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

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 designed from scratch in 2019. I was involved from the very beginning of the product planning, 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. We hosted a 3.5-day Design Sprint to collect insight and generate ideas. After the Design Sprint, our team of 3 designers developed high fidelity concepts and crafted a storyline for the north-star experience.

Picture: slides from the Design Sprint and Concept DesignSlide (intentionally blurred)

Slides Blurred.png

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 Exploratory Modules

Data Table, Time Series, Geo-Map, Heatmap

4 Segmentation Types

Product, Geo Location, Time, User Cohort

More details available upon request.

© 2020 by Fiona (Muyao) Ding. All rights reserved.