Data Cleansing also known as data scrubbing or data cleaning is a process of altering or in general terms detecting and correcting the given data from the storage to make it correct and accurate. Data Cleansing is the first step in preparing data for analysis by an organisation. Data cleansing can be achieved with the help of data cleaning tools or done traditionally. It can also be done interactively as batch processing or with data wrangling tools.
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No matter what is use of data or type of data it is, data quality is paramount. Inaccurate and inconsistent data can have impact on the end use of it. So, errors or inconsistency is solved by modifying, replacing or deleting the data.
The actual process of data cleaning may be to validate and correct the data against the known list or removing the small errors such as typographical mistakes. Data cleansing can also involve standardization of data against the given norm, enhancement of data by adding the relevant data, etc. As a part of data processing, any data redundancy or errors have to be cleaned. All these techniques are a part of Database Management System (DBMS), which helps in creating an informative, updated & accurate database for business.
Data purging is deleting old or useless data from the data-set. Data cleansing can also include deleting incomplete, old, or duplicate data but their focus is different. Data purging focuses on clearing space for new data, but data cleansing’s focus is on making the data in the system more accurate.
Some advantages of data cleansing are:
1. Removes major inconsistencies and error that are inevitable when data is being sourced from multiple avenues into one dataset
2. Ensuring and managing that the data is all clean can be beneficial for business and provide significant value.
3. Right data is the cornerstone of effective decision making.
4. Removing duplicate data helps business in streamlining the business practices
5. In data driven market clean and correct data of customers can be a gold mine.
6. Outdated data may lead you to choose wrong tactics, so to choose the appropriate tactics in business you need correct and up to data.
Some examples are as follows:
1. Corrupt Data: Cleaned with the help of historical backup
2. Inaccurate Data: Inaccurate data is usually the outdated data that was accurate when stored but with time becomes inaccurate according to the current standing. This can be corrected by updating the relevant data.
3. Completeness of data: Project supervisor may find the available data incomplete which can be completed by the third party data provider
4. Typographical errors: A travel website scans the reviews the and drop the reviews that have large number of typographical error or send the message to reviewer to update the error free review.
Hence, this concludes the definition of Data Cleansing along with its overview.
This article has been researched & authored by the Business Concepts Team. It has been reviewed & published by the MBA Skool Team. The content on MBA Skool has been created for educational & academic purpose only.
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