Data cleansing is a process that involves the detection and correction of errors in electronic data, typically consisting of removing extraneous or inaccurate records from the dataset. Data cleansing may also involve reorganising records within a dataset to reduce redundancy, identifying incomplete information, identifying inconsistent information, detecting outliers inconsistent with the rest of the data set, or detecting duplicates. The overall goal of data cleansing is to improve accuracy and consistency in order to gain better insights into a company’s data and how it is used.
Data cleansing is the process of detecting and correcting errors in electronic data. There are two main purposes behind data cleansing:
- to improve data accuracy
- to reduce or eliminate inconsistency
Data cleansing can be done manually by reviewing each record for discrepancies or by using computer software designed specifically for this task. Programs made specifically for this purpose are called “data scrubbers.” Manual review can take more time than an automated review but will result in more accurate data. Data scrubbers are more efficient but may not be able to detect all errors.
Benefits of Data Cleansing
The benefits of data cleansing are many and varied. Here are just a few:
- Accuracy: The most obvious benefit of data cleansing is that it results in more accurate data. This means that decision-makers have access to accurate information when making important decisions about the company.
- Consistency: Another benefit of data cleansing is that it ensures consistency in the data. This means that all records in the dataset are formatted and entered the same way, which makes it easier to analyse and draw conclusions from the data.
- Elimination of Redundancy: Data cleansing can also help a company reduce the amount of redundancy in its data. This means that decision-makers won’t have to sift through as much info and will be able to make their best time and resources.
How to cleanse your database?
The first step in cleansing a database is to identify the various types of data that you want to cleanse. Each type of data has its own set of issues, and they will vary from company to company. For example, a typical online business might have customer order information, customer profiles or records, and leads. In this example, the three most common problems are incomplete records, duplicate records, and inconsistent records
With these points in mind you can move on to actually cleansing your database:
Incomplete Records: Manually look over each record for instances where something was not recorded or an incorrect value was recorded. You can then either modify the record or delete it altogether if it’s already been processed correctly.
Duplicate Records: As before, manually look each record over. This time you are looking for records that are exactly the same. Delete any duplicate records that have already been processed correctly and use just one of them for future processing.
Inconsistent Records: Again, review each record by hand. You will be checking to ensure that all fields have a value or that they are all blank. You can then make a note of where a problem was found and take any appropriate action, such as contacting the person or IT services department that entered the data in order to get it corrected.
The Common Problem faced in Data Cleansing
The most typical problem with data cleaning is caused by incorrect data entry. When the quality of the data is poor because of a number of mistakes made while people are recording it, it can lead to unreliable outputs and ineffective decision making. This is exacerbated when there are a lot of records and because human error is inevitable, it can be hard to catch all errors without a lot of tedious work.
Staff need to input data correctly and safely, especially if it is sensitive. It is important to remember that GDPR training is a vital part of data cleansing. Staff need to be aware of the importance of accurate data entry, the risks associated with data inaccuracy and the GDPR requirements for data retention and destruction.
Even though data scrubbers are designed with technological trends like automating the process they may not find every error that could slip through. It is important to remember that data scrubbing should only be one part of the data cleansing process and that proper data input should be every company’s top priority for effective data cleansing.
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