What is Data Scrubbing / Cleansing – Data Science Jargon for Beginners

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Big Data / Data Science has a lot of Big Jargon. When I first started to explore data analytics I was amazed by the amount of data jargon that surrounded the industry.

A very important Data Science term to understand is a practice / technique called data scrubbing or data cleansing. You will hear this one a lot because it is important that the company you are pulling data for has the most accurate data possible to make essential decisions.

Data Scrubbing/Cleansing is the process of amending or removing data in a database that is:

  • Duplicated
  • Improperly formatted
  • Incomplete
  • Inaccurate

Within industries that are rich(full) of data it is know that there is a lot of inaccurate data that surfaces. The data analyst MUST search, clean, scrub, remove the inaccurate data to ensure that an organization can make very accurate decisions in their business plans.

If the data delivered to the organization is inaccurate the company could lose thousands or even millions of dollars. Data Scrubbing is a very important part of an organizations success in our world! It allows organizations to make decisions with a higher level of accuracy than ever before!

 

Make sure you are equipped to enter into the industry of Big Data. If you want to get a job in the data industry you must have these essential skills ===>

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