strategies for avoiding the slideument

A popular question posed to me (in general and several times just this week) is: what should I do when my deck is meant both to meet my presentation or live meeting needs and will also be sent ahead as a pre-read or afterwards as a follow-up?

This scenario often leads to a slideument—part presentation and part document, and not exactly meeting either of the above needs. The slideument is typically too dense to put up on the big screen and often not detailed enough for when you aren't there to talk through the material. I've written about this challenge before here.

Here, I'll suggest three potential alternatives for avoiding the slideument:

1. Create two different documents. Ideally, these two situations call for two totally separate work products: sparse slides for when you are talking through the material live and a denser, more detailed report-like document for the version that is disseminated. If it's something really important, consider whether creating two separate documents makes sense.

2. Leverage animation & annotation. If you're working in PowerPoint land (or something similar), you can animate a sequential appearance of elements on a slide, focusing your audience's attention exactly where you want it when you discuss the material live. Then the sent around version would have the final fully-built slide or be a version that annotates via text what you would say in the meeting or presentation. This blog post illustrates this approach using a specific example (from my book).

3. Make use of the Notes feature. If you're working in PowerPoint, make use of the pane below each slide that says "Click to add notes." Leave your slides sparse and put the narrative that you would say for each, or any additional context that is needed, in words in that notes pane. Just make sure to alert your audience of the sent-around-version to look there for details.

Do you have other ideas on how to address this challenge? If so, please leave a comment with your thoughts or suggestions on what you've seen work well.


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be gone, dual y-axis!

Due to popular demand (and a growing waitlist for the sold out workshop on 2/3!), I've scheduled another upcoming workshop in San Francisco on 2/8. Details & registration here.

I am generally not a fan of the dual y-axis. I've written about it before and offered some alternatives in my book and here. When scrolling through my Twitter feed last night, I came across the following offender and couldn't resist trying to improve upon it and taking part in the #MakeoverMonday challenge.

Before we get to the graph, let me spend a moment on #MakeoverMonday. This is a weekly segment that Andy Kriebel has been doing for quite some time (he's a busy guy; check out another fun current project of his with Jeff Shaffer, Dear Data Two). The premise is simple: take a less than stellar graph and make it better. In 2016, he's adding to the fun by doubling the Andys (having Andy Cotgreave join him) and also opening it up to the public. I think this is an awesome way to get people involved, share best practices and ideas, and generally inspire.

OK, back to the graph. The main challenge with a secondary y-axis is that there's always some questioning up front about which data to read against which axis. This particular example isn't horrible in that regard—the left and right orientation of Online hotel revenue and Number of travel agents titles, respectively, make it fairly clear which axis is which (though it does feel a little strange that each title is closest in proximity to the other data series, not the one it describes) . This graph does have another issue introduced by the secondary y-axis, however: the appearance of a crossing of the lines between 2005 and 2006. This looks like it might be something noteworthy, but actually is only a function of the scale used on the axes that creates a condition that they happen to cross each other at that point. Different scales would have them crossing in different places. I'd argue that they shouldn't cross at all.

In my workshops and book, when the topic of the secondary y-axis arises, I generally focus on two alternatives: 1) not showing the second (right-hand) y-axis but rather labeling the data in the secondary series directly or 2) pulling the graphs apart vertically so you can still leverage the same x-axis across both, but each gets its own left-hand y-axis so you can title and label them directly. Today, I thought I'd focus on a third potential alternative: turning the data that would be on two separate y-axes into the same units so you can simply plot it all on the same axis.

"Thought" in the preceding sentence is key. I'd envisioned my solution and penned the majority of this post before graphing the data (I've been doing this long enough that I should have recognized the danger in this). I thought I had the perfect solution in mind, but then graphed it only to recognize, "oops, that doesn't work." So let me rework the rest of this post. I'll take you briefly through my failed iteration and thought process as I do so.

Back to the idea of making the units the same and plotting it all on a single axis: this won't always be possible or appropriate, but I think (thought) it may work well in this case. For me, the point of this graph is that online hotel bookings have increased hugely over the past 15 years and that this has been—understandably—accompanied by a marked decrease in the number of travel agents. Since we're talking about increases and decreases here, one way to tackle would be to transform the numbers into relative increases and decreases and plot those directly. Here's what that could look like:

I hadn't looked at the numbers closely before graphing this, so failed to realize that the increase in online hotel bookings waaaay outpaces the decrease in travel agents. This totally makes sense now that I pause to think about it. But before seeing it, I was imagining a graph where online hotel bookings would be going upward to the right (as they are) and travel agents would be following perhaps a similar trajectory but downward to the right. The issue is that when scaled properly, the percent decrease in travel agents is totally dwarfed by the increase in online hotel bookings, so you don't really get a lot of value from the slightly downward sloping line (which also gets covered up in an unideal way by the x-axis labels).

Before seeing this, I was planning to discuss how moving from real numbers (for example, revenue or number of agents) to a percent (in this case, % change) causes you to lose something (sense of scale of overall numbers). I was then planning to go on to show a couple different ways to overcome this—first, by adding numbers to the graph directly, second by showing the actual numbers over time as well in bars (with number of travel agents being plotted in the negative direction) but pushing the bars to the background so they add a bit of context without a lot of clutter and maintain focus on the percent change. But none of this discussion makes much sense now that my original graph doesn't work.

Rather, after seeing the numbers graphed and recognizing just how huge the increase in online hotel bookings has been over the past 15 years, I'd be apt to just focus on that. The travel agent decline can become more of an interesting tidbit, included through use of text (not graphed at all). It isn't exactly the eloquent solution I was imagining, but it's where I'm going to land this time:

Be sure to check out Andy K and Andy C's respective solutions (here and here) where they go through more iterations and potential solutions for reimagining the original dual-axis graph. Interestingly, they both ultimately landed on scatterplots. For me, this does something strange to the dimension of time, but with adequate labeling (or animation, as Andy C uses), perhaps this is overcome. Take a look and see what you think.

By the way, if you're looking to hone your data visualization skills, consider participating in a future #MakeoverMonday challenge. I'll be following along. I hope to see your contribution there!


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connecting the dots

In this post, I feature an example before-and-after from a recent workshop and discuss the importance of connecting the dots for your audience.

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is there a single right answer?

I had the pleasure of lunching with Stephen Few last week. His books (particularly Show Me the Numbers) and overall approach to visualizing data has influenced my work in a big way over the years. We had an enjoyable conversation (and some amazing caramelized brussels sprouts!) at a lively Berkeley cafe.

We agree on a lot of things. We spent some time bonding over a shared distaste for pie graphs. In our work, we both promote clutter-free graphs that focus on the data, with sparing and intentional use of color to direct attention. We also happen to both be introverts who are passionate about data visualization in a way that leads us to break out of our respective comfort zones and share our learnings with others.

But there are certainly also areas of disagreement. Which is where the real fun comes in. As we were preparing to leave, Steve asked if I'd be open to discussing some of these areas. Of course! Friendly debate can help to refine one's point of view, articulate their logic, and perhaps even change their way of thinking. 

One area of disagreement is on the use of 100% stacked bar charts. Steve already wrote a great post on this that summarizes our conversation well, which you can view here (be sure to read the comments, where our conversation continues and others chime in with their viewpoints).

Through some of our back and forth on 100% stacked bars, another area of varying beliefs came up. It has to do with the existence of a single "right" or "best" answer when it comes to visualizing data, the topic of this post. Let me summarize our respective points of view and then I'll ask you to share yours.

I believe that data visualization sits at the intersection between science and art. There is absolutely some science to it: best practices and guidelines to follow. But there is also an artistic component. This means that two different people may approach the same data visualization challenge differently. There is room for diversity of thought and approaches. But it is important that we use our artistic license to make the information easier for our audience to understand, not more difficult.

My view is that we are often faced with tradeoffs when it comes to determining how we design our visuals. For example, I may choose a 100% stacked bar even though it makes it hard to directly compare two segments within the bar (assuming that is a lower-priority comparison and it allows me to still make my main point clearly) because with it, I get an implicit visual cue that the segments are part of a whole. I've decided that the tradeoff is worth it. Or I may include an annotation on my graph, even though it adds what someone else might consider clutter, because I believe it makes an important point in the data easier to interpret. I might choose sparing use of bright orange to draw attention to a negative aspect of the data because it comes from the given brand's color palette, while someone else might choose black or red for the same purpose. In these situations, none of these choices is necessarily the "right" choice. Different people will make different decisions. I think that is ok. For me, paramount is that design choices like this are intentional—I considered the tradeoffs and made a decision in light of those—not happenstance. Yes, they should be informed by science. But my view is there is also room for personal choice.

Steve's view is that the introduction of art into this conversation is a slippery slope because individuals will define art differently. He states, "there is no art in what I do; it's all science" and goes on to say that when he makes a judgement to design a data visualization in a particular way, he's basing that judgement on a fairly deep understanding of what works and what doesn't, which is rooted in science. That said, he concedes that there is a creative component but that this creativity and judgement should always be applied in an effort to create the most effective outcomes in understanding.

Given this, his view is that in a particular situation, there is a single right or best answer for visualizing data. In a comment on his blog, he poses the following question to me:

In your opinion, are some data visualization solutions better than others in a given situation or are they all equal in merit? If the former, on what basis can we judge one to be better than another? You mentioned that data visualization designers should “make the information easier for their audience to get at, not more difficult.”? This suggests that we can judge the merits of a data visualization by its ability to make the information as easy to understand as possible. Choices of graph type, colors, etc., are made to present the information as clearly and accurately as possible. If one data visualization does this better than another, it is the better solution–correct? If this is true, does it make sense to say that there is no “best” solution in a given situation among a set of proposed solutions?

I don't disagree that some data visualizations are better than others. We've certainly all seen examples of data visualizations that are inferior for a great number of reasons (bad chart choice, lack of clear labeling, too many colors, unnecessary clutter), while others are more effective. For me, though, it is possible to have multiple varying visuals that may be equally effective (or roughly equally, if the differences are marginal enough not to matter and all do a good job of getting the intended information across).

What is your view? Is there a single "right" or "best" answer when it comes to data visualization, or is there room for varying approaches? Leave a comment with your thoughts. I look forward to the continued debate!


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does your new year's resolution involve decluttering?

Happy new year! My, how time flies. This marks the fifth anniversary of this blog and also a time of year when I (like many others) tackle things like setting goals and cleaning things out (how two tiny boys can amass so many toys over such a short time period still has me in disbelief!)—in other words, prioritizing and decluttering.

On this latter point, I've been thinking about clutter in general. More specifically, about how we can relate clutter in life to clutter in data visualization. For example, growing up, I used to get in trouble cleaning my room. This sounds strange, right? But it happened because I'd be cleaning my room when I was supposed to be doing my homework. Reflecting back on this now, I realize that the room cleaning wasn't a delay tactic (as my mother assumed), but rather because I couldn't focus when the environment around me felt disorganized or cluttered. By decluttering my environment, I was better able to concentrate and be more productive and creative. If this is one potential impact of environmental clutter, what conclusions might we draw about the impact of clutter in our data visualizations for focusing our audience's attention and getting our point across? What is the impact of including the non-essential or non-informative? I have to assume it isn't positive.

What do you think? How does the presence of clutter impact the way we consume data? Are there connections we can draw between examples of clutter in life and clutter in data visualization? Is there anyone out there who experiences the opposite of what I've described, where you can actually concentrate better with clutter around you?

I'm planning on building out a standalone segment on clutter (similar to the one on color I did recently) and am interested in augmenting with additional research, topics related to clutter, and examples. Leave a comment or send me a note if you have examples (in life or in data viz) of clutter/decluttering or related topics I should consider including; any thoughts (whether researched or anecdotal) and examples (good and bad) are welcome!

If you're interested in the fruit of this labor, stay tuned here, where I'll eventually post the clutter segment for public consumption.

By the way, this is not the first time I've discussed clutter this time of year. For an oldie (but goodie!), check out one of my very first blog posts from January 2011, a new year's resolution: declutter your graphics, where I illustrate the impact of decluttering using a before-and-after example.

I'll wrap up with a pic of the aforementioned little darlings playing with some of their holiday lego loot. I hope your 2016 is off to a fantastic start!


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big think

On my drive home from dropping my son off at preschool in the mornings, I listen to NPR. It's my 15-minute daily dose of what's happening in the world (my husband likes to joke that other than this, I mostly have my head in the sand when it comes to current events). I've been enjoying a segment they are currently running as part of the California Report called simply, "Big Think."

In the Big Think series, they ask listeners to share their big ideas in 10 words or less. NPR usually plays a few of these ideas during each segment—wide-ranging ideas on topics as varied as drought solutions, moral philosophy in big business, and legalizing psychedelics—and then they also do deeper stories on a select few. 

When I was listening this morning, the connection between the Big Think concept and the Big Idea that I cover in my workshops (and book) dawned on me. The Big Think is your game-changing idea in 10 words or less. The Big Idea is the main point of your presentation or communication that you want to get across to your audience in a single sentence. In both cases, a drastic length constraint is imposed, which forces something very important: concision.

This sounds counterintuitive, but it is difficult to be concise. It's especially difficult when you are close to the subject matter: if it's something you are passionate about, have worked on for some time, or know a lot about. And yet that's often also when it's the most important to be concise. There might be a lot you want to say about a given topic, but if you can't condense it crisply and clearly in a way that can be understood and remembered by your audience, you've not positioned yourself for success. Forcing a major length constraint and the concision this imposes can do wonders when it comes to clarifying your message into its core and most critical components. Ultimately, this puts you in a better position when creating all of the supporting content, because you always have that main message in mind, making it easier to know what will help reinforce it (and what might be extraneous or unnecessary and is best left out).

When I'm going through the exercise of crafting the Big Idea (or helping someone else do so), it often starts by putting a lot of words down on paper. And then starting to cut. And reword. And move around. And cut. And replace a word with another that better describes what I want. And say it out loud. And reorder. And cut. This can feel a bit like word-smithing as you work through it, but there is something major in terms of clarity of thought that happens during this process. I find this is particularly the case when talking through it out loud with someone else, who can pose questions and help you to refine, making the main point you want to get across to your audience crystal clear.

So when you find yourself needing to communicate something to someone—whether a grand idea or simply the main point of your presentation—pause first and think about what you'd say if you only had a sentence or 10 words. Once you can say it that crisply, you're in a better position for success for ultimately getting this point across to your audience.

By the way, I realized it would probably be unfair of me to end this post without first composing and sharing my own Big Think (which won't be so surprising if you're a regular follower of my work):

Tell a story with data to inform and inspire action.

What is your Big Think? Or where could you envision yourself using a concept like this? Leave a comment!


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