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Why Data Cleaning Is Important

If you’ve decided on a career in data analysis, then chances are you understand what will be expected of you in your role. But knowing what you will have to do and how, or even why, are very different things. A large part of your role will involve scouring through reams of data, cleaning it up and drawing out key insights that answer questions or assist in next step planning. 

The data cleaning stage is essential to your role, so you must learn how to clean your data quickly and well. 

What is data cleaning?

Whenever you are presented with data to analyse, there will always be pieces of information that are:

  • Inconsistent
  • Incorrectly formatted
  • Duplicated
  • Incomplete
  • Irrelevant
  • Incorrect

These are not only useless but will damage the results of your evaluation if not removed or modified. Information like this is known as dirty data; therefore, identifying, amending or eliminating dirty data is known as data cleaning or cleansing.  

What are the benefits of data cleaning? 

There are multiple benefits to cleaning data, including:

  1. Peace of mind that your analysis contains useful information 
  2. Better decision making 
  3. Reputation protection
  4. Reduced waste
  5. Increased revenue
  6. Improved productivity 

Let’s dive into this in more detail:

Peace of mind

As a data analyst, you carry a lot of weight, and your opinion is critical. Poor analysis through utilising dirty data will result in poor decisions being made. These can have a financial or reputational impact on the business, which is why working from a clean data set is essential.

Better decision making

There are so many decisions that can stem from business data. Some include:

  • Altered marketing efforts
  • Product expansion, reduction or withdrawal
  • Recruitment plans

And much more. Without clean data, the business would ultimately be taking a stab in the dark, which would undoubtedly result in wasted spending, which has ramifications of its own.

Reputation protection

We all know that brand that sends email after email to our inbox that we really don’t care about. Hitting potential customers with content that has nothing to do with them is a surefire way to become labelled a nuisance. 

And it isn’t just emails, everything associated with the targeting and marketing of a business should be data-backed. Not doing so can result in the company being portrayed as out of the loop, annoying and worse. 

Reduced waste and increased revenue

This is particularly important for eCommerce businesses but can be true for service-based operations too. Clean data analysis means you only stock items with an increased likelihood of selling, the company saves money by foregoing projects that are statistically unlikely to succeed, and better hiring decisions are made. 

For service clients, it allows you to price accordingly and understand where your business is most popular. This can help service providers reduce marketing costs or even experiment with expanding their advertising reach with reduced risk of failure.

All of which increases revenue and stability for the company.

Improved productivity

There is nothing more disheartening than putting your heart and soul into a project and then watching it fail. Colleagues who know that they are working on a project with an increased likelihood of success (based on your data) often results in them working quicker and harder with less second-guessing. Really, your information is improving mindsets as well as practices.

What are the consequences of not cleaning dirty data?

If all of the above are the benefits of clean data, then the opposite are the consequences of working with dirty data. We can assume that making decisions based on dirty data runs the risk of:

  • Poor decision making 
  • Reputation damage
  • Increased waste
  • Lost revenue
  • Reduced productivity 

Is data cleaning hard?

The process of data cleaning itself is relatively straightforward, especially if you have an analytical mindset. As you start to learn and experiment, you may find that it takes a lot of time, but the more you practice and get to know the data you are working with, the swifter you will be until the process feels as though it’s second nature to you.

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