Client: A leading investment bank
Q: Why would a bank need UX people in a data science project?
A: Banks are very risk adversed. The industry needs UX people to help them reduce costs and risks.
The problem to be solved
- Disparate tools and data
- Difficult process to get anything done
- Fear of overspending and exhausting resources
The reason data scientists are hired in the first place is to develop algorithms and build machine learning models. Yet 80 percent of a data scientist’s valuable time is spent simply finding, cleaning and reorganizing huge amounts of data.
Too much time spent on finding stuff = Wasting data scientists' time = Wasting money
- Many APIs are read-only and an iframe form built into the portal was considered as not robust enough
- Data available at MVP stage was not granular enough for the visualisation we proposed
- Stakeholder workshop to understand business needs
- Rapid prototyping with Sketch and Invision
- Data visualisation to draw attention to critical resources
- User research with different user groups
- Close collaboration with development team to create the best solution with design and technical constrains in mind
- Respond to shifting business strategy
- MVP delivery for UAT
- High fidelity prototype, envisioned user journey and other documentations handed off to internal teams for further development
(More assets coming soon)