Mike Brown, Chris Contolini, Seph Coster, Jonathan Crane, Clinton Dreisbach, Marc Esher, Mehgan Iulo, Dev Mehta, Misha Tepper
Center for Plain Language ClearMark Award Finalist, 2014
When it comes to working with data, people are ultimately interested in the story it tells. Some people want to hear those stories, while others want to tell them.
Since 1975, the government has published data about mortgages due to the Home Mortgage Disclosure Act (”Hum-dah”). These data can provide insight into economic issues such as discriminatory lending patterns. But with millions of records reported by thousands of financial institutions, working with the data is overwhelming.
My team and I modernized existing HMDA datasets into a “streaming data” platform that makes it easy for researchers to explore, filter, and fact-check with data. As one of the user experience designers on this project, I led the user research effort to identify our best audiences and their needs, and designed the user interface for our Explore the data tool.
Identifying and prioritizing audiences
I observed people in data-driven roles work using their current workflow to identify behaviors and common themes.
I identified 4 major workflow factors:
Level of technical detail. People with really specific research methodologies, like a statistician, vs. people who don't deal with numbers at all.
Level of subject-matter detail. People working on specific industry things, like a policy analyst, vs. people who have no previous knowledge of the mortgage industry.
The team decided to build for current HMDA experts (orange!), who have specific research methods and also work on a specific aspect of the industry.
However, we also wanted to help out advocacy organizations, journalists, bloggers, and any other people who could benefit from fact-checking or backing up their work with data (blue!).
Writing user stories
Taking the insights from needfinding sessions and beginning to describe
features that enhance the work of experts, and provide value to non-experts.
Two major tools evolved. One that filtered data, and one that grouped, sorted, and compared the data.
The UI was designed using insights form participatory design exercises with users, but also drawing upon principles from cognitive psychology.
Summary Table design
Summary tables produce the answer to many questions, but they also help users narrow down even more on the data they need to incorporate into their work–like a graph, for example.
Improving the UI for user flow and API stability
A year after release, I was able to tackle the usability issues I found with the original UI.
The work is still in progress, but using a lean UX approach, I've focused on quick, paper prototypes throughout the design process.