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!

storytelling with data in 140 characters or less

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!

more public workshops + interview

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."

 - Francis Gagnon, Business Intelligence Officer at IFC and author of the blog Visual Rhetoric (12/13 update: Francis' new site is Voilà)

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!

visual editing

This week, I have the pleasure of presenting to three audiences on the topic of storytelling with data. Thank you to those who attended (and those who soon will) for your interest in the topic, example visual submissions, attention, and thoughtful questions and comments.

After today's session, one participant made an insightful observation: the process I teach for making effective data visuals is very similar to the editing process for the written word. When I paused to think about it, the parallels are striking. Here is a sample:

• Make your point crystal clear. When possible, state it up front.
• Remove things that are ancillary or detract/distract from the core message.
• If you can say the same thing with fewer words (or visuals!), do so.

The list of similarities goes on. With a day job as an editor, in addition to this observation, the participant noted a challenge he often faces. While with the written word, there are clear rules guiding and providing rationale for critique - it's difficult for someone to argue with feedback when a word is misspelled or grammar is incorrect - we don't seem to have agreed upon rules or language to use when it comes to visuals, which can make convincing someone to take editorial feedback in this area difficult.

The specific question posed was: what language can I use to justify editorial feedback on data visualizations?

I think this is one of those areas where there is no single right answer. I can talk for hours (and have a couple times in the past two days) about why the things I teach are important and show empirical evidence on the impact of well-designed visuals time and time again through examples. But is there an easy, quick, universally accepted way to convince someone of the 'right' way to visualize information? I think it's in part because there isn't a single right way - there aren't so many rules when it comes to data viz, and even in cases where there are accepted best practices, they aren't widely taught - that this is a challenging space.

Though many of the ideas are, the language itself that experts use in this space isn't pervasive or consistent (perhaps because data visualization draws from a number of different fields, each with their own language: statistics, design, computer programming, journalism). Nancy Duarte discusses maximizing the signal to noise ratio. Edward Tufte focuses on ridding visuals of chart junk. I personally don't use consistent language: in one sentence I'll talk about identifying and eliminating clutter, while in the next I'll discuss the "crud" 3D introduces or being aware of adding items to your visual that are taking up space or attention but aren't adding informative value. I found that my answer to the specific question posed focused on reducing cognitive load, which may be fine for helping to convince some audiences, but fall flat with others.

Because I feel I'm still thinking my way through a good answer to this question - how to convince someone quickly of the value of drawing attention to the important parts of your visual and stripping out the things that don't need to be there - I will pose the question here in hopes of gaining some wisdom from the crowd:

What language do you use to convince yourself and others of the value of the visual editing process when it comes to data visualization? 

Leave a comment with your thoughts!

lessons from GMN

GMN is the Grant Managers' Network. This afternoon, I had the distinct pleasure of speaking at their annual conference in San Antonio.

My topic was storytelling with data, presented in two 90-minute sessions to about 200 super engaged grant managers. The first session was an overview of data visualization best practices. The second was a hands-on workshop focused on applying the lessons covered in the first session to real examples (submitted by participants) to practice employing those freshly learned skills for showing philanthropic impact through storytelling with data.

With this post, I thought I'd recap the lessons I covered today and show them applied to one of the visual makeovers that we focused on during the workshop session.

First, the lessons:

1. Choose the right type of display: leverage bar charts whenever possible due to ease of interpretation, lines are for continuous data only, avoid pies
2. Eliminate the clutter: get rid of the stuff that doesn't need to be there, de-emphasize the necessary but non-message impacting stuff
3. Focus attention where you want it: leverage pre-attentive attributes (color, size, thickness, enclosure, placement on page) to draw your audience's attention to the important parts and create a visual hierarchy of information
4. Think like a designer: include affordances that make it clear to your audience how they should interact with your visual, make the visual accessible by favoring simple over complex, take the time to make your visual aesthetically pleasing to gain your audience's attention and patience
5. Tell a story: don't use graphs to show data, use graphs to reinforce your story; make your story explicit with words

Now, let's take a look at these lessons applied to one of the participant-submitted visual. Here's the visual:

There is a lot of information here. Here's a glimpse into my thought process as I look at it and start to figure out how I want to approach turning it into a story:

• I'm unsure at first how the data in the graph and two tables are related. The first thing I did was some math to try to better understand how the numbers relate to each other (to learn/verify things, for example Adoptions + Transfers + RTO = Live Release).
• Upon closer inspection, it seems the overall story is around positive (live release) and negative (euthanasia) outcomes; I want to make these two sides of the story more immediately visually clear.
• There are some unfamiliar things that I want to clarify: the acronym RTO and the fiscal year dates (so I know whether/how to compare Jan to FYTD). A quick email exchange with the submitter later, I learned that the former is "Released to Owner" and the latter is Oct-Sep.
• Depending ont he story to be told, there may be too many comparisons. The way it's set up now makes me want to compare Jan figures with fiscal YTD figures, which probably doesn't make sense.
• I crave an action title: the top-of-page space is precious because it's the first thing an audience encounters. I should be used to tell the audience what they need to know and orient them to the information that will follow.

After sketching out some things on paper and iterating a few times in Excel, here is my makeover of the visual:

Let's take a brief look at the changes I made, according to the 5 lessons I outlined at the beginning of this post:

1. Choose the right type of display: This isn't necessarily the right display, but it's one approach. I combined the data into a single visual and made life and death visually opposing (note that death is both red and in the negative direction to reinforce visually that it is a poor outcome).
2. Eliminate the clutter: I reduced the visual comparisons by focusing on FYTD and eliminating the January comparison. I pushed the axis and axis labels to the background by making them small and grey so they wouldn't compete for attention with the more important parts of my visual.
3. Focus attention where you want it: I made use of pre-attentive attributes: bold, color, space, and size of text to draw the audience's attention to the important parts.
4. Think like a designer: In this case, this was mostly about paying attention to detail: making sure things were aligned, leveraging white space, and trying to make the visual as easy to understand as possible.
5. Tell a story: I added a story in words to make the message clear.

I also did another version with a very slight twist to show how relatively small changes can completely reframe the overall message:

What do you think? If you attended one of the sessions, I welcome feedback on what worked well and what could have been better. (Other comments welcome, too.)

Stay tuned - I'll post about the other makeovers in the coming weeks. If you're a first time visitor to my blog, you can sign up for email updates in the upper left. I'll also point you to a couple popular posts: how to do it in excel and no more excuses for bad simple charts: here's a template. Happy storytelling with data!

12/5/13 update: To download the spreadsheet with the visual above, click here.

garage sale signs and data viz: the power of preattentive attributes

I was jogging the other morning and ran by a woman hanging a sign for a garage sale. Her advertisement was penned on a piece of yellow 8x11 paper, uniformly golfball-sized letters describing the detail. In short: someone would pretty much have to stop their car, get out and walk up to the sign to know what it said. And after doing so, would need to read the entire sign to find out the most relevant parts of the detail: if it was in an area of interest, or at a time that would suit.

This was obviously a poor sign. The only thing it had going for it was that the yellow paper was eye catching. But I imagine that only those in search of garage sales would think of stopping to pay it more attention; the sign was clearly not going to be read by the majority of passersby.

This led me down a thought path: what makes a good garage sale sign? I had a hypothesis. After arriving home, I looked up images of garage sale signs with my favorite search engine. Here's a sample:

It seems to me that one of the things that makes for a good garage sale sign is one of the same things that makes for a good data visualization: strategic use of preattentive attributes.

"Preattentive attributes" in the world of information visualization is a fancy descriptor for aspects of a visual that hit our iconic memory. Iconic memory is what happens in our brain before short term memory kicks in, before we even really know that we're thinking. Iconic memory is tuned to pick up preattentive attributes: things like color, size, added marks, and spacial position [learn more].

In the lessons I teach on data visualization, I discuss using preattentive attributes mainly with two goals in mind: 1) directing the audience's eye and 2) establishing a visual hierarchy of information. In both cases, the point is that if you use preattentive attributes well (especially color), your audience can't help but focus on the important part(s) of the message. By playing on their iconic memory, you're making it so they are seeing what you want them to see before they even know they are seeing it. Which is a crazy powerful thing.

I have a good Google before-and-after example that's been genericized that I'll post later this week. If you're too excited to wait, I'll be discussing it (and more on preattentive attributes) at the Visual.ly meet up on Thursday in Mountain View [see details].

visual.ly meet up

If you live in the Bay Area (or have plans of being there in late September), you may be interested in the visual.ly meet up taking place on September 29th (sign up is here; do it soon if interested, as spots are filling up quickly). I will be one of the speakers and will discuss leveraging preattentive attributes to make great data visualizations, highlighting an example from our research on Google's People Analytics team. Hope to see you there!

GMN conference

This afternoon, I had the pleasure of presenting at the annual Grants Managers Network Conference that is underway in Seattle. The topic: data visualization (the 90-minute session was an abbreviated version of the course I teach internally at Google, plus a section with makeovers of visuals submitted by participants that show the main learnings in practice). The audience: 100+ grants managers from philanthropic organizations across the country, an engaged bunch full of questions and evident interest in learning more about storytelling with data.

I get energized when I speak on this topic. A quick related anecdote: last week, I was attending an internal manager training on inspiring one's team. As part of one exercise, I had to compose 'my sentence' - a sentence that motivates and inspires me. The sentence I wrote down was as follows: To help rid Google and the world of ineffective graphs, one exploding, 3D pie chart at a time. The session today was a step along this path.

So often, the visual communication of information is an afterthought - the work and energy goes into jumping over hurdles just to get to the data and make sense of it - too little time is devoted to the visual part that other people see. If attendees take one thing from today's session, I hope it is to spend time on this piece. Use affordances (pre-attentive attributes, like color and size) to help the audience understand where to devote their attention; strip out the clutter that doesn't need to be there.

One of the reasons I enjoy data visualization is because it sits at the intersection of art and science. As a designer of infographics (which, if you ever find yourself making a graph, you are), you just want to make sure you use your artistic licence to make information easier for your audience to get at, not more difficult. On the science side, there are a few never-to-be-broken rules I covered today that I'll recap here:

• Color should always be an explicit choice (don't let Excel make this important decision for you!)
• Never use 3D (unless you are actually depicting something that is 3-dimensional!)
• Every graph needs a title; every axis needs a label (no exceptions!)

A quick note to anyone who attended the session this afternoon: Thanks for stopping by! Please leave me a comment if there's anything in particular you found useful, or if you have any feedback that I should take into account for future sessions. The books and tools I referred to in the session can be found by following the 'recommended reading' and 'additional resources' links on the left, respectively (in particular, I'd recommend Stephen Few's book, Show Me the Numbers; tool-wise, Tableau was the one we spent a little time talking about). If you're interested in contacting me to talk more, follow the 'email me' link at the top left of this page. Thank you for a great session, and I appreciate your help in reducing ineffective visual displays of information - I hope you find yourself putting what we discussed today into practice!