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.
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.
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:
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.
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:
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:
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:
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.
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.
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.
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.
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.
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.
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.