Panorama
Enterprise Tool • End to End • Data Visualization
Panorama is an internal tool designed for Uber to monitor, diagnose and resolve ride-sharing marketplace issues.
! Important: data in this page is for illustration purpose only and doesn't represent any real business numbers.
time
2019/2020
client
Uber
role
Lead Designer
data in this page is for illustration purpose only and doesn't represent any real business numbers.
Project Summary
Panorama is a brand new product that I designed from scratch in 2019. I was involved from the very beginning of the product planning, led the key design activities, and delivered the MVP version that was launched in Apr 2020.
Phase 1
Product Vision
Concept Design
Phase 2
Fundamental Research
Phase 3
Product
Definition
Phase 4
MVP
Design
Where it all started
What is our Product Vision for 2020?
As Uber evolves, our operation team's toolbox needs to catch up and support the expanded user needs. In 2019, we re-evaluate our product offerings and decided to craft a new vision. I co-hosted a 3.5-day Design Sprint to collect insight and generate ideas. After the Design Sprint, I led a team of 3 designers to develop high fidelity concepts and craft a storyline for the north-star experience.
Picture: slides from the Design Sprint and Concept DesignSlide (intentionally blurred)
What happens after concept design
Turn Vision into Reality
After pitching the vision to Uber's product leaders, this project got the funding and resource to build the product vision. We started from one of the concept directions and conducted fundamental user research to discover needs and pain points.
Research Outcome
3 Personas
3 Problems
01
Fragmented Workflow
Constantly jumping between many data sources, copy & pasting information, and resetting filters to obtain desired views hampers productivity.
02
Siloed Intelligence
Each existing tool offers only a slice of the domain, and altogether painting an incomplete view of the markets.
03
Insufficient Dimensionality
Deeper analyses are not possible due to the lack of suppor tin slicing and comparing the data in a meaningful way.
9 User Needs
Seasonality
Extended Data Source
Trust
Metric Standardization
Segmentation
Latency and Outage Info
Contextual Information
Data Comprehension
Reliability
Design Principles
Holistic
Standardization
Dimensionality
Framework
3 Tiers
Territory
City
Neighborhood
4 Segmentation Types
Product
Uber X
Uber Pool
Uber Black
...
Geo-location
City Core
Non City Core
...
Time
Month
Week
Day
Minute
...
User Cohorts
Loyal Users
Dual-Appers
...
3 Exploratory Analysis Modules
Time Series
Heatmap
Data Table
