Ana Sekerija

Software Tester at Infinum

“Shortly after acquiring a masters degree in Economics, I resumed work in the digital banking department of one of the largest banks in Croatia. Two years later, instigated by an interest in IT and fintech, I started a job as software tester in Infinum. Since then, I’ve worked on half a dozen mobile banking applications where I’ve learned a lot about manual and automated testing, financial industry and agile software development.

I strongly believe in the multidisciplinary approach to testing and the concept of continuous quality.”

Why QA & DA Need Each Other

Since we are living (and testing) in a data-driven world, to not use the data to help us create functional and user-friendly applications would be a huge disservice. Data collected on the app usage is often underused and left with the sole purpose of creating reports for the stakeholders. Every experienced QA already has hunches and feelings about app usage and potential problems, but when it comes to decision making and augmenting your opinions and ideas, hunches only are not enough – no matter how strong they are. Reliable data can be of great use here.

How do we truly know and understand the way users use our apps and avoid biases we are all prone to when looking at the same app for months? How do we select the most important parts of the app to test in situations of tight time schedule? How to argument your ideas on changes that you feel are needed to be made in the app? Data, when used wisely, can help answer all of these questions. Although, not using the data is a missed opportunity, using unreliable data can be the real danger in the process of decision making. That’s why QA is needed in all steps of data analytics – not only to use the final result of it but to make sure we are collecting data we need and that we are doing it the right way. Since quality assurance engineers are usually those people who know their projects inside and out, they should be included in the data analytics process from the very beginning – from defining the data we want to track and implementing the tracking tools, to making sure everything is being tracked the way it was defined and creating dashboards from collected data. While in the agile framework, continuous quality should be the whole team’s concern, no role is exclusively dedicated to it and the end-goal itself as QA. Building a beautiful software of great quality today requires more from QA than just making sure it is bug-free.

I strongly believe that the real value of QA reflects itself in helping well-informed decision making with valuable inputs acquired from both testing the app and analyzing the data collected. My talk will go in depth to clarify the interdependence of quality assurance and data analytics and the value of both in a fast-paced agile environment.