Helping cancer researchers understand a sea of data

As a first designer assigned to the project, my responsibilities were User Research, Usability testing, Interaction design and Prototyping.

In the winter of 2016 I have joined Seven Bridges, maker of cloud platform for analysis of genomic data. I was brought as an interaction designer to a small design team consisting of UI designer, several front-end engineers and a few interaction designers. In that time, company numbered just a little over hundred people, and rapid growth has started (it capped around 300 by the end of 2017).

The fact that the Design team was so small provided an opportunity to help teams working on diverse set of problems. Some of the most personally rewarding were:

I contributed by providing user experience research, product design, prototyping, critique and generally by being as helpful as I could. The outcomes of this were twofold - externally user satisfaction was higher, products enabled them new ways of doing their work and there was less need for support. Internally I helped (alongside bunch of smart, considerate folks) make a common ground for different stakeholders in multidisciplinary projects - through time some discussions shifted from “everything is equally important” to “we need to optimize for primary use case X”.

As the Nietzschean saying goes - it also changed myself - I learn to break work and iterate better, pick the important fights and let the rest go and a whole new world of cancer genomics opened up, with its fight against cancer, 3 billion base pairs per sample, various types of research and opportunities.

Data Browser, as its creative name suggests, helps cancer researchers mine data relevant to their research. The flow researcher usually goes through consists of few steps:

One commonly used dataset holds about 250 thousand files, some 500 terabytes in size (512000 Gb). This data is tagged by relevant metadata - properties stating who’s female, Asian, smoker, what was diagnosed, what kind of treatment was done, etc. There are dozens, sometimes hundreds of these fields for each file, explaining it in details.

This process was also disconnected from the next step, analysis, that requires them to download the data (size varying from gigabytes to terabytes), set up complex tools manually and only then proceed with the analysis. Seven Bridges platform already provided cloud storage and analysis, and Data Browser was built to fill a gap before it. It started as a prototype build by two engineering teams. As resources were strapped and deadlines loomed closer, it had to be launched. Some time went by and it got praise for its power and a stream of feedback concerning it’s steep learning curve - people didn’t know how to use it. I was brought as a first designer to this team, to help understand what were the problems and how might we solve them.

Before redesign, Data Browser behaved and looked like this:

In order to understand it and the problem it was built to solve, I conducted interviews with different stakeholders - product managers, engineers and domain experts. This helped build the feel for the territory.

Together with a project manager we devised a script for usability tests - set of tasks that would help us answer questions we deemed important and explain users thinking about troublesome areas.

We run the tests with internal bioinformatics folks, who were kind enough to help us, and similar enough to external researchers role. The results:

Some of the main takeaways were that starting a search was a big obstacle, also exporting results for a further analysis was hard, it was visually similar to another Seven Bridges tools that had different way of working, etc. There were a lot of frustrations, concepts that were hard to grasp, controls didn’t behave as expected, in some cases there were different meanings for same signifiers, and overall experience was poor. The results of this study had an amazing effect on the whole team. Instead of discussing what ifs, team started discussing how to solve concrete issues that real people had. It was a great experience to witness and be a part of this transformation.

There was also a round of gathering feedback from other sources - tickets users reported, sales and training folk who had first hand experience in explaining and using it, and it was all gathered into a single place:

Before jumping to details, let’s compare the product before and after the redesign. During research I found that mental model people had was different than one provided by the product. The bottom example illustrates a semi-complex example - sequencing data from both tumor and normal tissue for same cases. This enables researchers to compare these tissues and find differences in the genome.

The second one went one step further and provided better structure.

Number of cases, files and other items that matched the filter was important and useful to researcher. However it was hard to get those numbers because of a few things:

This was improved by providing color coding and global refresh. I tried advocating for automatic refresh on every filter change, but due to backend limitations a sequential global refresh was best we could do. It wasn’t perfect, but it worked better than the original.

These cards had a dual purpose - they communicated the number of items and served as tabbed header for result details.

Data Browser introduced a foreign way for filtering that was unknown to users. A set of templates, called Example Queries, intended to show how to utilize its power.

Redesign addressed some problems users had with this feature. Templates were split from the queries saved by users, preview was added and few adjustments to the flow were made.

Back before the redesign, there was a constant stream of support request from researchers. After the redesign request dropped sharply and now happen only from time to time.

We’ve got a lot of positive feedback and praise for Data Browser from external users. Many internal people consider it one of the top selling points for the platform.

The modular approach used to build Data Browser enabled gradual improvement and building upon the foundation. Since the redesign there have been numerous improvements focused on onboarding, flexibility, search, ease of use, suggestions and guidance, which improved the experience even further.