Date cleansing is method of tracing the inaccurate data records and then rectifying them. The inaccurate records could be in a table, field and database. The data record errors could occur due to several reasons. It may be due to incorrect data entry by the user, or some technical bug in the storage system. The process involves identifying the incorrect data and then quickly replacing it with valid content.
Data cleansing is also famous as data scrubbing. It focuses on removing all the discrepancies in the records. Incorrect data records are also termed as dirty data. Thus the methodology emphasises on modifying all the dirty data. Different methods such as batch processing and data wrangling are used to perform the task of data cleansing. Once the correction is performed, the data set must be consistent and accurate with respect to other data sets in the database. The data cleaning can be done on individual and multiple data sets.
The next important thing is to learn about the kinds of data cleansing methods. It is equally essential to understand how to do data cleansing.
Types of data cleansing methods:
Manual data cleansing: Manual cleansing is performed when there are errors in simple data sets. It is executed by individuals who analyse a particular set of records, track the errors and then correct them. The corrections include rectifying the spelling mistakes, filling the missing values and finally performing verification for concurrency and consistency of the records. Redundant and unwanted data is filtered out in the process to make data accurate.
Automated data cleansing: This approach is preferred in complex operations. Here all the correction chores are executed using high grade computer software. Automated data cleansing is good approach for verifying complex data sets in relatively lesser time. After the correction is performed, the data sets are compared for consistency with other data sets.
When performing the data cleansing, it is necessary to consider the precision, integrity, accuracy and reliability of the data source.
Stages of data cleansing process:
The data cleansing process has different phases. It helps you figure out which parts of the data are incorrect and need immediate correction. Implementing these steps in right way, you can enhance the data quality significantly. Read below to learn how to do data cleansing:
Plan: Identify the data sets that are of utmost importance. Place validations on the data fields to ensure the data cleansing process is performed effectively.
Analyse: Deciding the priority of data to be cleaned, the next step involves determining the loopholes in the data sets.
Implement Cleaning Operation: Data cleaning functions are performed along with several validations to the new data entry. Missing data values must be entered.
The last step includes monitoring the data system. It is essential to keep-up-to-date the entire data records to ensure no discrepancies.
Data cleansing is crucial component of data based systems. Inaccurate data sets can cause severe problems. Thus it is mandatory to perform the data verification to avoid huge business losses. Underestimating it could end up in fatal losses and other adverse impacts.