align against a common baseline

Registration for upcoming storytelling with data public workshops in NYC and Los Angeles is currently open here. Stay tuned for details on fall sessions to be scheduled in Seattle and SF.

I've been failing when it comes to staying up with reading and posting on data visualization related stuff lately (my focus has been elsewhere). But I found myself with a few spare minutes yesterday afternoon and decided it was time to change that.

The first article in my Feedly was by FiveThirtyEight and the graph that appeared with it caused me to click for details. Here's the graph that caught my attention:

I like FiveThirtyEight's general approach when it comes to data visualization: straightforward and clutter free, with emphasis on the story. My view is that the graph should never be what makes the data interesting, rather it's the story that makes the data interesting. They seem to subscribe to this view as well.

In this case, the story is called out clearly at the top: Being Arrested Is Deadlier For African-Americans.

The accompanying visual is fine. But I think it can be made better by adhering to one recommendation I find myself often voicing to workshop participants: Think about what you want your audience to be able to easily compare. Put those things as physically close together as you can and align them along a common baseline.

With the current view, it's easiest to compare deaths for Whites by cause and, separately, deaths for African-Americans by cause. Yes, we can see (and read) that the yellow bars on the right are bigger than the red bars on the left (the point called out in the title), but note the bouncing back and forth your eyes do when comparing the bars across the two graphs. It's also hard to judge how much longer the yellow bars are vs. the red ones. Sure, we have the numbers there to help, but this means we have to do some mental math to decipher the differences. Why go through this work, when we can restructure the visual to avoid it?

To make it easier to compare deaths by cause for African-Americans vs. Whites, we can align both series along a common baseline. Here's what that looks like:

I made a few additional minor changes in this remake. The original graphs weren't monotonically decreasing in order of either White or African-American death cases (not sure why), so I changed the ordering of the data here so it would be, ordering by decreasing cause of death for African-Americans (there should always be logic in the way you order your data). Where there was space, I pulled the data labels into the bars to reduce the visual clutter. I pulled the subtitle instead into the x-axis label so that the words are right next to the data they describe. I didn't like the bold colors in the original visual, so stripped color out of my remake entirely. (If you do want to use color here, I'd suggest different shades of the same color - red and yellow together are both so bright that it makes it hard to focus on one or the other).

Another potential alternative with this data would be to use a slopegraph. Or so I thought. But I quickly abandoned this approach: there are too many criss-crossing lines at the lower values to allow space to label the data effectively. The following is what it looked like (note I didn't spend any time on the formatting or labeling once I realized this approach wouldn't work; if you're interested in seeing a completed example of a slopegraph in practice, check this out).



Also, while I love the idea of slopegraphs for group comparisons, in practice I've had mixed responses. Slopegraphs can be a little less intuitive than bars for data like this. It's also important to note that the slopegraph makes it easier to focus on the difference (via the lines connecting the various points), whereas bars make it easier to focus on absolute values. In this case, even if the data values had been such that the slopegraph would have worked, I think I still would prefer the bars when it comes to supporting the story that overall deaths per 100,000 arrests are higher for African-Americans compared to Whites.

Meta-point: align the things you want your audience to compare along a common baseline!

To download the Excel file containing the graphs above, click here.

audience, audience, audience

I sometimes feel a little like a broken record when I talk about communicating with data. My latest oft-repeated word is audience. We must keep our audience in mind throughout the design process and in general, try to make things easy on them. I spent a little time on this topic in a webinar for TechChange yesterday and thought I'd turn some of my notes into a quick blog post, which is what you'll find below.

When it comes to audience, I often have workshop participants do an exercise where I encourage them to identify a specific person they are communicating to. While it isn't always the reality, designing with a specific person in mind can help us from falling into the "mixed audience" trap. If you are communicating to a "mixed audience," it's easy to treat them as a glob and not recognize that the mixed group is made up of individuals. In fact, it's surprisingly easy to make a data visualization (or the broader communication in which a data visualization sits) without ever pausing to think about the person on the other end of it. When it comes to communicating with data, my view is that we should not design for ourselves or our work or project. Rather, we should design for our audience. Always.

One benefit of identifying a specific audience is that doing so allows you to reflect on who they are and what drives them. What do they care about? What motivates them? What keeps them up at night? This is helpful for structuring your overall message in a way they will be receptive to. If you can identify what motivates your audience, you can think about how to frame what you need them to know or do in terms of those motivating factors, improving your odds for successful communication.

Beyond that, there are important things to know about how your audience sees that you can use to your advantage when creating visuals. These are the lessons I've more traditionally focused on in my workshops and here on this blog. Identify and eliminate clutter or things that aren't adding informative value. Leverage preattentive attributes like color, size, and placement on page to signal to your audience where to look and create a visual hierarchy of information. (I already sound like a broken record on many of these topics, so won't repeat more of that here today!)

In the Q&A portion of any workshop or presentation, my broken record player of audience, audience, audience tends to run on repeat Considering our audience can help us answer many of the design questions we face: What colors will work well? When does enough information become too much information? Will an image or video be appropriate? When do I need to add more context or explain in greater detail? When you find yourself facing questions like these, pause to consider your audience. Who are they and what will work best for them?

Meta-lesson: keep your audience in mind throughout the design process; designing with them in mind will set you up for success when communicating with data!

dogfood with data

My husband and I were watching TV one evening last week. One commercial caught my attention. It was a commercial for Eukanuba dog food.

I do not have a dog.

Still, there was something about the combination of music and video and text with a bit of data that left an impression. As a side note, I find it very interesting that because dogs have shorter lives, life-long studies are possible in a much shorter timeframe than for humans.

When I was searching for the commercial today (more than a week since seeing it), I did not remember the specific stats. But I did remember the message: their study showed dogs treated well on a diet of Eukanuba live longer.

I often get asked about the inclusion of pictures and videos when it comes to presentations in general. For me, the thing to think about is whether that picture or video will help you make your point and help that point stick with your audience.

Along those lines, I find this commercial to be an excellent example of storytelling with data. Enjoy!

tell your audience what you want them to know

It sounds simple. It sounds obvious. But how often do people give a presentation or send out a report or email without ever making it clear what they want you to know? 

Have you ever looked at a graph and thought, I'm not sure what I'm meant to get out of this? Or sat through a presentation or meeting only to realize once it's over that you're not really sure what you just witnessed?

I listened to a presentation last week where the person speaking put up a busy-looking, data-heavy slide. It wasn't a good slide, but the speaker was clearly comfortable on stage and knowledgable about his topic, so I was motivated to understand what he was trying to communicate. Then he said a few magic words: "what this is meant to show is..." followed by a clearly articulated statement. It is amazing how those simple words can make the intimidating accessible.

The effect these words had on me was to generate more patience on my part (and even a little curiosity) to understand what the slide was showing. The speaker knew what he wanted the audience to get out of it and walked us through the visual in a way that made sense. It still wasn't a fantastic visual - there are changes that could have made it more effective - but his words overcame this shortcoming.

Tell your audience what you want them to know.

It's simple advice, but the impact can be profound!

storytelling with data...scribed!

I was in Dallas earlier this week and had the opportunity to talk about storytelling with data with a few different groups. One of those was the DFW Data Visualization and Infographics Meetup. This afforded me the pleasure of meeting Randy Krum, president and founder of InfoNewt, and John Colaruotolo from Collective Next, who (as far as I'm concerned) is able to create magic with pens and a whiteboard. Here is the latter's creation, which he completed during my talk:

Download full-sized here.

My experience with meetups is that people tend to flee pretty quickly after the presentation and Q&A. This isn't surprising - in most cases, people have shown up after a full day of work and are understandably anxious to point themselves towards home when the 9PM hour strikes. 

But this meetup was different. After my talk, people congregated around John's whiteboard creation in awe. Taking it in was like reliving parts of the presentation they'd just experienced, but with a slightly different twist. For me, it was fascinating to see a visual replay of what I'd just said: seen and heard through someone else's eyes and ears.

I overheard one person describing this as a superpower. As in, "yes, John has a super power that many of us do not have." I thought this was an interesting perspective. And very cool - when you consider that probably most people you encounter have some sort of superpower that you do not personally possess. John's superpower was palpable. Or at least, I stood in awe. He was...

listening...

intepreting...

drawing...

ALL AT THE SAME TIME. 

Crazy. Brilliant. Beautiful.

That's all I have to say.

Well - not quite all. Big thanks to Randy (@rtkrum) for hosting a great event and to John (@johncolaruotolo, www.collectivenext.com) for capturing it on a (beautiful) whiteboard!

selling your data

A participant made a comment after my public workshop in Dallas this morning that went something like this: "I'm in sales. I was whispering to my colleague during part of your presentation that really what you're doing is selling your data - it's just that nobody recognizes that's what you're doing."

At first, I was put off by this. Selling my data? No, that has the wrong connotation.

But upon reflecting a little more, I realized that is a part of what I'm doing (and teaching others to do as well). To be clear, this is not about overselling, but rather making your data something people want to pay attention to. There must be corollaries between that and creating something that people want to buy, right? I think so.

So I pondered...

What makes somebody want to buy something? Here are a couple things that come to my mind when I reflect on this question and how we can translate to storytelling with data:

• It must look good. Packaging is important. If a product doesn't look good, no one is going to buy it. Beyond that, studies have shown that consumers tend to have more patience with aesthetic designs. If your data visualization (or the broader communication in which the data visualization sits) doesn't look nice, your audience may not pay adequate attention to it. Or put more positively, creating an aesthetically pleasing design can foster goodwill in your audience, making it more likely that they'll have patience and spend time with your visual or communication. 
• The product must meet the users' needs. A good product is designed with the end-users' needs in mind. The same is true for good data visualization, yet so often we fail to pause and think about the audience who is on the receiving end of the communication. What do they care about? What are their needs? How do I make what I want to communicate work for them? Success in communicating with data does not follow creating a data visualization that works for you; success is making a data visualization that works for your audience. 
• It must win over the competition. When it comes to purchasing, there are a lot of things competing for share of wallet. To win in a competitive marketplace, a product must be better than alternatives in one or more ways. Translating to communicating: there are a lot of things competing for our time. You likely face a busy audience, yet you need them to devote time to listening to your presentation or reading your report. For that, it must be better than alternatives. Which brings me back to my first two points.

These are just some quick thoughts on the topic. I'm sure there are other parallels we can draw. If any come to mind, please leave a comment with your thoughts.

Beth, if you're reading this, thanks for the thought-provoking comment!

I'll end with a couple of pics from today's public workshop in Dallas so those of you reading who weren't there can be jealous of all of the fun we had (yes, we even used crayons, courtesy of white space). If you'd like to take part in a future session, check out my public workshops page to register or suggest a location.

the power of categorization

There are still a few spots left in upcoming Dallas (5/5) and San Francisco (5/11) public workshops: details and registration can be found here.

I am writing this post on the heels of a lovely albeit short European trip. It included a few days in London, where I had the opportunity to conduct a day-long workshop and also present at Tucana Global's 2015 People Analytics conference. In our spare time, my husband and I ventured out to one of our favorite restaurants: Ottolenghi. As I was perusing the wine list, I was reminded of the importance of categorization (yes, apparently my data-brain is on even at dinnertime). Let's take a quick look at how categories help us make sense of things: both in life and in data visualization.

Here's a pic of the drink menu that inspired this post:

In the case of the drink list, categories ease our processing of the information. They appear on the left: aperitif, sparkling, rose, white, orange (!!), and red. Can you imagine how increasingly difficult the task of picking something to drink would be without this categorization to help us make sense of the list and understand where to focus our attention? There would also be a greater potential for misinterpretation - for example, without the categorization, I might have (incorrectly) assumed Dabouki to be a red wine. I certainly would have (again, incorrectly) believed Bianco Amphora to be a white wine. The processing of the information was made easier (and with less room for error) because I had a well-labeled construct to use as I interpreted the information.

Categories can be similarly useful when it comes to helping your audience interpret your data visualization. Let's look at one of the examples I discussed briefly at the People Analytics conference.

In the example below, data is plotted in a scatterplot across two dimensions. Imagine your organization collects information about its managers via an upward feedback survey, ultimately quantifying a manager's capabilities (as assessed by his or her team) with a single number. Your company also has a performance management process, through which everyone receives a performance rating. It might be useful to look at the upward measure (how employees feel about their boss) and the downward-looking measure (how the manager performs, as determined by their manager) together. This is shown below.

The vertical y-axis shows the manager rating. The horizontal x-axis shows performance rating. Each manager in your company is a point on the scatterplot.

We can add additional labels on each axis to help with the interpretation of the information. With this setup, the audience need not know that a higher % favorable on the Upward Feedback Survey indicates a better manager (in the same way that I didn't need to know that Dabouki is a white wine because of the categories on the drink menu).

We can take this a step further and add categories onto the x-y plane directly:

I'll admit that this final version does look a bit intimidating at first. For this reason, there can be value in starting with less and adding more, explaining what you're doing to your audience at each step so they can follow along with you, making the final visual feel less intimidating than it might otherwise. In my presentation, I started with a blank graph with only the axis labels and first described what I would plot (before showing any actual data; this can be a nice way to create anticipation among your audience as well). In the next view, I added the points to the previously blank graph. Then I emphasized the average. Following that, I drew the quadrants by adding vertical and horizontal lines based on the average. Then I drew attention to the points at the bottom left by making them red and adding the label Low/Low. Finally, I ended with the version shown above with all quadrants labeled and light shading at upper left and lower right.

In this final view, note also how the added labels on the graph make the data easier to talk about. With the quadrant titles, I can focus conversation on the cases where managers are scoring low from both the upward or downward perspectives (Low Perf/Low Mgr Score in red at the bottom left). Or there might be some interesting discussion in the cases where the signals don't align - Low Perf/High Mgr Score at the top left or the opposite on the bottom right.

Meta-lesson: categories (and more generally, descriptive and pithy labels) can help your audience interpret the data you show.

My other European destination this trip was Paris, where my husband and I enjoyed more amazing food, saw many sights, and perused many more drink menus. The overall trip was great, only too short. I hope to travel to Europe again this summer for a longer stay. If you are reading this and interested in discussing a potential workshop for your European team or organization, reach out to me at cole.nussbaumer@gmail.com.

I'll close with a couple pics of Parisian adventures with my favorite travel partner.

Eiffel Tower in the distance!

Musee D'Orsay

the great pie debate

You can't title a talk "Death to Pie Charts" and not expect to spur some debate on the topic. Sometimes being a little provocative can help generate interest and keep people's attention. It seemed to work last night at a talk I gave at the University of San Francisco as part of their Data Visualization Speaker Series.

We had an awesome turnout and I covered a condensed overview of the key lessons I teach in my workshops: understand the context, choose an appropriate visual display, identify and eliminate clutter, draw attention where you want your audience to focus, and tell a story. As part of the lesson on common visual displays, I noted one graph you won't see from me: the pie chart. We looked at an example to illustrate some of the challenges reading pie charts and discussed some alternative ways to visualize the data. Then we went on to cover the remaining lessons, followed by some lively Q&A.

The debate started with a simple question that went something like this: I've recently become interested in data visualization and I've been reading a lot about the field. Specifically on the use of pie charts, I've read some things that denounce them and others that say they have a place. Are you aware of any research comparing the takeaways that people get from pie charts compared to bar charts, for example?

My response went something like the following. This is a difficult space to study. Many of the studies that come out demonstrating one thing are opposed via counter-studies that show the opposite. My personal dislike of pie charts is more anecdotal - when I see them used in a business setting, inevitably they fail. 

I didn't talk about this last night, but upon further reflection, as I think back through the many pie charts I've encountered over time (hundreds, at least), I can think of only two cases where I tolerated them:

1. At Google when we first started sharing diversity stats on the workforce internally - the team wanted to show the general breakdown of men vs. women (for example) but didn't want to communicate the specific numbers. In this case, the fact that our eyes don't do a great job of accurately measuring two-dimensional space worked in their favor. So in a way, they were taking advantage of one of the pie chart's biggest disadvantages.
2. More recently, I encountered this data visualization highlighted in Best American Infographics 2013 - ten years of art history. Each pie represents an individual painting with the five most prominent colors shown proportionally. You can see the shift in color usage over time. Art via pies. I actually really like this!

Personally, I don't use pie charts because when I pause and think about what I want to show, I've always found a way that seemed to get the information across better than the pie chart.

That said, intelligent people will disagree with me and point out use cases for the pie. I welcome this diversity of perspective! Last night, after giving my viewpoint, I opened the question up to the audience. Santiago Ortiz (Moebio Labs) was in attendance and offered some great perspective. I'll paraphrase the viewpoint he shared: There are studies, and usually bar charts win in terms of people remembering the numbers. But it's difficult to research the Gestalt feeling of a "percent of whole" where pie charts are actually effective. So is the story about the specific numbers, or the relative amounts, as a percent of the whole? If it's the latter, then pie charts can work. (I'll note also that this is a similar point to one raised by Robert Kosara as part of his highly valued feedback on my forthcoming book).

Still, I'm standing firm. I won't use pies.

Does that mean you shouldn't use pies? Not necessarily.

First and foremost, always think about what you want your audience to be able to do with the data you are showing. Choose a visual that will make this easy. I often recommend the following. If you find yourself reaching for a pie, pause and ask yourself why. If you can answer that question, you've probably put enough thought into it to make it work. I should point out that this is something you should do for any type of visual you are using. Making yourself articulate why the chosen visual works for your needs is one way to help ensure that it actually does.

We didn't solve the great pie debate last night and we won't solve it here. People stand on different sides of the fence and I actually think that is ok. When it comes to data visualization, rarely is there an absolute right or wrong. You should constantly be applying your critical thinking skills. Don't do something blindly because of a statement you read or hear. Think about your audience, what point you are trying to make, and how you can do that in an effective way. If unsure, create your visual and seek feedback.

Big thanks to the event organizers and sponsors for last night's event: Scott, Sha, Alark, Sophie, Chris, all of the student volunteers, and everyone else who helped. Thanks also to those who participated in Q&A and everyone who showed up to the talk. I had a great time and I hope you did, too!

the biggest bang for your buck

After trekking through some surprise springtime snow, I had a great public workshop in Chicago this afternoon (want to join in the fun? see here for upcoming sessions, including workshops in London, Dallas, and San Francisco). Discussion and Q&A are some of my favorite components of the workshops, because we can tackle specific challenges that folks are facing. There are always great questions and today was no exception. There was one super practical question that stuck with me that I thought I'd share more widely here.

You've likely heard of the 80-20 rule. Basically, in business it's the idea that you can put in 20% of the effort and get 80% of the result (and avoid the remaining 80% of work that only yields an additional 20% of result). The question was: "how can we apply the 80-20 rule to what we've learned today?" In other words, out of all of the meaty content we've covered, where should you start when it comes to having the greatest impact? Or, as I'll paraphrase it - where should you focus your energy to get the biggest bang for your data visualization buck?

My answer? There are two easy things you can start doing today to have greater impact when it comes to communicating with data:

First: always tell a story. Think about what you want your audience to get out of every graph you show and STATE IT IN WORDS. Doing this simple step goes an amazingly long way when it comes to helping make the data you show make sense to your audience. When you put the takeaway into words, your audience knows what they are meant to look for in the visual. We spend the hands-on portion of the workshop looking at a number of real-world example graphs. All made by well-intending people. And the question that comes up again and again and again is: what point are they trying to make? Don't make your audience work to figure this out - state it for them!

Second: use color sparingly and strategically. Rethink how you use color - don't use it to make your graph colorful. When used sparingly, color is your single biggest tool for drawing your audience's attention to where you want them to pay it. I often start by making every single component of my visual light grey, pushing it all to the background - the data, the axes, the titles. This forces me to think about where I want to draw attention and use color intentionally and with purpose to emphasize those pieces of the visual.

Pair these two things - state your story in words and use color strategically to highlight where you want your audience to look - and you'll have gone a long way down the path of communicating effectively with data. Bonus: you don't even need crazy technical skills to do either of these things.

Thanks, Bill, for the thought-provoking question!

annotate with text

In keeping with my prior post, I'm sharing another "most-photographed slide" from my recent workshops.

My voiceover typically sounds something like this:

When it comes to storytelling with data, one very important component of stories is words. There are some words that absolutely have to be there: every graph needs a title and every axis needs a title. This is true no matter how clear you think it is from context. The only exception that comes to mind is if your x-axis is January, February, March, etc., you probably don't need to title it "months of the year." You probably should make it clear what year it is. Any other axis needs a title. Label directly so your audience doesn't question what they are looking at.

Don't assume that two different people looking at the same data visualization will walk away with the same conclusion. Which means, if there is a conclusion you want your audience to reach, you should state it in words. Use what we know about preattentive attributes to make those words stand out: make them big, leverage color and/or bold, and put them in high priority places on the page like the top.

Speaking of which, that title bar - stories have words: annotate with text - is precious real estate. It is the first thing your audience encounters when they see your screen or your page, so make it count. Use this space for active titles, not descriptive titles. If there is a key takeaway for your audience, put it there so they don't miss it. It will also help set up the content that is to follow on the rest of the page.

When you are communicating with data, there are some words that usually need to be there: data source, as of date, and perhaps notes on assumptions or your methodology. These are necessary words but they don't need to cry out for attention. Use what we know about preattentive attributes to emphasize the important parts of your visual and also to de-emphasize less critical pieces. Footnotes can be small, the text can be grey, and they can be in lower priority places on the page like the bottom.

Use words to title, label, and explain; they help make your data visualizations accessible!

To see this and other storytelling with data lessons firsthand, attend one of my upcoming public workshops.

consulting for context

Upcoming workshops: Details have been set for my Chicago public workshop - it will be on 3/23 and you can register here. I'm offering a full day workshop in London as a pre-conference session ahead of Tucana Global's People Analytics conference in April (it can be registered for separately from the conference and content will be made relevant to non-HR as well), details here. I'm also in the planning stages of a public workshop in Dallas in early May - to be notified when it is scheduled or suggest/volunteer a venue, click here.

Speaking of workshops, I've conducted a lot of them over the past three weeks. It takes nearly all of my fingers to count them. I've packed so many into a small time period that I've started to observe some interesting patterns across them. I'll tell you about one such pattern today.

I tend to begin each workshop by asking attendees to do something that I think may make some people feel uncomfortable: take whatever electronic devices are within their reach and place them slightly out of reach. (There is little that is more distracting in a shared learning environment than someone typing on their laptop or texting on their phone!) 

Still, somehow, people end up with phones in their hands.

To my amusement, however, for the most part they don't appear to be using their phones to check their email or update their Facebook status, but rather to snap a quick pic of the slide I'm discussing. This is one use of an electronic device in my workshops that I might be willing to condone! The pattern I referred to earlier is that it seems to be the same handful of slides that prompt said picture-taking. I believe this is an indication of the usefulness of the content, so thought I'd share some of that content with you here.

One popular slide is about consulting for context and lists the following thought-starters:

• What background information is relevant/essential?
• Who is your audience? What do you know about them?
• What biases does your audience have that might make them resistant to your message?
• What data is available that would help strengthen your case? Is your audience familiar with this data, or is it new?
• What would a successful outcome look like?
• If you only had one minute or a single sentence to tell your audience what they need to know, what would you say?

My voice-over of this slide usually sounds something like the following. Often, when you are putting together a communication, it is at the request of someone else: a client, a stakeholder, or a manager. Sometimes, the person requesting the work has things in their head that are important to understand that they may not think to say out loud. The above are some questions you can use when that's the case, to try to tease the full context out of the person requesting to make sure you have a robust understanding of the need to communicate before you start building the actual communication. Being clear on the context up front can drastically reduce iterations down the road.

I find the last two questions in particular can be really useful for getting at the main message you want to communicate. I often use these when I'm working with clients to get clarity on what they want to say. What would a successful outcome look like? Or, if you had a really finite amount of time or number of words to say what you need to say: what would that sound like?

I use this to set up the concepts that are typically covered next (that I've blogged about here previously): the 3-minute story and Big Idea.

Interested in other parts of my workshops that are prompting people to photograph what they see? Stay tuned here and I'll highlight some others in upcoming posts!

Jon and I chat about non-zero baselines

Earlier this week, I got a rare chance to chat in person with my friend Jon Schwabish (policyviz.com) who was in town for a quick trip. We recorded a short chat on a topic that's been generating some buzz in the data viz world lately: non-zero baselines (which I've previously blogged about here and here) and whether it is ever ok to start the y-axis of your graph at a value other than zero.

Check out the following to hear our chat. Leave a comment here or on Twitter (me | Jon) to join in the conversation!

A couple visuals we referenced while talking:

The graphs we had up on Jon's computer as we were chatting

From Drew Skau's recent Visual.ly blog post

what you can expect in 2015

I just pulled up my blog and noticed that my last post was published on October 31st. Exactly 69 days ago. Or, in other words, long than I've ever gone without posting. Yikes. It's not that I haven't been writing. I've actually been writing a lot. But we'll get to that in a moment. With this post, I'm officially ending my blog silence and letting you know about some exciting things I expect to happen in 2015.

In addition to posting here on a more regular basis, I'll be spending my time on a mix of public workshops, custom workshops, and may even be appearing on a bookshelf near you. Here's some detail on each of these areas:

PUBLIC WORKSHOPS
I plan to offer a number of public workshops this year, where individuals can register and attend. Workshops have already been scheduled in San Francisco, Seattle, and London, and I expect to add more in Chicago and on the East Coast in 2015. State-side, these are 3-hour sessions that provide an overview of data visualization theory and best practices, with a focus on practical application of lessons learned for telling a compelling story with data. In London, I'll be conducting a full-day session on the same topic (this is being offered as a pre-conference workshop ahead of Tucana Global's People Analytics conference, but will be relevant for non-HR folks, too). Details and registration for upcoming public workshops can be found here.

CUSTOM WORKSHOPS
I'll also continue to provide custom workshops to organizations. These are typically half-day sessions that arm participants with a solid foundation for communicating with data, with hands-on practice applying the lessons learned to the team's specific work. More information on these sessions can be found here.

storytelling with data: THE BOOK
Most exciting for me is a project I've been working on (one of the reasons posts here have been few and far between lately) that is nearing completion: storytelling with data the book. I have codified the lessons I teach in my workshops and am excited to offer them alongside many real world examples in a comprehensive reading. I'm nearly finished writing and if everything stays on track, it should be available for purchase later this year. Stay tuned here for the latest.

I'm looking forward to a great 2015 and I hope yours is off to a fantastic start!

annotated line graph from Uber

With the email that hit my inbox earlier this afternoon, Uber has impressed me twice in the past week. The first time was in response to a simple comment that accompanied my '3' numerical rating (the lowest I've ever given): "With the world series game today, should have avoided stadium area." I had an email in my inbox from Uber's customer service within the hour agreeing that was a silly route given the Giants' game and reducing the price to what it would have been without the crazy traffic. Amazing.

And now they've done it again, this time via effective data viz. The annotated line graph below shows expected Uber demand over the course of the evening and into the wee hours of morning. This is one of those rare cases where they can get away without showing the y-axis values at all, since the relative peaks and valleys are more interesting (and meaningful) than the absolute numbers.

Nice job Uber. Though I must say this makes me happy to report that kiddie Halloween in my neighborhood is on foot, so no need to even think about surge-pricing here!

Speaking of which, I find it impossible to publish a post on Halloween without couple pics of my superhero family.

Happy Halloween!

SF housing cycles visualized

If you've shopped for real estate in San Francisco recently, you've likely experienced the crazy world of multiple offers, waived contingencies, and all-cash deals well above asking price. We've been house shopping here for nearly two years, without much to show except a jaded view of the market and an ever-increasing pile of home-for-sale flyers. My husband and I joke that our toddler will grow up thinking that's what you do on the weekends: go look at other people's houses.

If you've been in this situation, or a similar one, you've perhaps also wondered (like us) whether prices will continue to increase at the rate they have been, or if there is an elusive bubble that is about to pop. To that end, we came across the visual below, which depicts a simplified view of San Francisco housing market cycles over the past few decades.

If you've followed this blog for long, you might expect that I will next proceed to rip the above visual apart. But I am not going to. 

I actually really like it. 

Sure, there are some minor things that could be changed. But let's focus instead on the good: it's well-labeled, both in terms of titles and text annotation on the graph itself. There is a clear narrative that calls out some interesting things in the data. For example, over the past 30+ years, the period between a recovery beginning and a bubble popping has been about 6 years.

According to the graph, the last recovery began in 2012, which would put the next bubble pop at approximately 2018.

Which means there's still time to buy before we hit the peak... 

show the full picture!

There is still space available in my upcoming San Francisco public workshop; details and registration can be found here. Click here to suggest a location for a 2015 workshop.

I've posted a number of times about Pew Research articles. Well, not the articles exactly, but rather the visuals they contain. To be honest, it's rare that I read the actual article. I scan the headlines as they hit my inbox and if something piques my interest, I follow the link and scroll through the article, not reading, but taking a discerning look at the graphs.

This is what I was doing when the following visual caught my eye within an article titled, "Perceptions about women bosses improves, but gap remains." The full article can be found here.

They are nice looking graphs, as is the norm. But upon examination, for me both of these visuals leave out an important part of the picture. Let's examine them one at a time.

I'll start with the top chart titled "Boss Gender Preference." The first thing I want to do with a graph like this is add the percentages to understand what proportion of the overall population we're talking about. Everybody? No. In this case, a little math tells us that a lot is missing, especially in the more recent years. My "data-spidey sense" (as a former boss of mine used to call it) gives me suspicious pause. I go back and read the title, etc. to try to put what is missing (the piece or pieces that would enable the lines to add to 100%) into context. In this case, the lines represent the percent of those responding to the survey who said they prefer male bosses and the percent who prefer female bosses. So, I would assume that the remainder are those who have no stated preference (and only now as I write this do I see the footnote below the second graph confirming this; for sake of this discussion and my forthcoming makeover, I'll assume "no opinion" is the same as "no preference"). In recent years, this accounts for nearly half of the total. That seems important. And yet isn't shown explicitly.

Let's look at a different view of this data:

In this remake, I replaced the line graphs with stacked bars so that we can see the full 100%. Those preferring male bosses are along the bottom in blue, female along the top in orange so that we have a consistent baseline at both bottom and top to be able to compare what has happened over time for those groups. I only graphed the points that were labeled in the original graph, omitting the 2000 data point altogether so that I'd have a (roughly) consistent gap between the dates along the horizontal x-axis. That data point was strange to me in the original graph, anyway, since preference for both male and female bosses increased (while % indifferent went down), but then bounced back to the previous trend. It looks like something strange may have been going on there (was the question worded differently? had something recently happened in current events that influenced this? I'd want to better understand this, however since it doesn't seem critical to the overall story I'll simply omit).

With this view, you can visually lump together the grey and orange bars, or even focus on just the grey to see progress on a different level than was shown in the original graph - from an overwhelming preference for male bosses to lessened sensitivity when it comes to the gender of one's boss overall.

After plotting the data this way, I considered that perhaps a line graph would work better, but with an additional line for the indifferent portion. I drew it, but after seeing it, it reinforced for me that the stacked bars work better for being able to add different data points together, which I think is important here. In case you're curious, here's what the line version looks like:

Next, let's turn our attention to the second visual: Female CEOs. The graph as drawn by Pew Research looks like great success: up and to the right. In this case, frame of reference is critical. Yes, 4.8% is huge relative to zero. But it's really small compared to the potential (100%). Here's how I'd graph this one:

With this view, there has been progress - yes - but there is potential for much more.

As a side note, it also bothered me that the dates were totally different between the two graphs (it seems like you should be able to compare the data points across them, but if you follow 1975 in the original top graph down, this lines up with something like 2002 in the bottom graph). I attempted to address this through the titling of the graphs ("the past 60 years," "the past 20 years") but this isn't a perfect solution. Part of me wants to have a bunch of empty space on the left of the second graph and make it so the 20 years of data we have there lines up to the top graph along the same date scale, but I think this would squeeze the data we do have too much to be legible. So I settled with attempting to address through the titles.

Here is the side-by-side of the two visuals:

Note how in both cases above, the remake causes us to see a different story and perhaps even draw different conclusions than we might have with the original visuals. For me, the makeovers present a fuller picture.

It is possible that this fuller picture is all explained in the article. I really can't say, as I still haven't read it. I am probably not the only one who "reads" this way. Which is another lesson: design your visuals so they still work when your audience doesn't read the accompanying text!

In case you're interested, my other recent posts on Pew Research visuals can be found here and here. The Excel file with the makeovers from this post can be downloaded here.

design with audience in mind

Recently, my husband shared a USA Today graphic with me that summarizes diversity stats across a number of Bay Area tech companies. Surely, this would be a good blog topic, he told me. He knows me well. Here is a screenshot of the visual:

Online version can be found here.

First, let me mention how cool I think it is that companies like Google have started sharing their diversity stats. I expect that with this transparency, we'll see movement towards more diverse workforces over time.

Next, let me discuss what an annoying user experience it is to try to look at the diversity data with USA Today's visual. It shows the breakdown for the given company (Apple, in the above screenshot) by gender on the left and ethnicity on the right. The various tech companies each have their own tab; you can toggle between companies using the numbered tabs along the left (not sure what the numbers on the tabs mean...if anything).

What is the first thing you want to do with this data?

For me, the stats for a given company, on their own, are not so interesting. It's by comparing them to the other companies that we help build context for what is good (or if not good, then at least better), what is worse, and so on. In other words, the single thing I want to do most is compare the stats across companies. The way this visual is organized makes this a lot harder than necessary. If I want to compare the proportion who are women at Apple (for example) to other companies, first I look to the Apple tab and commit 30% to memory, then I click through the other tabs one by one to try to put that 30% into context. This is annoying, but possible.

It gets more annoying and difficult if you try to do it by ethnicity. Try comparing the proportion Hispanics make up of the various workforces, for example. It's further complicated by the fact that the slices on the pie move and the order in which the companies are listed changes as you toggle between companies.

This is not an ideal user experience. My guess is that there was some desire to make the visual "interactive," which it sort of feigns via the tabs of various companies along the left. But really all this does is allow you to see the various static graphs, one at a time. Why not replace with a single static visual that makes the task your audience is going to want to do easy?

In other words, let's design the visual with our audience - and how they are going to want to interact with the data - in mind. If the goal is to compare across companies, I might do something like the following:

(Note that the title and takeaway at the top were preserved from USA Today's visual; I'm not sure I would have been quite as negative.)

The above version allows me to see things that were very difficult to get to with the original. eBay is doing the best from a gender diversity standpoint, but worse when it comes to racial diversity, where Yahoo is doing better than the others, etc.

Bottom line: design with your audience in mind!

Click here to download the Excel file with the above visual.

nice summary by UP

I have been religiously wearing my UP24 band over the past two months, after taking a hiatus from the technology while pregnant. I originally strapped it on to have record of my sleep. With a newborn, of course sleep looks much different now; there's something strangely gratifying when you can not only know that's the case, but also see it. Over time, I've seen the number of night-wakings generally go down (though last night was an exception, which I feel as I groggily type this post) and sleep consolidate into bigger chunks as my little chunk sleeps for increasingly longer segments. I can start to see a pattern emerge (bed at 11pm, wake for feeding at 2-3am and again at 5-6am, get up around 8am). Visual evidence of slow but measurable progress!

What caught me by surprise when I started wearing the band again is the motivation it inspires when it comes to my activity level. The recommended goal is 10,000 steps per day. When I don't hit it, I feel a bit of shame. When I do hit it, I feel a gratifying sense of accomplishment. That sense of accomplishment goes up as the amount by which I surpass the goal increases. This motivates me to get out and move on a daily basis to ensure I'll hit my goal.

So all of this is a long prelude to the summary from UP that hit my inbox this morning. I tend to post a lot of examples of data viz with which I take issue, so thought I'd mix it up and focus this post on one that I found to be effective. Of course there are things that I would have designed differently, but this summary gets the job done. It's keeping me motivated. Let me step you quickly through what it shows.

It starts with an overall summary of week-over-week changes:

Relative to the prior week, my average sleep per night went down a hair and my movement increased a little. I like the big, clearly articulated takeaway: you held steady.

This is followed by detail on my sleep this past week:

My average nearly hit the nightly sleep goal of 8 hours. I even beat the goal three times (versus prior weeks where I haven't hit it at all!). In fact, this summary looks perhaps deceivingly good, though the number of nights of uninterrupted sleep, at 0, starts to point to the newborn effect. It will be life-changing when that number moves, even by one!

The sleep summary is followed by a movement summary:

I hit my 10,000 step goal each day (my informal goal for myself is to hit it every day in August). You can see the days where a jog or long walk really put me over the top. My most idle time of 8-9am comes as no surprise, as that's when the little one eats breakfast, sequestering me to an armchair for the better part of an hour. The rest of his day remains less predictable.

It's a straightforward and simultaneously (for me, at least) motivating summary.

For more on UP from a numbers-person's perspective (including downloading the data it captures to analyze on your own), check out Nathan Yau's recent review here. For more on the cool insights the team at Jawbone is starting to make based on the crazy amount of data they are amassing, check out their blog (for example, this post). They've shared it with others who have started to analyze and share as well (here's a recent WSJ example, though I still lament the lack of color-key on the heatmaps - ok, turns out it is impossible for me to write a blog post without critique!).

Someday, I'll download all of my data and perhaps do something fun with it. For now, I'll continue to check out the daily and weekly summaries to track my progress and for that feeling of accomplishment when I hit my goals.

the challenge of teaching data visualization

You want to increase your skills when it comes to creating effective, captivating, and informative data visualizations. I want to teach you. But with this, comes certain challenges. It is these challenges that I hope to discuss in a SXSW 2015 panel (along with three expert colleagues: Jon Schwabish of policyviz.com, Kaiser Fung of Junk Charts, and Ben Schneiderman of University of Maryland).

But we need your help.

Over 3,000 proposals have been submitted for SXSW Interactive. Obviously, only a fraction of these will be chosen for sessions. Public voting accounts for about 30% of the decision making process. That's where you come in.

Please take 20 seconds to vote for our session by clicking the button below (which will prompt you to create an account before voting if you haven't already - it's super fast, I promise).



Check out the following slideshare for more detail on the session topic.

The Challenge of Teaching Data Visualization from Jon Schwabish

Thanks and I hope to see you at SXSW 2015!

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?