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Data Cleaning Tools and benefits

Nithyasri S May 06, 2021 | 12:30 PM Technology

Data cleaning is the crucial process of identifying and resolving broken, inaccurate, or unnecessary data. Data defects include missing numbers, misplaced entries, and typographical errors. This critical data processing stage increases the uniformity, dependability, and usability of a company’s data.[1]

Figure. 1. Data Cleaning

the process of correcting and deleting inaccurate records from a database or table.

The top 7 data cleaning tools

  • OpenRefine
  • Trifacta Wrangler
  • Winpure Clean & Match
  • TIBCO Clarity
  • Melissa Clean Suite
  • Data Match Enterprise
  • Drake[2]

benefits of data cleaning

“Better quality data impacts every activity that includes data. Almost all modern business processes involve data. Subsequently, when data cleaning is seen as an important organizational effort, it can lead to a wide range of benefits for all. Some of the biggest advantages include

  • Streamlined business practices: Imagine if there are no duplicates, errors, or inconsistencies in any of your records. How much more efficient would all of your key daily activities become?
  • Increased productivity: Being able to focus on key work tasks instead of finding the right data or having to make corrections because of incorrect data is essential. Having access to clean high-quality data, with the help of effective knowledge management can be a game-changer
  • Faster sales cycle: Marketing decisions depend on data. Giving your marketing department the best quality data possible means better and more leads for your sales team to convert. The same concept applies to B2C relationships too!
  • Better decisions: We touched on this before, but it’s important enough that it’s worth repeating. Better data = better decisions.[3]
References:
  1. https://www.marktechpost.com/2022/02/20/top-data-cleaning-tools-for-data-science-and-machine-learning-projects-in-2022/
  2. https://www.analyticssteps.com/blogs/top-7-data-cleaning-tools-2022
  3. https://research.aimultiple.com/data-cleaning/
Cite this article:

NithyaSri S (2022), Data Cleaning Tools and benefits, Anatechmaz, pp. 41

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