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.Read More
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.
I am writing this post on the heels of a lovely albeit short European trip. It was part work, part play. In our spare time, my husband and I ventured out to one of our favorite restaurants. 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). In this post, we'll take a quick look at how categories help us make sense of things: both in life and in data visualization.Read More
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:
- 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.
- 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!
A few weeks ago, I ran a storytelling with data workshop for the IMPACT Planning Council in Milwaukee. It was a fun session (hosted by the School of Public Health at the University of Wisconsin, which is housed by a beautifully renovated former Pabst brewery building) with a super engaged group (plus my mother-in-law in attendance!). Last week, IMPACT shared with me a reconstruction of the workshop using the tweets published live during the event.
I found this pretty cool, so thought I'd share the bite sized morsels from my session here.
- Cole Nussbaumer, kenote speaker at data viz wrkshp takes podium
- Nussbaumer blogs about data viz at Storytelling w Data
- Cole says understanding the situational context of the data is key
- Who do you want to communicate to? What do you want to communicate? How can you communicate?
- Keep in mind what background info is relevant? What sound bite could you use to clearly articulate ur message? No more than 3 minutes
- Ur big idea must be a complete sentence that tells the audience abt the context of ur data
- V imp to choose the right way to display ur data. Sometimes plain text is best option
- Table or graph? Tables interact w our verbal system. Graphs interact w our visual system
- Line graphs are for continuous data, usually across time. Bar graphs are for noncontinuous data
- Bar charts shld always start from zero
- Exploding 3D pie charts misrepresent the relationships btwn the sections. Don't use them. Our eyes have difficulty judging size of areas
- There are many diff types of graphs. Always use the type that makes the most sense for your data and audience.
- @laurynbb:And kill the 3D bar chart - data viz wkshp “@planningcouncil: Give your graph to a friend to see if they understand it”
- You know you've achieved perfection in design when you have nothing more to take away
- Gestalt principles of visual perception: proximity, similarity, enclosure, closure, continuity, connection
- Eliminate clutter from your graphs.
- Get rid of anything that makes the audience work . Make it easy for audience to understand the point you're trying to make with the data
- @HelenBaderFound: RT @planningcouncil: Eliminate clutter from your graphs. #zentweets
- To focus attention where you want it, understand how ppl perceive info
- @HelenBaderFound:Follow @planningcouncil for tips on presenting your nonprofit's data, beautifully and effectively.
- Nussbaumer recommends Stephen Few's book as a good resource on data viz
- Ppl can keep abt 4 pieces of data in their memory at one time so design your graphs accordingly
- @HelenBaderFound Thx for following our live tweets of Cole Nussbaumer & Thx for your support of this wrkshp!
- @laurynbb: VizComm tenets “@planningcouncil: You have 8 secs to get audience's attention”
- The most effective data viz will still fall flat if you don't have a story to go with it.
- Stories stick in our minds in a different way than facts do.
- Use text to highlight key points in your graph
- Plot, twists and ending are components of your data story. If there's no twist - if it's not interesting, don't share the data
- Tactics for making story clear: horizontal & vertical logic, repetition, reverse storyboarding, fresh perspective
- Your graph is the evidence that backs up your story
- Cole says Excel can be used to make good graphs. It's not the default charts but a good user can make excel work
- Light backgrounds on graphs are easier on the eyes (and ink) than dark backgrounds
- Any time you cut out info be sure to think abt what context you might be losing
- Everything in a data viz (text & visuals) needs to reinforce the same message
- Rather than hope the data will tell you what it's about, be clear what your question is & then organize the data to answer the question
- Cole asks what distinction do you make btwn data viz and infographics?
- Infographics came out of journalism but have changed over time so now they are more glitz than data
- Just putting in a graph or infographic in to fill white space is not a good reason
- Donut graphs are even more difficult to read than pie charts; don't use them
One of the makeovers we discussed in the workshop.
- @MilwaukeeStat:@planningcouncil and @storywithdata – thanks for putting on the best data viz gathering Milwaukee has ever seen. Well done!
- @storywithdata Thanks, Cole, for inspiring 50 Milwaukee data geeks today with your advice on good data visualization!
- @storywithdata:@planningcouncil Thank you for the invitation to speak to your group - an engaged and lively bunch - I had a fantastic time with you all!
Another visual makeover from the workshop.
The full PDF (that puts my session back into the context of the rest of the afternoon, including a brief segment by Milwaukee mayor Tom Barrett) can be found here. Big thanks to the IMPACT Planning Council for hosting the event and allowing me to post their recap here!
I recently conducted my first public workshop in DC, where individuals could register to attend. The content was similar to that which I cover in a typical custom workshop for an organization, but with more industry agnostic examples and public data, reports, and visuals for the interactive pieces.
Leading up to the workshop, I thought my first might also be my last. Setting up a workshop means dealing with a lot of logistics (finding and securing the venue, setting up a way for people to pay, providing details to people as they register) - I basically play event planner on top of subject matter expert and content provider (and while the former is not my core skill set, I do find that my control-freak nature and attention to detail serve me well!). This all felt like a lot to take on. During the session, however, my attitude totally changed. There is something magical about people coming together, interested in learning. It more than made up for any logistics tedium. I simply love teaching people to be better storytellers with data. An eager audience like this is my version of bliss.
That said, I'm happy to announce upcoming public workshops in San Francisco and Chicago. For more details and to register, click here.
So you don't only get my viewpoint on how the session went, here's a snippet from one of the participants:
"It's obvious Cole knows what she's talking about, that she's studied the theory and applied it in the real world. The workshop itself is fine tuned and Cole is ready to answer any questions. It's a pleasure to learn from someone so knowledgeable."
On a related note, Francis reached out to me after the session for an interview for his blog; you can view the interview here on Google, what businesses need and what's hard to unlearn.
I hope to see you at one of my workshops soon!