· data engineering data quality elt

What Is Data Cleaning and How Do You Do It?

written by Bash Sarmiento


The world we live in is now largely dependent on data. Fields and industries ranging from finance, education, healthcare, and even entertainment all rely on data to streamline processes and maintain the required transmissions without losing data quality and security.

To make sure that every piece of data remains correct, usable, and consistent across your entire network, a process called data cleaning is necessary. To learn more about this emerging best practice in data management and how to do it, here is everything you need to know about data cleaning.

Understanding data cleaning

Basically, data cleaning refers to the process of handling data prior to analysis by removing or modifying parts of the set that are irrelevant to the application, incomplete, duplicated, improperly formatted, or outright incorrect. Aside from telematics and communications, data cleaning is also used in data engineering, data mining, and research–basically every field that uses data will have a use for data cleaning.

There are software solutions that help make data cleaning fast and convenient that are now available in the market. However, some parts of the process, mostly depending on the application and its requirements, still have to be done manually. Overall, these tasks are significant parts of data handling and management for every organization.

Not only does data cleaning remove common and virtually inevitable data errors and inconsistencies before compiling into a new dataset, it benefits the process and the entire organization. It saves up time that could otherwise go to troubleshooting or tracing root causes of even larger problems stemming from incorrect or inaccurate data. This, in turn, translates to productive time and equivalent costs for the company.

How to perform data cleaning?

Before you start with the processes of actual data, it’s important to first assess the entire data flow and the sets that you’ll be handling. Understand the purpose of the data being collected, processed, and analyzed and get an idea of what your goals should be.

As you try to define your goals and expectations for the project, you might want to prepare a set of criteria or metrics, or your own data cleaning checklist, to give you measurable goals for your upcoming effort. Once you have a good grip on what you want to achieve, you can start data mining by:

1. Identifying hot spots for error

As you monitor your data stream, you might notice recurring patterns of where errors come from. This narrows down your search and gives you a better idea of the sources of data errors later on. Once you encounter incorrect, illegible, or corrupt data characteristic of those earlier data errors you found, it’ll be easier to track and take down details about these information. More importantly, records are important when you start integrating your process records with other parts of your organization’s platform or management solution. Having a record of your data errors saves you the time from having to sort them out with data errors in a larger stream that usually involve other departments.

2. Set standardized processes for data accuracy checking

A standardized methodology for checking accuracy saves time for your entire department and makes your strategies accessible for the next members of the team, not to mention the opportunities it will provide for improvement and strengthening your processes. Define data entry points for your data cleaning process to limit your search and minimize irrelevant data.

For checking data accuracy, you can start by setting acceptable values for variations in your data. Better yet, you can invest in tools or even develop specialized machine learning tools to automatically check your data accuracy, make decisions based on previous occurrences, and inform you once triggered by specific events. Similarly, this kind of technology can help you scrub your data set for duplicate data, which skews accuracy reports by logging the same data multiple times.

3. Analyze the “clean” data

As much as possible, ensure that you have the proper tools and training to handle data that has been checked for errors, standardized, and cleared of duplicates. There are reliable SaaS solutions that can help you with it by reducing the time it would take to analyze data as well as reduce the risks of human error that usually comes from manually handling data.

Afterwards, you can now endorse it to the intended client or user, or compile it for documentation purposes. The clean data can also be used as an input for business intelligence and other relevant analytics for your organization.

Conclusion

Data cleaning is an important part of keeping your data streams correct, accurate, and relevant to the application they were intended for. Having it integrated into your organization’s processes could help improve your time and save you from fixing errors or finding out where incorrect data comes from.


About the Author - Bash Sarmiento

Bash Sarmiento is a writer and an educator from Manila. He writes laconic pieces in the education, lifestyle, and health realms. His academic background and extensive experience in teaching, textbook evaluation, business management, and traveling are translated in his works.


About Me

I'm a staff data engineer working to advance data-driven cultures by wrangling disparate data sources and empowering end users to uncover key insights that tell a bigger story. LEARN MORE >>



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