Thursday, July 31, 2014

love and hate for NYT graphics

I rarely find myself in front of a computer these days. My time has been overtaken by a tiny little man (related post), who insists on spending hours a day with me, sitting in a rocking chair, at least one arm rendered otherwise useless by cradling and cuddling (not a bad way to spend one's time, I must admit). Only in the past couple of days have I emerged from my lack-of-sleep haze to realize that it only takes one hand and my cell phone to reconnect with what's happening in the world via Twitter and Feedly.

It was during one such cuddling-and-catching-up session that I came across the recently published New York Times article, Gains seen for Medicare, but Social Security holds steady. To be honest, I'm less interested in the findings, but the data visualizations within the article caught my eye.

At first glance, the two visuals look really clean and well-designed. Still, I am initially a skeptic when it comes to looking at any data viz. I started out hating the two data visualizations included in the article, but with a bit of patience, my feelings morphed from hatred to... well... I guess we can call it love and hate. Let's take a look at the two visuals included in the article and do a little analysis of each.

Here is the first:


My initial thought was that, with time on the x-axis, the above should be a line graph. But I was too quick to judge: it's not exactly time that's being plotted, but rather the forecast for expected Medicare solvency at the given point in time. Given this, it makes sense to treat the points as discrete (rather than continuous) in a bar chart, as has been done above.

My next would-be beef was with the gridlines drawn across the bars. Gridlines often add clutter, bringing little informative value with them (and making the visual appear more complicated than necessary - related post). But the increments of 5 on the y-axis and coordinating gridlines allow your eye to do a bit of math without your brain really having to. The gridlines within the bars could perhaps be made a little thinner so your eye would still see them without the cluttering effect, but this is minor.

While it took a little time to like the above components of the graph, other design features were love at first sight: it's well-labeled with clear title, axis titles and labels, the words above the graph tell you what you are meant to takeaway while attention is drawn to this point in the data - the most recent forecast - via difference in color.

Now let's turn our attention to the second visual included in the article:


This time, I'll begin with the components I like. Again, the takeaway is clearly articulated via text. Everything within the graph is clearly labeled. But in this case, I'm having a hard time moving to full-on love. The background shading and gridlines - though I can understand the motivations for them - bother me. And the labeling within the graph just doesn't seem as clean to me as it could be from a placement standpoint.

I really wanted to remake this visual, but was unsuccessful in finding the data being graphed and not patient enough to take the time to eyeball it. When I was considering the design choices I would make (get rid of grey background and gridlines, change the forecast portions of the lines to dashed lines, label the series with both title and % change to the right of the 2023 projections), I read the takeaway at the top again and realized that I don't even agree with it. The callout says the forecast is for faster growth for Prescription drugs and Physicians, yet the slope for the Hospital line is steeper (faster growth) than the Prescription drugs line. I assume it's true that the increase over the entire period forecast for Hospital is 25%, as noted, but the forecast is for a brief reduction followed by rapid increase, so I find this description to be misleading. 

Based on the data alone, to me more interesting is the inflection point and subsequent forecasts for Physicians and Hospitals. Historically, Hospitals have accounted for the majority of cost, but this is projected to change, with Physicians expected to make up a bigger (and rapidly increasing) proportion of beneficiary cost going forward. Interesting. I wonder why that is?

Perhaps this is explained in the article. But my call-to-duty by the little man is bound to be soon, so rather than go back and read the article, this is where I'll wrap up today.

My hatred turned to love in the initial visual, but I failed to get there in the second case.

What do you think? What do you like about these graphics? What would you change?

Monday, July 14, 2014

lead with story

July is storytelling month over at the Tableau Public Blog; the following is a guest post I authored.

When asked to write a guest blog post for this month's focus on storytelling, I spent some time reflecting: if I had just a single lesson to share, what's the #1 piece of advice I'd give in this space? I'd boil it down to three simple words: lead with story.

It may sound counterintuitive, but success in data visualization does not start or end with data visualization. To resonate with your audience, you need to do more than simply show data. Attention and time should be paid to the context for the need to communicate: what does your audience need to know? What do they need to do? How can you make the data you want to share meaningful and memorable? Part of the answer is story. Stories resonate and stick with us in ways that data alone cannot. Purposeful story can bridge the gap between showing data and imparting information.

Now, if you're an analyst by training (like me), "leading with story" might strike you as a little off-putting. This can be an uncomfortable space for many. Often, this seems to be driven by the belief that the audience knows better and therefore should choose whether and how to act upon the information presented. In other words, that they should be the ones creating the story. I would argue this is rarely (if ever) the case: if you are the one analyzing and communicating the data, you likely know it best, you are a subject matter expert. This puts you in a unique position to interpret the data and lead people to understanding and action. So, while it may feel more comfortable to lead with the data, I recommend you fight this urge when it comes to explanatory analysis and lead with story.

To ensure you story comes across clearly, there are two lessons to keep in mind: 1) don't make your audience wait for it, and 2) don't make your audience work for it. Let's discuss each in a little more detail and then look at an example of these lessons in action.


Don't make your audience wait for story
Don't bury your story: lead with it! Too often, I see situations where the communicator of the information wants to take the audience through the same chronological path they took to reach their conclusion. In most cases, this is unnecessary. Rather, lead with the "so what" and then back up into the path you took to get there only if absolutely necessary. This way, you don't leave your audience wondering when you're going to get to the point and run the risk of losing their attention before you do.

When it comes to crafting the narrative arc, I recommend storyboarding. Storyboarding is perhaps the single most important thing you can do up front to ensure the communication you're crafting is on point: it establishes a structure for your communication. Write each of the main points you want to make on a post-it note. Then you can play with different arrangements to get the right flow that makes sense given your audience and what you want to communicate. Once you get the flow how you want it using this low-tech method, you can leverage Tableau's Story Points feature to create this same narrative arc with your data visualizations. For more on storyboarding, check out this blog post.


Don't make your audience work for story
Spend time making the story you're telling impossible to miss in your data visualization by leveraging visual cues to help direct your audience where to look. Without these visual cues, our audience has to do work to figure out where they are meant to pay attention. When we ask our audience to do work, we run the risk of them deciding they don't want to and moving on to something else, at which point we've lost our opportunity to communicate. Preattentive attributes like size, color, and placement on page/screen can be used strategically to signal to your audience where to look in the visual for evidence of the story you are telling. For more on preattentive attributes, check out this blog post.


Lessons in action
Let's look at a simple example applying these lessons (if you're a regular reader, you may recognize this example, as I've used it before). Imagine you work for a car manufacturer. You're interested in sharing insight around the top design concerns for a particular make and model. Your initial visual might look something like the following:



While the preceding view may work as part of your exploratory analysis (where you're looking at the data to understand what might be interesting or noteworthy), it can be improved when it comes to explanatory analysis (where you want to communicate those interesting or noteworthy observations to someone else) by applying the lessons we've discussed.

First, let's think about what story we want to tell and make that clear with words:



In the above, we've made clear the point we want to make via the statement above the graph. However, our audience has to do some work to see the evidence of those words in the data. Let's reduce that work by employing some visual cues to help direct their attention:



In the above iteration, it's clear where our audience is meant to look through strategic use of color. We can even take this a step further, continuing the narration and use of color to tell a story with the data we are showing:



In this example, annotation and strategic use of color are combined to turn a simple graph into something more. Lead with story: don't make your audience wait for it or work for it.

Here is the above sequence published on Tableau Public.

Leverage these lessons and Tableau's Story Points feature to turn your data visualizations into compelling stories!

Monday, July 7, 2014

and then there were four

Three may be a magic number, but my favorite number of the moment is four.

As in, we are now a family of four.


We welcomed Dorian Werner Knaflic into the world on June 23, 2014. You may recall the timeline that I posted after Avery's arrival. In comparison, this birth was pretty much the opposite experience (we had an appointment, walked into the hospital prepared for what was happening, baby came home from the hospital the same day I did). I continue to be amazed at the absolute perfection of this tiny being.

And because it wouldn't be a proper storytelling with data blog post without a data visualization of some sort, I'll share the following, created from some of the stats I've been collecting, both by hand and with my UP24.


A couple things are clear: Dorian is eating plenty, as evidenced by his steady weight gain since hospital discharge on 6/26. The longest sleeping stretch I get is typically the one preceding the first nighttime feeding (though there have been some nice stretches between that and the second night feeding as well). I was (naively) hoping that clear eating patterns would emerge, but we aren't quite there yet. In time. Surely there are other interesting insights to be drawn, however since I'm operating on a somewhat impaired brain from broken sleep, I'm not going to look too hard for those now.

Rather, let's focus on the cuteness of this little one...

Dorian Werner Knaflic
Born June 23, 2014
6 pounds 11 ounces