What do you mean by data cleansing term?

Data Cleansing reviews all business data to ensure it is formatted correctly and consistently, corrects as needed, or notifies end-user to address ‘dirty’ data if it does not meet the business standards. Occurs in data processing, step one of the data pipeline. Only clean data can move to the next step of the data pipeline.

Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. This data is usually not necessary or helpful when it comes to analysing data because it may hinder the process or provide inaccurate results.

Briefly, it’s an effort to take real data and address those problems to make a data set more consistent and complete.

With data becoming one of the most important assets for businesses, the need for accurate & clean data cannot be ignored. As data comes from several touchpoints, it may have duplicate entries, corrupted data, irrelevant data, etc. And so, to make sure that all these data errors are fixed and missing information is filled up, data cleansing techniques come into the picture. To define “Data cleansing is an important part of the data management process, which mainly involves cleaning up the dirty data by identifying errors, duplicate entries, and irrelevant content”. It also includes various steps like - filtering data/records, eliminating incorrect, obsolete entries, filling in the missing entries, and more. If you want to outsource data cleansing make sure the outsourcing company has scalable solutions.