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Data Quality Analyst Job Description

the data quality analyst job description

Every industry you can imagine collects and uses data at some point in time.

There are different types of data that serves unique purposes. Some may be in the form of a customer database to predict shopping habits. Or it could be a log of symptoms and side effects of patients in a clinical trial to determine the safety and efficiency of a new drug. In a criminal investigation, timestamps on a clock-in system will determine if the accused had an alibi.

Some industries will need enormous amounts of data stored and someone to make sense of it all. Millions of numbers can be overwhelming if you have no idea how to use them. It is also a powerful tool that can make or break a business – especially if the data is not used accurately.

So, let’s take an in-depth look at exactly what your job will entail as a data analyst.

data quality analyst job description

Data Quality Analyst Job Description

Identifying the desired data

Identifying what data is useful and what is not can be detrimental to a business. Executive departments may have suggestions, but ultimately it will be part of your job to you to guide them to what data they should be gathering.

For example, one executive may want data on which product was the most popular that month, but it may be better to identify which products have the best profit margins.

Gathering data

This part may be easier later, as most companies will have a system in place for this. For example, a clothing shop may collect customer data with a royalty point card system. If it is a new company, they may look to you to set up a system that can be used to capture this data.

There are some computer software applications you will have to use for this, but that will be discussed later on in this article.

Cleaning data

As mentioned, mass amounts of data can become overwhelming. Inaccurate data can lead to disaster. For example, the marketing executive would like to send a text message to all the customers that signed up to the baby club regarding a baby product promotion and a free gift for each. To make use of the Customer database, they need to separate all the Baby Club customers from the rest of the customers.

Texting the Baby Club members only, instead of the full database, will be significantly cheaper. They will also have to make sure that all the numbers are correct. You won’t know if someone has changed their number, but you will at least be able to see if a number has a digit missing.

When data gets duplicated…

Sometimes, data gets duplicated. A customer lost their shopper card and signs up for a new card with an all-new account rather than asking for a replacement. A large chain store may have 15,000 Baby Club members, but the duplicate entries can take that number to 18,000.

If the system is not monitored for duplicates, the marketing department may purchase 3,000 unneeded “free gifts.” This would be a waste of resources. This same example can be applied to any industry – patients, employees, etc.

Analyzing the data

You will now have to analyze the data to get to a conclusion. You may see a decline in certain sales or a rise in new symptoms of patients.

Or say now, you have to analyze data for a criminal investigation. You can spot that the accused in a specific case always leaves at a specific time. But on one specific day, left 40 minutes earlier. Your analysis of the data will bring you answers to specific questions that you will have to present.

You can even tell what a customer wants…

It may be obvious in some instances that patients buy a new product because it went on sale and it happens to be popular. Then, over time, the product just stops selling, and no amount of discount will push the product. You may notice that with time, new information, new developments, and better products have reached the market, and the old product is now outdated and no longer trusted.

This sort of thing tends to happen as target demographics’ values change over time. Many products were extremely popular a few years ago that are no longer marketable because they are harmful to the environment or they are tied to businesses with unethical business practices.


You will have to present your findings to the decision-makers by using charts, figures with percentages, and reports. This will help them to decide on what to do next, to either remedy a situation, nuke a certain product or even adjust their company policy.

What Skills And Qualifications Do I Need?

Remember those software applications mentioned before? These are extremely important. Depending on the industry you are in, you will need to be proficient in some of the following applications:

  • Microsoft Excel, Power BI
  • Google Sheets
  • SQL
  • Tableau
  • R or Python
  • SAS
  • Jupyter Notebooks

Depending on the company, you may be required to have a tertiary degree. This can be with a major in mathematics, statistics, business, economics, or other relatable fields. Some places may just require you to have the skillset or experience in the field.

Become More Proficient In Jupyter And Python!

Many software applications are crucial in data analytics. We’ve decided to concentrate on Python and Jupyter specifically with these informative online handbooks.

Firstly, we do have a selection for a more general insight with Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information or The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight, the Data Analytics for Organisational Development, and finally Data Quality: The Accuracy Dimension.

Next, for Python users, we recommend Python For Data Analysis: A Complete Crash Course on Python, or perhaps Practical Python Data Wrangling and Data Quality, and Python for ArcGIS Pro: Automate cartography and data analysis, as well as The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python available in 2023.

Lastly, are our Jupyter guides such as the Jupyter Notebook 101, or alternatively the Jupyter notebook, Data Science with Jupyter, the Practical Data Analysis Using Jupyter Notebook, and of course Learning Jupyter 5: Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab to really hone in on your skills.

Final Thoughts

The field is extremely lucrative. The average salary for a data analyst can easily be up to $65,000 per year. The demand for this job is on the rise as well, especially in the US.

If you can pay attention to detail, have excellent critical thinking skills, and can spot patterns easily, you may be well suited to become a data quality analyst.

All the very best with your career as a Data Quality Analyst!

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About Wendy Young

Wendy runs an employment agency with her husband, Ian, in Rochester, New York.

She loves nothing more than hosting a good dinner party and spends weeks intricately planning her next 'event.' She often uses these to introduce clients to potential employers in a relaxed, informal fashion. The food must be delicious, the cocktails and wine must be a perfect match, and the decor needs to impress without being over the top. With all that going on, it's amazing that she gets any time to write about her thoughts on securing the dream job.

They live on the outskirts of New York with their poodle, Princess.

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