Times are stressful right now. There is an ongoing pandemic affecting people’s health and livelihoods. Schedules are messed up, kids are home. People who aren’t used to working remotely are fumbling through learning how to work from home. And then there are the normal stresses that aren’t taking a break just because there is a pandemic: arguments with family members, home repairs, student loan debt.
I recently read the book Design for Real Life by Eric Meyer and Sara Wachter-Boettcher. I discovered it because I read another book by Sara Wachter-Boettcher, Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech that encourages us to look at the technology around us and consider how it can and has negatively affected people. If you work in analytics or web design, I recommend both books. She approaches design more from the context of user interfaces and web apps, but there is a lot of applicability to data visualization.
What’s a Stress Case?
Design for Real Life encourages us to consider stress cases. The idea behind stress cases is to consider our users as full, complex, sometimes distracted, vulnerable, and stressed-out people. They aren’t just happy personas who always click the right buttons and share all of our knowledge and assumptions.
Certain use cases, often labeled as edge cases, often go unidentified or dismissed as too infrequent. User interface designers, including data visualization designers, need to train ourselves to seek out those stress cases and understand how our design choices affect people in those usage scenarios. This helps us to minimize harm done by our design. But also, as stated in the book,
When we make things for people at their worst, they’ll work that much better when people are at their best.
Design for Real Life
There is a toxic trend in tech that we developers make things for ourselves, valuing the developers and designers over the users. Designing for stress cases helps us change that trend. We need to value our users’ time, understand our biases as designers, and make features that match our users’ priorities. A very applicable part of this for data visualization is to consider all the contexts that usage scenarios might happen.
Stress Cases in Data Visualization
Whether you are designing a visualization to be embedded in an app, included in an online news article, or published in a corporate dashboard, you can design for stress cases.
Let’s think about a corporate HR report built in Power BI that shows a prediction of employee retention. HR and team managers may use this information to help them assign projects or give promotions and raises. This dashboard and the conversation around it may address information related to identity (race, gender, sexual orientation). The use of the dashboard may affect someone’s compensation and job satisfaction.
How can we design for stress cases here?
We should be asking if we have the right (appropriate and validated) data to accomplish our goals, not just using whatever we can get our hands on. We definitely need to consider laws against discrimination that might be applicable to how we use demographic data. We need to consider the cost or harm done if we allow users to see these predictions down to the individual employee rather than an aggregation. And we should always be asking what actions people are taking as a result of using our dashboard. Have we considered any unintended consequences?
We want to use plain, easy to understand language. We want to explain our data sources and (at least high-level) how the predictive algorithm works. And we need to explain how we intend for this dashboard to be used. Basically, we want to be as transparent and easy to understand as possible. This dashboard can affect people’s livelihoods and happiness as well as the operational and financial health of the business. These data points are more than just pretty dots – they are people.
We also want to make sure our dashboard is easy to use. Think about digital affordances. We want it to be clear what happens when someone interacts with it. We can get fancy with bookmarks and editing interactions in a Power BI report. But does our intended user understand what is shown when they click a button that loads a bookmark? Can the intended user easily get what they need? Or do they have to jump through hoops to get to the right page and set the right filters?
Let’s say Sarah is a new manager at an organization that is trying to improve a high attrition rate, and she needs this information for a meeting with her boss and peers. She’s sitting in an online meeting using a 3-year-old tablet with 10 minutes of remaining battery life. Her two-year-old child is running around in the next the room. The group is discussing whether they should let some people go and then try to rebuild their teams. Can Sarah easily pull up the dashboard on her tablet and set the correct filters to see attrition numbers and retention factors for her team while trying to put on a brave face in front of her colleagues as she races against the tablet’s remaining battery time?
Visualizing the Coronavirus
Currently, it seems everyone in the data community wants to visualize the spread of COVID-19. Just look at the Power BI Data Stories Gallery. I get the appeal of using new and relevant data for practice, but consider your audience and the consequences of making it public. Do you trust and understand your data? Have you considered how the chart types and colors you choose can misrepresent the data? Who is it helping for you to publish your visualization out into the world? What message is your visualization sending? Is it unintentionally adding stress for your already stressed-out twitter followers? Most of us are not working with city officials or healthcare practitioners. We are visualizing this “for fun”. People are showing off Power BI/Tableau/D3 skills and not necessarily communicating clearly with purpose.
Your twitter friend/follower Frank is really concerned about the pandemic. His daughter lost her job at a restaurant. He’s worried about lining up projects for the next few months. And his wife is immunocompromised and in a higher risk group for getting COVID-19. He feels scared and helpless.
Your friend Dave is worried about his mother who lives alone a few hours away from him. He is bombarded by messages every day that say older people are more at risk of getting COVID-19. His mom says she is fine, but he wants her to come and stay with him. It’s all he’s thought about all day.
If you know nothing about epidemiology and don’t understand the response from various national, state, and local governments in your data, and you publish your viz with a choropleth (filled) map covered in shades of red, how will that affect Frank and Dave and everyone else who reads it? Did you add value to the COVID-19 conversation, or just increase confusion and fear?
Amanda Makulec published Ten Considerations Before You Create Another Chart About COVID-19 with some good advice, including the following.
To sum it up — #vizresponsibly; which may mean not publishing your visualizations in the public domain at all.
Stress cases can be related to a crisis, mundane technology failures, or just situations that are stressful in the context of the user’s life. If you are visualizing data, remember there are people who may be interacting with our visuals in less than ideal circumstances. We need to design for them as much as for our ideal use cases.
If you have real-life examples of designing for stress cases in data visualization, please share them in the comments or tweet me at @mmarie.