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Implement a QA Process for Your Data

Implement a QA Process for Your Data

Testing a company’s data quality is an important key to success. It could help you save money and fix issues before they start affecting your bottom line. While it may seem like an overwhelming task, there is a process to break it down. To do that, you’ll need a deeper understanding of what data quality is and how it works.

What Is Data Quality?

Data quality is a measurement of information your company has accrued. Through metrics and feedback, you can ascertain whether or not the data is of good use.

Good data means better operations, noticeable improvements, and deep insights into your business. Bad ones often lead to a stall, or in the worst-case scenario, a regression.

With the right data quality, you’ll make accurate decisions. The advantage of using it is that it doesn’t have any inherent bias, meaning that you’ll be able to look at your business at face value. Data quality testing is crucial to ensuring that the source you’re basing your decisions on is the right one.

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Measuring Data Quality: The Six Dimensions

There are six dimensions to consider in creating a reliable base point for all data quality measurements. These metrics were first introduced in 2013 by the Data Management Association (DAMA). Using these metrics helps ensure data quality and reveals what needs improvement. The six are:

  • Accuracy: Accuracy helps determine if data depicts the actual transaction. Accuracy data should trace back to its sources and be as close to the actual transactions as possible.
  • Completeness: Data should have complete fields, especially in critical areas. For example, transactions should all have timestamps.
  • Consistency: Data copied from another source should be consistent across the board. Data should reflect the same number, zip code, or other information taken. Several data consistency metrics can work for this dimension.
  • Timeliness: Businesses should look into when they got the data and if it’s still relevant today. Timeliness ensures that one can make decisions accounting for present-day needs and calculations.
  • Uniqueness: Data should have a unique field to identify it. A customer should have a record in the business database to help reveal a trend with their online behavior.
  • Validity: In any data set, validity is a requirement. For example, if you were to ascertain data from different states, the data should use the abbreviation for each. If one breaks that code, then the data’s validity is affected.
 

What Is Data Testing in the Engineering Process?

In data quality testing, the involvement of the engineering team is essential to hasten the process. A data engineer ensures that each set measures well against the six dimensions. They handle all the technical aspects in gathering and checking its qualities. Engineers also work closely with analysts to validate data.

 

How To Test Data Quality

To maintain a standard for data accuracy, engineers rely on several industry tests. They investigate software, identify any problems, and check each aspect of the process. Testing is crucial in ensuring that data stays relevant. Many development teams use these for data testing regularly. Here are some of the most common tests:

  • Smoke Testing: These efficiently check the data pipeline and its features. They verify if every part is working and will try to troubleshoot if any problem occurs. The name comes from the way computer hardware underwent testing in the past.
  • Unit Testing: These tests check the code and identify issues within the functionality of the program’s basic features. For example, a quick test can reveal any information missing from data columns.
  • Integration Testing: Rather than testing components, integration testing checks the entirety. It ensures that the program's execution goes by without any issues.
  • Feature Testing: When new components or features become part of the program, tests must ensure it works. Not only that, but the new item must also gel well with the current infrastructure. It’s also a way to ensure a backup if the feature suddenly fails in the future.
  • Regression Tests: The regression tests check part of the code that should remain the same no matter what changes apply to the program. It’s often used before the launch of the program, covering all critical areas.
 

Data Quality Testing Strategy

Testing ensures that critical data has reliable and repeatable information. Tests help you cover all fronts and can reproduce results as needed. It’s vital to have a strategy in place to ensure that you leave no stone unturned. It acts as an anchor and direction for the entire process. Here’s what most testers use:

  • List Business Cases and Data Quality Requirements

    Reference all your business conversations and strategies before creating your data quality process. Use specific key performance indicators (KPIs) and data dimensions used throughout your cases. Combining them and listing them will help make the process easier.

    When it comes to your requirements, make sure they fall under the SMART qualities:

    • S: Specific
    • M: Measurable
    • A: Achievable
    • R: Relevant
    • T: Time-Bound
  • Prioritize Data Quality Requirements

    After listing all the necessary information, it’s now time to prioritize them. Depending on your goals, sort the data quality requirements from the most relevant to the least.

    For example, an AI program tries to determine the rate at which COVID-19 symptoms appear by duration. In this case, one of the priorities would be the symptoms and timeframes involved with them. Data tests prioritize these first before all other information.

  • Create Your Test Cases and Run Them

    After prioritizing, you’ll then work on matching the critical data with your highest priority requirements. This will involve many verification tests to ensure that it fulfils your KPIs.

    For example, the AI program verifies data from the first appearance of COVID symptoms and how fast it spreads. The tester will write steps on how the AI does so and then test using a sample database.

  • Create Data Boundaries

    From testing, you’ll then begin to create data boundaries. These help ascertain if there are any issues with the data’s quality. If they go beyond the limit, they are beyond the bounds of the six core dimensions. It acts as the endpoint for testing to help create a more stable program.

    In the example above, the names listed by the AI should have accurate inputs and outputs. The data tester checks information from the last two years and then runs the sets with the program to ensure that it fulfils all conditions. The tester can then set bounds using the data.

  • Include Negative Testing in the Plan

    Negative testing takes into account unexpected events that still coincide with your requirements. In the example above, the test now tries to account for other variables that may happen. The patient may have similar symptoms to COVID, but they may not have the virus at all.

    How can the program maintain its accuracy when false positives appear in this case? The tester takes this into account.

  • Monitor the Data Regularly

    Because of the immense volume of data, monitoring is essential. It helps keep data clean by removing redundancies and incomplete information. Even a simple check can reveal issues that the tester can then fix. For example, checking the database maintained by the AI shows missing information in the address field. The tester can then complete the data and make the necessary fixes.

    The issue may only be present in a specific region, but the tester may need to verify it. Testers check nearby areas and see if the problem persists.

  • Using the Results, Develop a Data Quality Improvement Plan

    A data improvement plan aims to fix any critical issues found with testing while also working on minor fixes. For example, if the AI showed numerous data gaps, the improvement plan should address them. A simple refresh of the software could fix the issue. Otherwise, there might be a deeper-rooted problem that needs to be addressed.

 

Conclusion

Data quality testing minimizes errors and omissions from any given data set. It helps businesses make informed and measured decisions. Regular testing and monitoring should always be a part of a company’s plan if they work heavily with data. Of course, not everyone is ready to handle these types of work.

If you need help with data quality testing, consult a pure play software testing company like QASource. We have the skilled professionals to test data against your company’s goals. It’s an investment to take to ensure that you can secure profits and make sound business decisions. Contact us today or request a free quote and get the data quality checks your company needs.

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Disclaimer

This publication is for informational purposes only and nothing contained in it should be considered legal advice. We expressly disclaim any warranty or responsibility for damages arising out of this information and encourage you to consult with legal counsel regarding your specific needs. We do not undertake any duty to update previously posted materials.