What is Data Imputation in Data Engineering?

In the race to get cleaner, better data there aren’t any shortcuts.

Virtually every decision, output and process has become more data-driven than it’s ever been, which results in a burgeoning dependency on accurate, accessible data. 

Whether that’s for the enterprise or the SMB.

These extreme volumes of data that businesses hold onto and accumulate, drive the need for safety, accuracy and accessibility. Now, more than ever, having accurate information is important for every business. Big or small. Especially in an increasingly data-driven world.

In fact, without clean data you run the risk of atrocities such as lost revenue, mistrust and ineffective decision-making.

Especially where missing data is concerned.

That’s Where Data Imputation Comes In

Data imputation is a big part of data engineering and the data cleaning process, as a whole. 

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

Data imputation is one of the key processes in any successful data cleaning endeavour. It works by replacing missing data with substituted values to better retain the information/data of the dataset being worked with.

This can be done with a variety of methods, including mean imputation, k-nearest neighbours, and hot deck imputation. Using data imputation techniques are necessary because of concerns around biassing the dataset and incorrect analysis.

Why would you want to use data imputation?

There are 2 big advantages: 

  • Improves the accuracy of data analysis. 
  • Ensures that data is complete and consistent.

1) Improves the Accuracy of Data Analysis

When data is missing, it can be difficult to accurately analyse and make effective use of that data.

Data imputation can help to mitigate this problem by filling in missing values with substituted values, resulting in an overall improvement in the accuracy of data analysis.

And there are many benefits to having as much accuracy as you can muster:

  • Better decision-making. 
  • Improvements in your ability to identify problems and trends. 
  • Improvements in your products and services. 
  • More effective customer targeting. 
  • Better optimisations for your marketing efforts.

2) Ensures That Data is Complete and Consistent

Data imputation can help to ensure that data is both complete and consistent. 

This is because data imputation can fill in missing values with substituted values. Which can help to reduce the amount of time and resources needed to clean and prepare data for analysis.

These are some of the benefits of complete and consistent data: 

Lower Costs. Complete and consistent data can help to reduce the amount of time and resources needed to clean and prepare data for analysis. This is because complete and consistent data is easier to work with and requires less time and resources to clean and prepare. 

Improved Compliance. One of the benefits of having complete and consistent data is that it aids in compliance. 

This is because businesses can use data to track their performance against regulatory standards. Having complete and consistent data can help businesses more easily identify and correct any non-compliance issues, while improving their compliance posture.

Why is it important? Because you may face penalties from the government or other regulatory agencies. Which can be costly and will inevitably damage your business, as well as your reputation.

Nobody likes fractured customer relations.

Familiarise Yourself With Data Engineering

Data engineers are the backbone of any successful company. 

They lay down the foundations for analysing and stitching together information from many different sources. Then make it all work smoothly to provide solutions that solve the most unique and complex customer problems.

If you want to know more about data cleaning, data engineering, or already have a burning desire to become more data-savvy – then check out our blog, or contact us today! 

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