Starting a data science project is always challenging: you never know what data is available, what the results will be if any, and how useful or insightful the answers are. For that reason, we developed an approach to design, develop and test potential results within a short timeframe.
Our approach has a foundation in concepts from Design Thinking, Data Science and the Lean Startup to maximize results. The idea is to use validated learning as a process to develop a data science solution by means of prototyping with the end users involved. It incorporates data hypotheses, GDPR analyses, user interface design and a valid and pragmatic user validation study.
At the end of the Design Sprint, you know what a potential solution could look like, what data sources you could use, how useful they are for answering your business challenge, what privacy implications are involved and what other aspects you should take into account when you want to move forward. We will illustrate this in-depth with a practical example project.