Data Cleaning: What is it?

Data cleaning. Data cleansing. Or, data scrubbing. Whatever you choose to call it, going through this vital process is essential to the success of any data-driven organisation. 

Even more so when ensuring that your data is both prepared and optimised for the best insights, analytics and functionality that your tech stack can offer.

And it’s becoming increasingly evident just how important data and having a data-driven culture is, because according to Mckinsey Global Institute:

  • Data-driven organizations are 23x more likely to acquire customers;
  • Up to 6x as likely to retain customers; and 
  • Are 19x more likely to be profitable. 

However, as powerful as your data might seem, the results that you achieve depend on how good that data is. If data has all sorts of inconsistencies like incorrect formatting, corrupt files or duplicate items, then prepare to face an ever-growing mound of issues.

So What Exactly is Data Cleaning?

Having data at the center of any company’s decision-making requires a strong combination of multiple data sources. The more inputs you’re able to get, the better your predictions become.

But because the data comes from multiple sources and in different forms, there is ample room for error. With everything from XML, to CSV files, as well as text documents and spreadsheets, there is plenty that can go wrong.

Data can be duplicated or mislabeled. It can be incorrect, or broken. Any issues with your data leaves algorithms and models, and thus predictions, inaccurate and unreliable.

So to deal with all of that messy, chaotic data, data cleaning is a massive requirement.

The process is around fixing or removing any incorrect, corrupt, wrong format, duplicate, or incomplete data within datasets.

And while there are no one-size-fits-all solutions in the data cleaning process, as processes will vary from dataset to dataset, it is crucial to understand the effect that data cleaning will have on your business.

How it Works

Data cleaning is a process of preparing data for use by removing or modifying data that is incomplete, irrelevant, incorrect, duplicated, or wrongfully formatted.

With a variety of stages that data needs to go through before it can be considered clean, there are also different approaches, methods and tools for cleaning that data. 

Things like validity checks and understanding constraints surrounding data get first priority way before even removing errors and duplicates or converting data types.

Then you’ve got tools like OpenRefine, Drake and Cloudingo to help facilitate things like data transformation, deduplication and wrangling.  

Data cleaning isn’t as simple as erasing information to free up space for new data. It’s about finding ways to maximise a dataset’s accuracy without having to necessarily delete information.

Get Your Data Squeaky Clean

If your data is messy, chaotic, or simply not working – our teams can help with that. 

We have all of the data expertise you’ll ever need…

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Our DataOps Lead orchestrates quality Data Engineering best practices and expedites scalable data pipelines for rapid insight and the facilitation of machine learning and AI. His passion for Infrastructure as Code (IaC) through Terraform represents his inherent curiosity and enjoyment of solving real-world data problems.

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