Stop Visualizing Data!

You work in a small company that has a program to help consumers manage their health. Your basic product involves a mobile app for tracking daily events and a personalized dashboard. For a monthly subscription users can also get access to coaching and other resources.

There’s a meeting with a potential investor on the calendar and you want to use data to support your story that things are going well. So, you open up Excel and start digging through the data you have.

Finding the Story

You got some nice local news coverage back in March and you signed your first partnership in June, both of which resulted in a spike of app downloads. So, you look at that.


Well, that’s something, but it doesn’t really communicate the excitement of the last few months. You remember that a lot of those downloads in the spring never turned into even free accounts. So, you decide to look at new accounts instead of downloads.


That looks more like what you were expecting. Whereas the app downloads spiked in March, the new accounts hit a peak in July. Comparing the two graphs, you become curious as to how many new accounts were linked to the news coverage and the partnership, so you draw another graph.


This view makes it clear that by the time the July peak hit, the effect of the news story had died. The big spike in July was just the partnership. You kind of knew this, but it’s the first time you’ve seen a picture of it, which is pretty cool.

You remember that your company has a 20% download-to-account conversion target, and you want to see how many of these months hit that. This seems like a good situation for a scatter plot:


Wow. Comparing against the diagonal line that represents the 20% target, you can see July and August blew it away, while March and April didn’t even come close.

You note another promising detail on the spreadsheet. Not only are accounts up, but the percentage of accounts that are paid subscriptions is rising as well. This is good for revenue, which investors obviously care about.


You wonder how many of the paid accounts come from the new partnership, so you look at that.


Clearly, the partnership has been a great thing for your company. Armed with these insights you put together a nice summary in dashboard form for your investor. You add a few other interesting tidbits (you know from your market researcher that about two-thirds of your paid account holders are women) to make it visually interesting.


When you walk a few of your colleagues through it you get some nice comments—this is the first time some of them have seen all this information together like this—but when you present it the following day, your potential investor squints at the wall and tries to figure out what’s going on

Visualize Situations, Not Data

When you start by looking at the data you have and concentrate on how to draw a picture of it, it’s easy to lose track of the message. Overwhelming your audience with data is an easy trap to fall into. The person crafting a dashboard (or an article, or a presentation, or a web page) knows the content backwards and forwards and can unconsciously assume that the audience is on the same page.

A graph is a picture of a situation. The trick to creating a good one is to start by identifying a situation that your audience cares about. In some cases, you may know. Your investors probably care more about revenue (and projected growth) than they do about specific conversion rates.


This graph describes a situation that investors will understand: Revenue is going up due to a partnership, and more partnerships and more revenue are on the way.

Often you won’t know what situations your audience cares about, even when you think you do. A clinician who is monitoring a heart failure population may not need to know about her patient’s every movement but does care if he has become less active over the past few days. A credit card customer looks at a breakdown of his purchases out of idle curiosity, but what he really wants to know is how he can maximize the frequent flyer miles he earns by using his card. A patient doesn’t understand what her deductible is, but she does want to know which insurance plan is going to cost her less over the coming year.

It’s not fair to throw data at people and expect them to decode it. Just as with any design, effective data visualization requires you to understand the situations that are significant to your audience. By starting there, you can use data to describe something they will care about.

Useful Data Visualizations

There are a lot of ways you can visualize data, and there’s no shortage of best practices out there for making your charts and graphs. Best practices only take you so far, however. I’ll be talking about how a user’s context and goals inform useful data visualizations at the UXPA’s February meeting, next Thursday, February 11. Event details are here. Also: jokes. Hope to see you there!

View Jeff’s SlideShare Presentation

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):


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.