How Might We… make a better world in just one weekend?

“It’s amazing what can happen in just three Earth rotations…”

This past weekend I was lucky enough to participate in the Twin Cities gathering of the Global Service Jam 2016, both as a coach and observer. A “service jam” brings together small, local groups to use design thinking techniques to brainstorm, research, and prototype completely new services inspired by a shared theme.

Friday kicked off with revealing the secret theme for this year’s Global Service Jam. “Jammers” were surprised to hear an audio clip of what sounded like someone (or something!) splashing into a pool of water. They then took out their Post-It notes and pens and started brainstorming things that the splash reminded them of; first individually and then as groups. Ideas were sorted into related themes and groups of two to four “Jammers” used the themes to create their preliminary “How Might We” questions.

Haven’t heard of a How Might We question? The term is used frequently in design thinking activities to describe a question that acts as a foundation for research and design inquiries. It describes the problem you are trying to solve, and is stated optimistically to reinforce the feeling that a good solution is possible. A How Might We (HMW) question is usually brief, allows for a variety of answers, and inspires ideation and creative thinking.

Here are three ways you can form great How Might We Questions:

  1. Refine the scope. It’s important to have a statement that sets helpful boundaries. Avoid questions that are so narrow that they shut down creativity (“How Might We build more community spaces for relaxation?”) or too broad (“How Might We redefine how people spend their free time?”). A right-size question leaves room to be surprised by your research findings and iterate solutions, but doesn’t feel overwhelming or unfocused. One team eventually settled on “How Might We remove barriers that keep people from finding peace and relaxation?” and after interviewing several users, decided to focus on one persona that seems to have the most barriers to relaxation: Millennials.
  2. Remove embedded biases and assumptions. By Saturday morning, another team had coalesced around the question, “How Might We raise awareness of individual water consumption so that people reduce their global footprint?” By writing down as many assumptions as they could think of, the team realized that they had started wading in to “solutioning” before even beginning their research. In order to identify the most effective ways to get people to reduce their global footprint, the team needed to be open to any number of solutions, not just the solution of “raising awareness.”  Another way to avoid type of assumption is to focus on the ultimate benefit or change you want to bring about. While it is natural to imagine the best way to get there, those perspectives should come later and be based on user research.
  3. Let the facts speak for themselves. On the other hand, do rely on available facts to inform the background of your user research. This same team also wondered if they had gone too far by assuming that individual water consumption has a negative environmental impact. They questioned whether they should do user research to determine causality. While asking users if they think their individual water consumption has an impact on the environment could be an interesting area to research, it’s not necessary to support this particular How Might We—this information has been proven through scientific research and is easily found online. The team decided to move forward, and their final prototype of the weekend outlined a campaign that began with awareness of consumption and then grew into a competition engaging communities, large corporations, and even governments.

I could not have been more impressed by Sunday’s team presentations. In just 48 hours the “Jammers” had become very comfortable with terms like “insights,” “personas,” and “failing fast.” Their prototypes were solidly based in research and they were able to articulate the needs they had uncovered and how they had iterated their solutions as they got more and more feedback. Not a bad way to spend a weekend. You can view all of the projects from the Twin Cities Service Jam and others around the world here.

Prototype from Global Service Jam

Prototype of a community to address the question “How Might We help millennials find more opportunities to relax?”

GSJ2016_1

Prototype of a five-part campaign to address the question “How Might We increase community members’ capacity to positively affect water consumption?”

 

Visualize Nothingness

By Jeff Harrison

It’s an exciting time to be me! If this email I got from LinkedIn is any guide, my career is about to really take off.

linkedin

Also, this email from my bank shows my rewards balance on this credit card remains at an all-time high. (I don’t know what “Earn More Mall Earnings” means but as someone who lives within a hypothetical short drive of the Mall of America I’m pretty stoked.)

rewards

To top it off, according to this visualization in ClassDojo, my kid is rocking Spanish class. The chart helps me see that all of the feedback from his teacher is positive.

class dojo

All these displays have one thing in common: underwhelming data. I do not actively promote my profile on LinkedIn [edited to add link to LinkedIn profile], and my son’s Spanish teacher never got into the habit of using ClassDojo to communicate with parents. I never signed up for the rewards program for which I receive the monthly grid of zeroes above; they just started showing up in my email a year or two ago. (The program is attached to an overdraft protection feature that Wells Fargo couldn’t figure out how to implement without issuing me a second debit card, which I routinely cut in half each time I get a new one.)

It’s easy to imagine the design reviews for these interfaces. Colorful charts! Insights! Engagement! When there’s a match between the data in these displays and what customers care about optimizing, magic happens: think of all the Fitbit users who consult their apps to monitor their steps and optimize their day for physical activity. The data contributes to a feedback loop, and more people take the stairs. However, when there’s a mismatch the displays aren’t motivating. They just feel kind of lame.

Do your user research. Get it right. And stop sending me notifications that suggest my life is somehow disappointing. Because LinkedIn and my mom would both tell you different:

allstar

The "So What": Telling Stories with Data


When we review a presentation before showing it to a client, someone often asks about the “so what.” Your findings seem reasonable, but so what? What do you want your audience to learn? What action should they take as a result?

If there are charts and graphs in the presentation, those also have to support the “so what.” You can’t just look at data and pick the optimal visualization. You’ll get better results if you first figure out the point you’re making, then design the graph as a supporting illustration.

The Storytelling with Data blog, written by Cole Nussbaumer, is not the only place to learn about good data visualization practices, but it has a more holistic view of communication than many. Yes, Cole talks about the pros and cons of bars, lines, and (shudder) pies, but she goes beyond that to discuss titles, labels, and other accompanying text, and how the shapes and the words come together to make meaning happen.

One of the reasons I like Cole’s blog is that she trades in small data. Her examples tend to feature manageable data sets that might inspire normal people to whip up a graph. So, when she announced a visualization challenge a couple of weeks ago the goal was not to inspire the kind of kinetic sculpture that big companies use to brand themselves as innovators. It was simply to improve upon a set of world population graphs published by The Economist.

While the challenge was to remake the visuals, the biggest problem with the original was the lack of a clear point. The text accompanying the graph was a laundry list of observations:

“The number of people will grow from 7.3 billion to 9.7 billion in 2050, 100m more than was estimated in the UN’s last report two years ago. More than half of this growth comes from Africa, where the population is set to double to 2.5 billion. Nigeria’s population will reach 413m, overtaking America as the world’s third most-populous country. Congo and Ethiopia will swell to more than 195m and 188m repectively, more than twice their current numbers. India will surpass China as the world’s most populous country in 2022, six years earlier than was previously forecast. China’s population will peak at 1.4 billion in 2028; India’s four decades later at 1.75 billion. Changes in fertility make long-term projections hard, but by 2100 the planet’s population will be rising past 11.2 billion. It will also be much older. The median age of 30 will rise to 36 in 2050 and 42 in 2100—the median age of Europeans today. A quarter of Europe’s people are already aged 60 or more; by 2050 deaths will outnumber births by 32m. The UN warns that only migration will prevent the region’s population from shrinking further.”

What I get from this is, there are going to be a lot more people. Okay. So what?

To me, the big story in the data was the massive projected growth of Africa, and the problems that it could spell for billions of people. Here’s what I made (click to embiggen):

swdChallenge

There were other interesting observations that could have been made, but I picked the story that seemed compelling to me and focused on that. The great thing, though, was that other people chose different points (for example, the shifting makeup of the world’s overall population, or how the projections fit with historical trends), and most of them improved on the original in some way. The variety of stories and approaches are on display on the round-up published yesterday, along with Cole’s critique.

Data can clarify, illustrate, and convince, but it doesn’t speak for itself. If you want it to support what you want to say, you have to figure out the “so what” first.

Working in Context with Axure 8

By Jeff Harrison

The Axure 8 beta dropped yesterday, and as always there’s a lot to take in. Some of the new features are pretty flashy: updated animation options mean you can finally flip and spin things—at the same time! There are tools for creating custom shapes, repeater updates, and lots of other improvements that will help you make fancier prototypes, if that’s your thing.

The changes I’m happiest to see are the ones that promise to improve the way I work in Axure by allowing me to work on things in their context, instead of having to switch to one mode where I’m editing in isolation, and then back to see the results of what I’ve done.

There are three examples of this that stick out at me right away.

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You Still Have to Do the Work

By Jeff Harrison

Here’s a common exchange when I’m talking to a prospective client (let’s call him “Steve”) about an Axure workshop:

Me: Tell me a little bit about how you see your team using Axure.

Steve: We’re using all kinds of tools today. Some people are using Visio, some are using PowerPoint. The designers are using Photoshop and OmniGraffle. It’s all over the map. Everybody’s stuff looks different. We have decided to standardize on Axure, so the purpose of this training is to get people up to speed.

Me: Okay, that makes sense. Is there anything you know you want to focus on?

Steve: I’m extremely interested in the custom libraries that Axure has, so we can all be working with the same components. We spend too much time reinventing the wheel today. I definitely hope that these libraries are part of the training.

Me: Sure, I can cover that. What are you doing today to try to standardize components?

Steve: As I said, it’s all over the map. We have no standards.

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Mourning Robin Carpenter

Today, we are mourning the unexpected and untimely loss of Robin J. Carpenter, a founder of Evantage Consulting and a partner, a leader, a colleague and a friend for everyone here and for many, many people outside our four walls.

Robin was a driving force behind Evantage since our inception in 1999 and shaped the organization that exists today.  Uncommonly smart, Robin pushed us all to do better for our clients and to always understand their needs and to put those at the forefront of every project.  Uncommonly kind, Robin pushed us all to be better people – to seek balance in our lives, to respect others and to always ask “How are you doing?”

Robin’s determined spirit and her desire to improve every day will always be felt at Evantage and we will honor her memory by continuing the work that she loved, pioneered and was so very proud of.

As details of a service or memorial become available we will post them here and on our Facebook page.

Miles to Go Before I Sleep

By Jeff Harrison

Graphs are pictures that help humans make comparisons and spot patterns in data. Using size, color, position, and other visual cues, a good graph is designed to support this analysis. Designing a good graph means going beyond simply dropping your data into a pair of axes. Making sure that the right comparisons are easy can make all the difference for your audience.

Here’s an example. My car claims to tell me how many miles I can drive before refueling:

gauge

I have 260 miles left on this tank. That’s enough to reach Des Moines—and freedom!

 

The number isn’t exact. To start with, it’s always a multiple of ten miles. This would still be precise enough to be useful to me if it were reliable, but the number also jumps around a bit. Sometimes it goes down by twenty miles when I’ve only driven five, and occasionally it actually goes up.

This variation makes sense when you think about it. Cars have different fuel efficiency ratings for city driving and highway travel. For most cars (mine included) the city number is smaller because every time you stop at an intersection you lose your momentum and have to burn extra gas to get up to speed again. (The city number is higher in hybrids, which use the battery for driving at lower speeds.) The projection’s uneven progress toward zero could mean that it’s based in part on my recent fuel usage. And, indeed, the manual confirms that the projection is based on fuel consumption over the last 19 miles.

That’s good to know, but it doesn’t tell me how far off the prediction is likely to be at any given moment. To put some numbers on it, I decided to collect some data. I reset my trip odometer to zero when I filled the gas tank. Then, every time I arrived at a destination, I recorded how many miles I had traveled, and what my projected remaining miles were. Then I dropped this data into a line graph:

remaining-miles

The initial estimate when I filled the tank that time was 430 miles. Practical considerations prevented me from driving until I ran out of gas, but it looks like I would have landed somewhere near there, or possibly a little higher. The gas light came on at 373 miles, when the projection read 60; it’s slightly off the trend because it came on between stops and I took note of the numbers at that point.

Make It Easier

As expected, the projection trend is not a straight line, but it’s kind of hard to estimate the variability with confidence. In order to do so you have to compare it against an imaginary diagonal line that represents what an ideal projection would look like.

Realizing this, I looked for a way to make it easier. Humans are better at making comparisons against a horizontal baseline than an angled one. Adding the miles already driven to the Y axis lets us plot projected total miles, which gives us that comparison:

total-miles

This graph makes it easier to judge the vertical distance from valleys to nearby peaks. The trend’s range—the vertical variation—over the left half of the graph is 60-70 miles. We don’t know what the correct final number would have been, but the spread alone is enough to convince me not to pay too much attention to this number when my tank is even half full.

Further Analysis

There is variation on the right side of the trend, too. These more gradual changes are much easier to detect in this version than in the first one. They suggest that the projection is still varying with my fuel consumption, but that the variation is less when the tank is emptier. This would make sense because having three gallons of gas gives you a smaller range of possible outcomes than does a full tank. However, a more gradual slope could also indicate a different driving pattern. Perhaps this last section contained trips that were more efficient than my typical errand-running but less efficient than my commute.

To shed some light on that, I looked back at my trip log and added an overlay to my trend to call out segments that were predominantly highway driving:

converging-estimate

This graph illustrates that highway driving reliably made the projection rise. In the first half of the experiment, highway trips pushed the projection to about 480 miles. Segments comprising many short errands and school drop-offs brought the projection back down. The graph also shows that I actually drove more highway miles during the second half of the tank, but that these periods of (presumed) higher fuel efficiency failed to bring the projection up to its earlier highs. The variation during the last rise is only about 30 miles. It’s difficult to measure this variation in the original graph, but in this one it’s easy.

In the end I decided to disable the projection, though I sometimes turn it back on when the gas light comes on and it’s not convenient for me to stop at a gas station right away. Another thing I learned from the manual is that the light is designed to come on when there are 2.6 gallons remaining in the tank, which tells me I do have some breathing room there. This may some day get me into trouble but for now I’m seeing it as a win, at least until I’m stranded in the woods with promises to keep.

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