Browsing by Subject "data-driven development"

Understanding users’ needs and delivering solutions to them is a demanding task that is often based on guesses. Data can be a capable tool in making those guesses more educated, and more importantly, validating them. Developing software is expensive and doing so based on experiences or opinions imposes a big monetary risk. Continuous experimentation introduces an idea where data is used in a systematic manner to reduce these development risks by constantly validating hypotheses providing crucial knowledge whether the innovation is on the right path or not. There are some existing paths in the form of experimentation models, but implementing and adjusting one to fit your specific environment may be difficult. This thesis presents a case study on a mobile application and its journey to using data in the decision making process. We take a look if existing set of written and event data can be utilized and what are the limitations of them. The data reveals there are multiple uncovered lessons to be learned from. We then look at how to take a more systematic approach and apply continuous experimentation practices in the context of the application. Some initial steps along with an experimentation road map and further experiments are presented. We concluded that the key element to initializing continuous experiment practices is to start small and gradually build the knowledge of the team.