Data Scientist Journey Map
UX Techniques
Journey Mapping, Use Cases, Requirements Elicitation, Contextual Inquiry
Problem
While the journey of building predictive models are generally consistent with data scientists from all organizations, the Analytics team did not want to make assumptions. The team needed insight into the processes, operating environment, and tools used by the the bureau’s data scientists.
Solution
As the lead UX designer, I recommended spending half a day with data scientists to see first-hand what a day looks like for them (aka contextual inquiry). Prior to the meeting, I performed user research on data scientists as a professional. I documented the typical journey map and versed myself in their terminologies. By coming prepared with domain knowledge and having a typical data scientist journey map, we were able to quickly review and adjust the contents with minimal changes. On the day of our meeting, as I sat with data scientists, I began observing the files and tools they were using and asked questions, such as “why are you doing that?” and “how painful is this particular task?” At the end of our session, I created a report and provided a summary on how data scientists operate within the organization.
Outcome
I was able to identify additional data scientist personas since there wasn’t a single role that built predictive models from start to end. The data scientists broke out portions of the lifecycle to specific personas.
I was able to empathize with the data scientists by seeing first-hand their challenges with having to move from isolated machines from one location to another, as well as their challenges with “smoothing data.”
I was able to document the current suite of tools they currently use and what they prefer to use. This information helped the Analytics team with short-listing tools to evaluate as part of a standard tool set.
