Lead designer contributing to several observability projects for an enterprise cloud data services dashboard used by thousands of businesses globally. This is a reproduction of a 0-1 dashboard I worked to prioritize and streamline with 6 PMs, 1 UXD, and 9 Devs to scope out the MVP and deliverables within 3 weeks to present to leadership.
Google Dataproc
UX Design, Interaction Design, Visual Design,
AI Strategy
0-to-1 MVP
Problem Statement:
How might we streamline a troubleshooting dashboard for data professionals to find and solve observability problems quickly?
Image here of Dataproc platform’s customizable multi-dashboard data functions
Before
Dataproc is a complex Google Cloud platform that data professionals use to monitor when systems are down.
As Dataproc grows, dashboards become more complex. Dashboards may include multiple filtering and hard to understand data viz.
As workloads increase, Dataproc customers’ overall costs may spike.
Example of Initial Dashboards from Project
After
Developed and implemented 0-1 creation of a MVP dashboard to manage serverless batches and integrate AI in troubleshooting.
Example of Low-Fi Concepts here
Learnings
Reduce complexity. Users were overwhelmed by multiple types of data viz and did not understand distinctions.
Design for Engagement. Filtering by time frame was unclear and users did not engage with the double filter.
Example of A/B Test dashboard tested with real users
Next Steps
5 PMs were added to my pod after MVP and had multiple ideas to integrate Gemini AI. I drove UX strategy towards focusing on 1 clear way to integrate AI, use of visual icons, and
scoping the user journey into multiple steps.