take up space and cause inefficient operations.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 check the data with a variety of rules and procedures decided upon by the user. A program could be set to delete all records that have not been updated within the previous five years, correct any misspelled words and delete any duplicate copies. A more complex program might be able to fill in a missing city based on a correct postal code or change the prices of all items in a database to another type of currency.
Benefits
Data cleansing is very important to the efficiency of any data-dependent business. If some of the clients within a database do not have accurate phone numbers, for example, employees cannot easily contact them. If a clients' email addresses are not formatted correctly, as another example, an automated email system would be unable to send out the latest coupons and special deals. 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 are not much use to anyone.
Whenever two systems of data need to work together, data cleansing is even more important. If a company has two branches that work with many of the same customers, not only does the data in each branch need to be complete and accurate, the two branches also need to have matching data. When a customer updates his or her phone number with one branch, the data at the other branch needs to be updated with the same information to ensure the highest efficiency. Data cleansing works not only to make sure that data is accurate but also that it is consistent between different records.
Any time a lot of data is being stored, errors are bound to creep into the system. The goal of data cleansing is to minimize these errors and to make the data as useful and as meaningful as possible. Without this process being done regularly, mistakes and errors can add up, leading to less-efficient work and more complications.