MY ROLE
Product Designer
PRODUCT
B2B Dashboard
INDUSTRY
Data Science
Reducing food insecurity for families in San Diego
FEATURE 01
Operational Health & High-Alert Hotspots
High-level operational metrics surface immediate food insecurity needs — replacing 3 to 4 weeks of data gathering with instant, action-ready insights.
FEATURE HIGHLIGHTS
Quick health checkup
of the food bank’s operations together
High-alert view
of areas that are underserved
Direct view of the gaps
that require action and intervention
FEATURE 02
Food Distribution Trends & Forecast
Forecasting food insecurity 6+ months ahead to prepare for when demand strikes.
FEATURE HIGHLIGHTS
Choropleth map
shows geographic relationship of food distribution centers and insecurity levels
Forecast predicts 6 months ahead, to drive action proactively
Customizable filters
tailored to streamline reporting and budgeting for any department needs
FEATURE 03
Socioeconomic & Demographic View
The 5 “Whys” answered in one interface, understanding the “who” and “what” causes food insecurity.
FEATURE HIGHLIGHTS
Demographic breakdown highlights groups that are the most impacted
Rate parameter data provides correlation to levels of food demand and insecurity
THE PROBLEM
PROBLEM 01
All departments collected differently and inconsistently
PROBLEM 02
Food banks were overfeeding and underfeeding specific areas
PROBLEM 03
Food banks were taking action after food insecurity has impacted its people
RESEARCH APPROACH
We met with 8-10 members of the leadership team to understand their food distribution and reporting process. We noted the key frustrations they were experiencing and the pain points in the tedious process.
USER INTERVIEW INSIGHTS & REPORTING PROCESS
USER INTERVIEW INSIGHTS & REPORTING PROCESS
PRODUCT GOALS
We clarified the goal which was to make the data digestible starting with a high-level overview and making it flexible to be granular.
APPROACH
We categorized more than 40+ datasets to understand how we will visually layout the data. We initially used tableau to explore data visualization concepts, we found it was a large learning curve and limiting in the user experience and decided to work with developers to build the dashboard.
Initial Concept on Tableu vs. Improved Low-Fidelity
USER TESTING
Asynchronous and live meeting feedback: Our users carry leadership roles and only had a chance to meet them 1-2 times for 15 min, therefore it was challenging to receive thorough feedback.
What I’d do differently
Conduct 15-30 min smaller groups/one-one feedback sessions
If budget allowed: use a user testing tool like maze to screen recording users using the dashboard to accurately see their behaviors and interactions
Followed up on how food banks interacted with each other after using the dashboard
FEEDBACK 01
60% of users said they’d like to switch back and forth frequently between the frequency and level selections.
FEEDBACK 02
Both food banks requested that their branding be more prominent on the dashboard, to solve this I made the metrics more prominent in their respective colors and since my team (DSA) are the creators of the dashboard we applied our branding to UI.
FINAL SOLUTION + IMPACT
We established a long term connection between both food banks and now they correlate with each other to end hunger in San Diego
