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What is data cleansing and processing?

 

Data cleansing is a part of processing wherein it is ensured that the data is correct, consistent and useful. This process involves detecting and filtering errors or corrupt data entries, missing space, incomplete data typos and other related inconsistencies. These are all corrected and then, the data are transformed in a usable form or make it ready for analysis, research or any other business purposes. This is called processing. In the data world, a data cleansing and processing services company covers every step from capturing, web extraction, cleansing to quality testing and tailoring data format as per requirements. Today, these processes are the must-have for big data research, AI and data science. 

In short, Data Cleansing Processing can be a combination of, but is not limited to the following sub-processes:

•                     Data Migration, which is all about data upload, capture, import, & export to the defined server/ cloud storage

•                     Data Collection, which lets you pool data from different sources like interviews, discussion groups, websites, or any sources.

•                     Data De-duplication, which deals with dupes or similar entries using TrustMaps, Druva Data Risilency Cloud etc.

•                     Data Verification means filtering valid datasets to use in marketing or email marketing through mailchimp or any other ones. 

•                     Data Normalisation ensures completing abbreviations using Table Analyzer, Normalizer or manually

•                     Data Appending makes your records complete by integrating contextual details like Name, Last Name, Email IDs, Zip Code, etc.

The foresaid sub-categories require a proper and well-defined data cleansing strategy. A number of outsourcing companies, like Eminenture come with unique plan & workflow, which is built around these steps:

Step1. Developing a proper quality plan by defining KPIs or Key Performance Indicators

Step 3: Data cleansing, which includes the aforesaid sub-processes.

Step 4: Data validation that deals with defining and assessing quality & accuracy

Step 5: Data appending and enrichment which ensures that the complete information is going to be delivered.

Step 6: IT security policy & measures for safe and secure transitioning of cleansed datasets in a requisite format.

Some useful links

Data Cleaning Steps and Techniques.

Top Excel Data Cleansing Techniques.

Data Cleansing: What Is It and Why Is it Important?


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