Equipping users with insights about outcomes and representation

The community had spoken: insights about disparity and disproportionality were important for engaging in factual dialogue with their local officials. The data was inside Commons, but was difficult to locate and interpret. Something had to be done.

First, my team and I had to level set. What did the community know about disparity and disproportionality? We interviewed several users to understand what a new feature would require in order to be useful. Here’s what we discovered:

01

What do we know?

Users struggle to distinguish disparity and disproportionality

How could we provide an experience that serves as a learning opportunity for understanding the differences between these two concepts?

Interpreting data about disparity & disproportionality is difficult

Successful interpretation of disparity & disproportionality often comes down to having previous experience with data.

Effective data visuals are layered and provide necessary context

Provide a full picture of the data and invite the user to dig deeper to find the details that are most important to them.

Over the course of the next few months of iteratively designing and gathering feedback from my team, we worked out a solution that addressed each of the insights we collected during research.

02

Design Priorities

Present a comprehensive view of the data that invites the user to interact and make their own comparisons

Make the data users are looking for easier to access

Provide takeaways, summarize the data in a way that’s memorable, easily explained, and accessible

PLACEMENT

The disproportionality feature was picked to appear on a primary page inside the site to encourage discovery and utilize the revamped tabbed navigation for curated case flow stage data points.

DESIGN

The data visual was designed to provide an overview of recent data alongisde a breakdown of the county’s demographics.

Previous usability studies indicated that our UX for desktop users are great, but the mobile experience is not always great. The disproportionality feature was designed to dynamically respond to the user’s device in order to meet user expectations (tap or hover).

To Click or to Hover?

🎓 A learning moment

Our research showed that users were not often able to distinguish disparity and disproportionality. To fix this, I worked within my team to edit and create an experience that surfaces helpful information for the user as they interact with the data.