You’re probably using a test management tool as part of a quality assurance project. This means you are aware of the importance of using the right data.
Nowadays, testing and test management are done a bit differently because AI has joined the ride. However, although AI can make these processes more efficient, things won’t go as smoothly if you do not have the right data quality level.
AI-based testing data quality can make a massive difference in whether your software development project is successful or not. Even if you have the smartest AI models for test management, it will all be in vain unless you are using clean data.
But why exactly is the quality of your data so critical for effective AI test management? Let’s find out the answer.
Data Quality Explained
As part of a QA test management team, you know data quality is a huge aspect to pay attention to. It represents the measure of cleanliness, timeliness, consistency, and completeness of data. To put it simply, it reveals how reliable a data set is.
When the data is accurate and has a high-quality level, it means there are no mistakes or issues with it. It includes all of the necessary information for the data’s intended use. The data is delivered on time and has details that are up to date. Moreover, it is even throughout the system and does not contain any contradictions.
Thus, the level of data quality has a massive impact on how effective the testing management process is. When it comes to AI-based testing, data quality ensures that test results are accurate and efficient.
Why Does AI Test Management Need Good Data?
Using good, accurate data is crucial in project management. That’s because AI influences the way your system looks at its surroundings. When you use low-quality information, the system will give inaccurate results or make unfair decisions.
What is worse is that this could lead to consequences that are hard and costly to fix. You would have to spend a significant amount of time repairing all errors. Eventually, your system will become unreliable.
For instance, AI is commonly used in fields like finance and healthcare. This technology is an important part of the decision-making process. So, low-quality data can result in unsuccessful outcomes. There might be wrong financial decisions and even incorrect diagnoses, which would negatively affect the lives of those involved.
But when you have good, diverse, and relevant data, AI can offer fair results. This means that your test management process would also become more reliable.
To ensure good data for AI-based test management, you must gather, clean, and organize the info accordingly. This makes it possible to train the AI system and help it offer accurate results.
To make sure the quality is maintained over the years, teams must regularly keep an eye on this info and perform maintenance. This can prevent bad data from infiltrating the systems and lead to unwanted outcomes.
Examples of the Impact of Bad AI Data
Using the wrong data can lead to small or huge issues, depending on the case. But if you’re a newcomer and you don’t think it can get that bad, there are a few examples that will prove to you just how far things can go.
Microsoft’s Tay chatbot incident is one of the best examples of what can happen if you don’t monitor and organize the data used for AI.
Many years ago, Microsoft launched the Tay chatbot. The goal was for it to interact with Twitter users in a fun, entertaining way. However, everything changed when Tay learned some offensive language and began to post some of the worst tweets. Microsoft had no choice but to shut down the bot only 16 hours after launching it. This is just a glimpse of what can happen when you do not filter data properly.
Amazon also had an issue with an AI hiring tool it implemented. This technology was supposed to find the most talented individuals for the hiring process. However, because it used old hiring data that was advantageous for men, it was biased against women. As a result, Amazon was forced to stop using this tool.
How to Ensure High Data Quality for AI-Based Project Management?
When using AI with a defect management tool, the quality level of your data can make a massive difference. Here are some things that can ensure data quality when doing AI-based testing:
- Make Sure the Test Data Selection Is Diverse
Many times, the data you want to use with your bug tracking software or test management systems is deeply flawed and biased. This is why you must carefully select every piece of information to train the AI model.
The data should reflect multiple user behaviors and scenarios. Diversity helps technology deal with a wide range of situations while avoiding biased results.
For example, when using an AI model for financial decisions, you should include different income levels, past financial behavior, and so on. In the hiring field, you must train the AI model to include different genders, skills, physical abilities, experience levels, etc. With enough accurate test data, you can easily prevent biases or offensive results.
- Collaborate with Multiple Teams
Don’t train your AI on data by yourself. The best way to do this is by collaborating with other teams. So, make sure that people from other backgrounds, functions, and roles take part in the data testing, development, and validation for your AI systems.
This approach will bring fresh, diverse perspectives. It’s a process that can immediately find any possible issues or biases in the information used for training your AI models. In the end, this can lead to better predictions and results.
- Cleanse Your Data
Do not feed any data to your defect management software or AI model without cleansing it first. Cleansing and normalizing information helps you find and get rid of any bad or duplicate details.
Data can be turned into a format that the AI system can read. At the same time, any inconsistencies and mistakes can be corrected. On top of correcting data, you must also normalize it. This means that you remove any biases that would interfere with analysis processes.
- Use Synthetic Information
Training AI models on synthetic data for test management is a good alternative. This is because it uses information generated by computer algorithms instead of real-world data. The information mimics real-world scenarios, so it can still be accurate and effective. Not only that, but synthetic data can also keep sensitive information private and secure.
The Bottom Line
Data quality is essential for effective AI test management. If you are implementing AI into your defect tracking tool or test management processes, you must make sure that you use the right information to train it. This helps you get rid of biases. At the same time, it ensures that your results are more accurate.
For higher chances of success, consider collaborating with multiple teams to bring different perspectives and points of view into the mix. Also, use a diverse test data selection so AI can see the bigger picture. Some other tips would be to cleanse and normalize your data and use synthetic information.
