Wednesday, December 18, 2013

quick reflection on the past 3 years

I've been debating whether to write and publish some sort of "best of 2013" post. While reflecting back over the past year, it occurred to me that I originally launched this blog in December. Which means it is currently anniversary month. I could have sworn I've been doing this for two years, but as I look back at the date stamps, I see that the very first post (5 easy tips) that launched www.storytellingwithdata.com was 12/9/10.

Which means it's actually been 3 years.

My, how time has flown by.

On the personal side, this time has been marked by death and birth, divorce and marriage, new cities and known cities, and probably a number of other dichotomies. On the work front, I've been very lucky to be able to grow storytelling with data from a side project into my focus, through this blog and an increasing number of public (including upcoming sessions in Boston & DC in early 2014) and custom workshops throughout the world. It's been an amazing adventure and I'm excited for what the future will bring.

I'll keep this reflection relatively short. It seems unfitting to write a post like this without some links back to historical posts, so I'll list for you the top 10 most-viewed posts to date:
  1. how to do it in excel
  2. no more excuses for bad simple charts: here's a template
  3. the waterfall chart
  4. my penchant for horizontal bar graphs
  5. strategies for avoiding the spaghetti graph
  6. chart chooser
  7. logic in order
  8. and the winner is...
  9. slopegraph template
  10. the power of simple text
Very big thanks to everyone who is reading this. I wish you and your loved ones a fantastic holiday season. I look forward to continuing to learn and share with you in 2014.

On that note, if there are any specific topics you'd like me to focus on here in the near year, please leave a comment with your idea(s).

Happy holidays!

Friday, December 6, 2013

a fresh perspective

This is the final post in the series on helping ensure the story you aim to tell is coming across effectively in your communication. Prior posts have been on horizontal logic, vertical logic, Bing, Bang, Bongo, and reverse storyboarding.

Today, we'll briefly discuss the value of soliciting a fresh perspective.

Once you've crafted your communication, give it to a friend or colleague. It can be someone without any context (it's actually helpful if it is someone without any context, because this puts them in a much closer position to your audience than you can be given your intimate knowledge of the subject matter). Have them talk out loud through what they pay attention to, what they think is important, where they have questions. This will help you understand whether the communication you've crafted is telling the story you mean to tell, or in the case where it isn't exactly, help you understand where to concentrate your iterations.

There is immense value in getting a fresh perspective when it comes to communicating with data in general. As we become subject matter experts in our space, it becomes impossible for us to take a step back and look at what we've created (whether a single graph or a full presentation) through our audience's eyes. But that doesn't mean you can't see what they see. Leverage a friend or colleague for their fresh perspective. Help ensure your communication hits the mark.

This ends my series of posts on concepts that can help ensure the story you want to tell comes across clearly in your communication. Here's a quick recap of the concepts we've covered (click the blue text to see the full related post):
  • Horizontal logic: if you read through just the headlines, they tell your story.
  • Vertical logic: everything on a single slide is reinforcing.
  • Bing, bang, bongo: tell your audience what you're going to tell them, tell them, then tell them what you just told them.
  • Reverse storyboarding: flip through the pages and write down the main point from each; this should reveal clear logic and progression and tell the overall story you're aiming for.
  • A fresh perspective: seek input and feedback from others to refine your story.
What other methods do you use as you craft and refine your storytelling with data?
Leave a comment with your thoughts!

While I have your attention: I've just scheduled a public workshop in DC in February (details) and am in the process of finalizing a Boston session - details forthcoming. 

Thursday, December 5, 2013

reverse storyboarding

This is the fourth (and penultimate) post in a series on helping ensure your story comes across effectively in your communication. Prior posts have been on the topics of horizontal logic, vertical logic, and Bing, Bang, Bongo.

Today, we'll briefly discuss reverse storyboarding.

When you storyboard at the onset of building a communication, you craft the outline of the story you intend to tell. As the name implies, reverse storyboarding does the opposite. You take the final communication, flip through it, and write down the main point from each page (it's a nice way to test your horizontal logic as well). The resulting list should look like the storyboard or outline for the story you want to tell. If it doesn't, this can help you understand structurally where you might want to add, remove, or move pieces to create the overall flow and structure for the story that you're interested in.

Stay tuned for the final post in this series, which will be on the value of soliciting a fresh perspective.

Wednesday, December 4, 2013

bing bang bongo

This is the third post in a series on helping ensure the story you want to tell comes across clearly in your communication. Prior posts were on the topics of horizontal logic and vertical logic.

Bing, Bang, Bongo is a concept that was introduced by my junior high english teacher when we were learning to write essays. I don't actually remember what the bing, bang, bongo nomenclature referred to, but I do remember the main point - and I believe it can be leveraged when we need to tell a story with data as well.

The idea is that you should first tell your audience what you're going to tell them. Then you tell it to them. Then you tell them what you just told them. If you're the one creating or giving the presentation, this can feel really redundant (because you are familiar with the content and know your stuff well). But to your audience (who is not as close to the content), it feels nice: you've set their expectations, then provided detail, then recapped. The repetition helps cement it in their memory. After three times of telling them, hopefully they are clear on what they are meant to know and do from the story you've just told.

The next post in this series will focus on reverse storyboarding.

Tuesday, December 3, 2013

vertical logic

This is the second post in a series on helping ensure that the story you want to tell comes across clearly in your communication. The previous post was on horizontal logic.

In this post, we'll briefly discuss horizontal logic's cousin: vertical logic.

Vertical logic means that all information on a given slide is self-reinforcing. The content reinforces the title and vice versa. The words reinforce the visual and vice versa. There isn't any extraneous or unrelated information. Much of the time, the decision on what to eliminate or push to an appendix is as important (sometimes more so) as the decision on what to retain.

Employing horizontal and vertical logic together will help ensure that the story you want to tell comes across clearly in your communication.

Next in this series, I'll reveal what I mean with the term Bing, Bang, Bongo.

Monday, December 2, 2013

horizontal logic

There are a number of concepts I discuss in my workshops for helping to ensure that the story you're telling in your communication comes across. I'd like to discuss these in a series of brief posts over the next week. First, a couple assumptions to make clear:
  1. My first assumption is that you want to tell a story. My view is that you should always want your audience to know or do something, and story is what can help make that something clear as well as make it resonate with and stick with your audience. For me, great data visualization makes itself a pivotal point in a story
  2. For this conversation, I also assume that the format of the story you're crafting is a presentation deck. While not always the case, I find that this often is the main form of communicating analytical results, findings, and recommendations at many companies. Some of the concepts we'll discuss will be applicable to written reports and other formats as well.
The first concept we'll discuss is horizontal logic.

The idea behind horizontal logic is that you can read just the slide title of each slide throughout your deck and, together, these snippets tell the overarching story you want to communicate. It's important to have action titles (not descriptive titles) for this to work well.

One strategy is to have an exec summary slide up front, with each bullet corresponding to a subsequent slide title in the same order. This is a nice way of setting it up so your audience knows what to expect and then is taken through the detail.

Checking for horizontal logic is one approach to test whether the story you want to tell is coming through clearly in your deck.

Stay tuned for the next post in this series, where we'll discuss vertical logic.

Monday, November 18, 2013

slopegraph template

I've found myself increasingly using slopegraphs as of late. They can be useful when you have two time periods of data and want to quickly see increases/decreases between the two periods (example below; see second half of this post for more discussion and another example).
From a formatting standpoint, however, they are annoying. They take a lot of time to set up because basically everything is different from graphing application defaults. I realized as I was making a recent one that I make the exact same changes every single time and may actually leverage a template for this (I say "may actually" because I thought that would be the case once before, but it didn't happen, though I've heard from others that they do use it).

In case you find yourself wanting to use a slopegraph (or quickly see whether one will work given the specifics of your data), you can download the Excel template I created here (screenshot below).


Tuesday, November 12, 2013

what I look for in effective data viz

Last week, I had the distinct honor of being one of the judges for the very first VizCup, hosted by Facebook. Participants had an hour and their choice of half a dozen or so datasets plus their preferred software to create an interactive data viz. Each group/individual then had 90 seconds to explain and demo their visualization. After an hour or so of seeing some amazing work, the judges narrowed it down to the top five, who each had a little more time to share their viz. Top entries ran the gamut in scope, ranging from being able to see UFO sighting stats on your birthday, to where to locate to avoid natural disasters, to the winning entry that looked at the bias in soccer red card handouts by referee. Interestingly, all of the top entries (and nearly all of the entries overall) were created in Tableau, likely due to its ease and speed in creating interactive visualizations.

Leading up to the event, I put some thinking time to the most important things that I look for when judging the effectiveness of a data visualization. I thought this might be of interest, so will share it here.

At a high level, there are four things I look for when evaluating data viz:
  1. A sensible display. The choice of graph or visual is appropriate given the data and given the purpose.
  2. Absence of clutter. I disdain it! The presence of elements that don't carry informative value or aid interpretation in some way will hurt not help when it comes to my evaluation.
  3. Affordance in design. Through strategic use of things like color, size of elements, spacial position, and text, it is so clear to the audience how to interact with the data visualization that they don't even notice the design.
  4. A clear story. For me, the best data visualizations are the pivotal point in a story. Use written or spoken narrative (or a combination thereof) to make the story your visualization tells clear.
Big thanks to everyone who worked to make this event a success: the Facebook team, the other judges (Drew Skau from Visual.ly and Anya A'Hearn of DataBlick), and especially Andy at VizWiz for inviting me to take part in the event!

The Facebook team that organized the event, plus judges.

Thursday, November 7, 2013

student makeovers

This fall, I had the pleasure of teaching Intro to Information Visualization for MICA's MPS in Information Visualization. It was a 4-week course, where we explored some fundamentals of data visualization and storytelling as it relates to communicating effectively with data.

The course was unique from my typical workshops in a number of ways. It was great to get to start to know the students during our time together. Perhaps the most exciting difference for me was being able to see the lessons we covered put to use in homework assignments.

One of the assignments was a visual makeover, where students were asked to select a less-than-stellar visualization from the media, identify the underlying story and create a new and improved visual using data together with narrative to tell an effective visual story. I had a great time reviewing the before-and-afters. I thought I'd share this fun with you by posting some of them here (with my students' permission; I realize the snippets below are a little small - and my process for getting images onto my blog has started to create a sort of strange grey background, so if you want to see bigger non-grey-background versions, you can download the PDF here). Enjoy!

Makeover 1: bird feeder location  by Kevin Ripka | kevinripka.com

Makeover 2: youth programs  by Brittney Younger

Makeover 3: NYC refuse  by Marianne Siblini

Makeover 4: BB Finale  by John Breakey | www.johnbreakey.com

Makeover 5: climate change  by Jennifer A. Stark

Makeover 6: prezi growth  by Jess Mireau | www.jam-i-am.com

Big thanks to the students above for agreeing to let me post their work, and to the overall class for making my first time teaching at the graduate level an incredibly rewarding experience!

Tuesday, October 29, 2013

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!

Wednesday, October 23, 2013

the right amount of detail

In my workshops, we spend time discussing the importance of context when it comes to putting yourself in a position to create an effective data visualization. In my mind, there are two flavors of context that are important. First, there is the context that you as the data visualizer must know: who you want to communicate to (your audience) and what you need them to know or do (the call to action). When it comes to creating the visual communication, the second type of context also becomes important: the context that your audience needs to know in order to make sense of the information you are attempting to communicate. This often leads to the question: how much detail is the right amount of detail to show?

The answer is... it depends.

I started off writing this post with the assumption that the right amount of detail for a given situation depends on a lot of things, but as I sit here and think about how to articulate it, I'm landing on just two things that I believe are most important: 1) the method of communication and 2) the level of trust your audience has in you. Let's discuss each of these briefly and then we'll look at an example.

The method of communication. When you present something in a live setting, you are in nearly complete control. You can respond to audience cues and questions. You can get away with having less context and detail physically written down or shown because you are there to fill in any blanks. This is not the case in written communication, where the level of explicit detail must be higher because you are not there to facilitate the flow of information but rather must count on your words and visuals to paint the full picture (and address at least first-order questions). Because of this, the level of detail necessary for written communication is much higher than for a presentation.

The level of trust your audience has in you. If you have an established, trusting relationship with your audience, you can get away with showing less detail without your audience questioning it. On the other hand, if you haven't established a basis of trust with your audience, you may need to show more detail explicitly so that your audience doesn't feel you are somehow trying to mislead by only showing the parts that back up your story and ignoring the rest.

When thinking about the audience's level of trust, be sure to keep the ultimate audience in mind. If you're preparing something that someone else (let's call her Mary) will be presenting, think about not only how much trust Mary has in you, but also how much trust Mary's audience has in her and use this as your guide for determining the appropriate level of detail.

One thing to try to anticipate when considering the right amount of context and level of detail is what questions your audience will have. Of these, think about which need to be answered explicitly in your communication directly vs. which can be addressed on the fly. In a live presentation, one feature you can leverage is the hyperlink. If you can anticipate potential questions and have visuals or data you can use to address but only want to leverage them if the conversation turns that direction, you can include that detail in an appendix, then create a hyperlink on the given slide in the main presentation that points to the relevant slide in the appendix. If the question comes up, this gives you a seamless way to jump to the appropriate slide (but doesn't force it in the progression in the event that the question doesn't come up and you don't have to address it). Make sure to also make a link from the appendix page back to your main presentation so you can continue with the planned flow.

Let's look at a quick example of how we might think through the above as we're creating a specific visualization. The following is a generalized example from a recent consulting project. Let's assume here that this is one slide that's part of a broader presentation to the head of Marketing for our company and that the story we want to tell is around the most important features based on the survey feedback.


When we're thinking about what level of detail to show here, there are a couple different decision points:
  1. Can we combine response categories? Currently, there are five response categories that range from Extremely important at one end, to Not important at all at the other. Could we combine some of the categories to make the information easier to consume? What information would we lose by doing so? Even if we don't combine categories, there are things we can do to draw more attention to some and de-emphasize others that will allow us to keep the detail there without it becoming visually overwhelming.
  2. Can we show fewer features? There are currently 15 features shown - this is probably the complete picture of all of the features we asked about in the survey. We could think about reducing this to just the features we want to focus on and get rid of the rest. Or again, if we don't feel comfortable eliminating them entirely, perhaps there are things we can do to draw attention to some and de-emphasize others that will allow us to preserve the detail without it feeling so cumbersome to our audience to consume.
Let's look at a couple possible solutions. 

If our audience trusts us and we're comfortable with a minimalist approach, we could reduce the visual above to something like the following:


If the above feels like we've taken too much away, below is an alternate view that preserves nearly all of the detail from the original visual, but uses emphasis and de-emphasis to make it easier to consume and less visually overwhelming for our audience.


Note that in both cases above, I've also added a footnote with some details about the survey. This is detail that needs to be there, but can be de-emphasized (make it small, grey, put it at the bottom).

If we're presenting this information live, I'd suggest going with the minimalist version: there's less detail there to process, which means your audience can listen to what you're saying vs. focus their attention on deciphering the graph. Keep the full version on hand so you can address questions that might come up (having a printed version of it you can refer to if necessary can work well), or you could put the detailed version in the appendix and leverage the hyperlink strategy discussed above to jump to it only if there are questions on survey feedback on the other features. This could work well in a written report as well, where you can put a footnote that says "Survey feedback on full feature suite can be found in the appendix on page x" or something to that effect.

In case you want to look at this example more closely, the PowerPoint file that contains the above visuals and underlying data can be downloaded here.

What's your reaction to the above? What other considerations should we have in mind when it comes to determining the right amount of detail to show?

Note: it struck me in the middle of writing this post that I started out writing about context but ended up with discussion focused more on the level of detail (vs. broader context). Rather go back and rewrite, I'm going to go ahead and post, with some minor edits and the hope that the discussion above isn't confusing. There are certainly other aspects of context that are important when it comes to specifically understanding the data that is shown as well, but I think discussion there is probably more situation-specific so haven't addressed that at all here but rather focused my discussion on level of detail.

Monday, October 21, 2013

another advertising graph

I feel conflicted when it comes to the use of graphs in advertising. I like it in theory. But in practice, I tend to be disappointed with what I see. Almost like the designers couldn't come up with anything better, so they threw in a graph. Perhaps it's just my nature, but also when I see a graph in an advertisement, I'm immediately skeptical - it's like I start with the hypothesis that the creator is trying to mislead me. I'm not sure what drives that. Obviously, I like the use of graphs to communicate information; what is it about graphs in advertising that gets under my skin?

Last month, I enjoyed reading your comments on the vacuum graph. Last week, I came across another advertising graph in a fashion magazine:


I can outline specific things I would change in the above visual. But beyond that, it's interesting to me that my first reaction to data in advertising like that depicted above is skepticism vs. improved understanding.

What is your reaction to the Neutrogena graph? Have you seen examples of data and data visuals used successfully in advertising? Leave a comment with your thoughts.

Tuesday, October 15, 2013

how I would visualize this data

A couple weeks ago, I posted the following visual (prompted by a question that came up at a recent workshop), along with the challenge to come up with a better way to show this data.


Big thanks to everyone who spent time with this data and shared what they came up with (see full original post, plus comments linking to reader solutions here).

I found myself with some unexpected free time this afternoon (that hasn't been happening so much lately!) and spent a bit of it playing around with this data. There were a couple challenges I was looking to address in my solution:

  1. First, the inclusion of Unaided Awareness. I can understand why it's important, but it doesn't seem to quite fit with the other categories, as many of the reader comments pointed out. Rather than complicate my visual with it, I omitted it entirely. Perhaps it belongs on another page of the deck that walks through this data?
  2. The other challenge from my perspective is that the remaining categories are each a sort of subset of the prior category: those who prefer a given brand are a sub-portion of those who would consider the brand, which is a sub-portion of those who are aware of the brand. I think this means that it doesn't make sense to stack the bars, or to compare only a single category at a time across the different brands as some of the user makeovers suggested.
For me, the goal here is to get a quick visual understanding of how the three categories for OUR COMPANY compare to various competitors, as well as how the three categories compare to each other. The goal is for our bars to be big to the right. There are many competitors with bigger bars than ours. But the ratio of Consider to Aware is actually pretty good for us (better than many competitors). Here is how I chose to visualize this data:


It isn't particularly creative. But perhaps it doesn't need to be. In the original visual, the two main things I wanted to change were 1) the idea that you should compare only downward - the layout of the original charts left me wanting to compare competitor A to E to H (first column), whereas really I should be comparing OUR COMPANY to each of the various competitors (not only those within the same column); and 2) center aligning the bars as is done in the original makes the relative values hard to compare, so I wanted to align each element to a single baseline to allow for easier comparison both within a specific brand as well as across the brands.

To those who read this blog regularly, my choice of a horizontal bar chart here probably isn't a surprise. I use them a lot. I use them a lot because I think they are easy to read. Note that this example isn't totally complete - there's still a story to be woven around this data to make it relevant to our audience. Given that I've anonymized the example so much here, I decided to focus just on the data viz in this case.

I think this works here. What do you think?

In case you're interested, the Excel file for the above visual can be downloaded here.

Tuesday, October 1, 2013

how would you visualize this data?

I had a follow up session yesterday with a group that recently went through one of my custom workshops. It was really great to see and hear how participants have been able to put what we learned to use and to discuss where they are facing challenges and strategies to overcome them. One participant recapped what they took away from the session: enhance what's important, tone down what isn't, and question the question!

We also looked at some specific data they were having challenges visualizing and had some fun experimenting with possible solutions on the whiteboard. There was one that we didn't get to that I want to share here for input:

Click here to download the data in Excel.
A few comments on the data:
  • In the original, "Competitor A," "Competitor B," etc. were actual competitor names, which I've generalized here (and in the downloadable data) to preserve confidentiality. 
  • Unaided Awareness, Awareness, Consideration, and Preference are in regards to the given company's brand. This is sometimes known as the "purchase funnel". The data is collected through a survey; percents represent the percent of the total sample population. 
  • Unaided Awareness is the % of people who know the brand off the top of their heads (for example, What brands come to mind when you think of shampoo?).
  • Awareness is in response to a list of brands: Which of the following brands of shampoo are you aware of?
  • Consideration is which brand a consumer would consider purchasing.
  • Preference is which brand a consumer is most likely to purchase.
  • Conversion (highlighted in title) is the percent of people who move from one funnel step to the next (what percent of aware people consider you, etc.)

The intent is to be able to compare the brand of "OUR COMPANY" along these dimensions to the various competitors, as well as get a sense of where our opportunity lies.

I think this could be done more effectively than it is here. I have a couple ideas, but am lacking time at the moment to try them out. So I thought I'd solicit your help. How would you tackle this visualization challenge? Leave a comment with your thoughts!

Note: please link to your visual if you create one; you can embed a link in your text comment with the following HTML (replace the parts in blue).
<a href="http://www.link_you_want_to_insert.com/" target="_blank" >text to link</a>

Tuesday, September 17, 2013

the vacuum graph

I was flipping through a recent copy of Dwell over the weekend and came across the following advertisement.


In case you can't read sideways, the * at the left says: "Machine representation relative to Air Watts. Suction tested against upright market to ASTM F558 at cleaner head, dust-loaded as per IEC 60312-1.

It caused me to pause (as most graphs, especially when found in unusual places - like a vacuum ad in a design magazine - do) and stare at it for a bit. I have my reaction, but rather than share that with you, I thought I'd open up this post to gather your feedback. What do you like about the ad? What bothers you? What do you imagine the creators assumed about their audience when they conceptualized the design? What questions might you want to ask the designers? Does the ad make you want to buy a Dyson?

Leave a comment with your thoughts. (I look forward to reading them!)

Wednesday, September 11, 2013

logic in order

There should be logic in the order in which you display information.

This probably sounds obvious when you read it. Yet, like so many things that seem obvious when we read them or hear them or say them out loud, it's interesting to me how often we don't put into practice those things that seem like they should be obvious. This is an example of that.

While I would say my intro sentence is universally true, I'll focus in this post on a very specific example to illustrate the concept: leveraging order for categorical data in a horizontal bar chart. This is based on a real makeover for an upcoming workshop, but I've anonymized it here for illustrative purposes.

Let's say you work at a company that sells a product that has various features. You've recently surveyed your users to understand whether they are using and how satisfied they've been with each of the features. You now have data from this that you'd like to make use of. The graph you create might look something like the following:


This is the actual graph that was presented, with the exception that I've replaced the actual descriptive feature names with Feature A, Feature B, and so on. There is an order here - if we stare at the data for a bit, we find that it is arranged in decreasing order of Very Satisfied + Completely Satisfied. Which might suggest that is where we should pay attention. But from a color standpoint, my eyes are drawn to the bold black Have not used. And when we stop to think about what the data shows, perhaps the areas of dissatisfaction would be of most interest?

Part of the challenge here is that the story, the "so what" of this visual is missing. We could tell a number of different stories and focus on a number of different aspects of this data. Let's look at a couple of ways to do so, with an eye towards leveraging order as we do.

First, we could think about highlighting the positive story: where our users are most satisfied.

Sidenote: In all cases, this data warrants the inclusion of a footnote that says something like, Responses based on survey question, "How satisfied have you been with each of these features?" and also has the necessary details to help put the data into context: how many people completed the survey, what proportion of total users this represents, whether those who completed the survey look like the overall user population, demographic-wise, when the survey was conducted, etc.

In the above, I've ordered the data clearly in order of decreasing Completely Satisfied + Very Satisfied - the same as in the original graph, but we've made it much more obvious here through other visual cues (namely, color, but also the positioning of the segments as the first series in the graph, so the audience's eye hits it first as it scans from left to right). I've also put some text at the top to help explain why your attention is drawn to where it is, and what you should be getting from the visual.

We can leverage these same tactics (order, color, placement) to highlight a different story within this data: where users are least satisfied.


Or, perhaps the real story here is in the unused features, which could be highlighted as follows.


Note that in the above, you can still get to the differing levels of satisfaction (or lack thereof), but they've been pushed back to a second-order comparison due to the color choices I've made (whereas the relative rank ordering of the Have not used segment is the clear primary comparison on which my audience is to focus).

If we wanted to tell one of the above stories, we can leverage order, color, and placement as I've shown to draw our audience's attention to where we want them to pay it in the data. If we want to tell all three stories, however, I'd recommend a slightly different approach. 

It isn't so nice to get your audience familiar with the data once, only to reshuffle it and show a different view. So let's go back to our base visual and preserve the same order (so our audience only has to familiarize themselves with the detail once), highlighting the different stories one at a time through strategic use of color.


Above is our base visual. If I were presenting this to an audience, I'd use this version to walk them through what they are looking at: survey responses to the question, "How satisfied have you been with each of these features?", with responses ranging from the positive Completely satisfied at the left to Not satisfied at all and finally, Have not used at the far right. Then I'd pause to tell each of the stories, in succession. 

First, comes the exact same visual we started the last series with that highlights where users are the most satisfied:


This is followed by a focus on the other end of the spectrum: where users are least satisfied: 


Note how it isn't as easy to see the relative rank ordering of the features highlighted above as it was when they were put in descending order (mainly, because they aren't aligned along a common baseline to either the left or right), but we can still relatively quickly see the primary areas of dissatisfaction (Features J and N) since they are so much bigger than the other categories. I've added a call out box to highlight this through text as well.

Finally, preserving the same order, we can draw our audience's attention to the unused features:


Note that here, it is easier to see the rank ordering (even though the categories aren't monotonically increasing) because of the alignment to a consistent baseline at the right of the graph. Here, we want our audience to focus mainly on the very bottom feature in the graph - Feature O. Since we're trying to preserve the established order and can't do this by putting it at the top (where the audience would encounter it first), we can use the call out box to help draw attention to the bottom of the graph.

The above views show the progression I'd use in a live presentation. The sparing and strategic use of color lets me direct my audience's attention to one component of the data at a time. If you were creating a written document to be shared directly with your audience, you might compress the above views into the following:


When I process the above, I see the bold "Features" in the graph title. Then I'm drawn to the dark blue bars, which I follow across to the dark blue text box, which tells me what's interesting about what I'm looking at (you'll note my text here is mostly descriptive, partly due to the anonymity of the example; you'd perhaps want to use this space for something more insightful). Then, I hit the orange text box and read it and glance back leftward to see the evidence in the graph that supports it. Finally, I read the aqua text box at the bottom, and can look back upward to the graph to see the data it describes.

As I describe this, there is one final view I want to create (though as I write this sentence ahead of actually doing so, I'm skeptical that it will be effective). My final idea here is to make it very clear the specific place in the data the audience should look to for evidence of what is described in the text, while still setting the series apart from one another using color. Here's where I landed:


This actually works better than I anticipated. Note that it does make it harder for your audience to form other conclusions with the data, since I've drawn attention so much to the particular points that I want to highlight. But I'd argue that typically once you've reached the point of needing to communicate, there should be a specific story or point that you want to highlight (vs. let your reader draw their own conclusions). The above is definitely too dense for a live presentation (rather, I'd recommend stepping through it, story by story, as shown previously), but could work well for a document that is going to be shared.

One final note - I should mention that in some cases, there is intrinsic order in the data you want to show (ordinal categories). For example, instead of features, if our categories in this case were age ranges (0-9, 10-19, 20-29, etc.), we'd want to keep those categories in numerical order (this provides an important construct for the audience to use as they are interpreting the information) and then can use the other methods of drawing attention shown (color, placement) to direct our audience's attention to where we want them to pay it.

In case it's of interest, the excel file used to create the above visuals can be downloaded here.

Saturday, August 10, 2013

recommended reading: Data Points

I've been a long-time fan of Nathan Yau and his work. He writes the popular (and prolific) blog, Flowing Data and published his first book, Visualize This, in 2011 (you can read my review of that book here).

Nathan's second book, Data Points, came out earlier this year. When a client of mine said the following -
"I've only read chapter 1 so far of Data Points, but it's the best chapter 1 on data vis I've read. He talks about the data being an abstraction for reality, and he uses his own wedding pictures to explain. Great idea, and great example."
I promptly ordered my copy and, upon arrival, consumed its contents. I must agree, this is a fantastic book. Moving beyond the aesthetics (it's the right size and has that sort of matte cover that just feels nice - one of the reasons an e-book will never satisfy me), the lessons are clearly articulated and well demonstrated through creative and varied examples.

In Data Points, Yau focuses on data visualization as a medium, rather than a tool, and discusses the importance of context when figuring out what the right medium will be (which he argues can run the gamut, from the informative to the entertaining). Rather than a narrow focus on the visualization step, Nathan spends a good portion of the book discussing the data itself and strategies for exploring and analyzing it. One example used early on effectively was based on car crash data: he showed how you can aggregate in different ways to highlight different aspects of the data - the difference if you show hourly vs. daily vs. monthly vs. annual data, with discussion on the specific context that might lead you to choose one view over the other.

When it comes to visualizing the data, Nathan breaks it down into four "working parts" -
  1. Visual cues - how you encode the data in things like shapes, sizes, and color; 
  2. Coordinate system - Cartesian vs. polar vs. geographic;
  3. Scale - linear, logarithmic, categorical, etc.; and
  4. Context - clarifying what values represent, explaining how people should read your visualization.
There is a good deal of explanation on each of these to make the concepts clear. Yau refers to the above as the ingredients and describes putting them together to get a complete visualization worth looking at. He says,
"To make the jump from data to visualization, you must know your ingredients. A skilled chef doesn't just blindly throw ingredients into a pot, turn the stove on high, and hope for the best. Instead, the chef gets to know how each ingredient works together, which ones don't get along, and how long and at what temperature to cook these ingredients."
If I had to find fault in this book, it would be that Yau's discussion of context is mostly limited to the context of the data and helping your audience understand your visual, than about the broader situational context and helping your audience understand how the data fits into something bigger. For me, this is the storytelling piece, the step that helps make the data visualization relevant.

Still, it was an excellent read that I would recommend without hesitation. It's a thorough and accessible overview of data exploration, analysis, and visualization. For those already familiar with this space, it serves as a good reminder of some of the things we should make sure to pause and think about as we are working with and visualizing data.

I found the book to be such a great overview, in fact, that I will be using it as one of my required texts in Introduction to Information Visualization, which I'll be teaching in MICA's MPS in Information Visualization this fall.

You can purchase Data Points in the storytelling with data bookstore.

Thursday, July 18, 2013

"animation" with power point

Let me begin this post by stating clearly that the only types of animation in Power Point (or substitute your presentation application of choice) that I endorse are: appear, disappear, and (sparingly) transparency. Please steer clear of any bouncing, flying, or fading in/out (as well as any other "slick" animations by which you may be tempted). To use an analogy, flashy animation is to presentation software as 3D is to a graph: unnecessary at best, and distracting at worst.

But that's slightly off topic. Today, I want to show you how you can use Excel and Power Point together with some simple screencasting with QuickTime Player to simulate a fully animated video.

When I solicited examples for a recent workshop, one participant sent me a graph they had created, along with this explanation:
This graphic summarized the key finding of the LAC (Latin America & Caribbean) middle class flagship we just launched. It is clearly not difficult to understand but my frustration was knowing that it could be more effective as an animated chart that could tell in a few seconds how far LAC has come. I tried to no avail to find someone who could do it for us. At the end, The Economist did what I would have liked to have done for our launch (link). 
Could we have done this ourselves? Or who could have done this for us and for how much? What kind of skills are required?
General consensus when I asked around was that The Economist's version was probably done using D3. I don't have any experience with this, and when I read the first part of the summary on the site ("D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document." ...that's a mouthful!), my inclination to use tools more familiar to my audience was confirmed.

What I'll show you here should probably be considered a brute-force approach. There are certainly more eloquent solutions out there, but in case you aren't familiar with those (or don't have time or interest to learn the tools that would allow for them), this is a workable solution using good old Excel and Power Point, together with QuickTime Player.

My approach was to build the final graph in Excel, and then make a number of copies of it, eliminating some of the data elements from each so that I could focus on one component at a time to tell the story. Then I copied and pasted each of these into Power Point (making sure the visual was in exactly the same place on each slide). I pasted onto separate slides, so you see the progression as you move through them. You could also do this on a single slide with animation in Power Point (appear/disappear), which just takes a little more time and patience to set up. Patience is key throughout this process - it's a little painstaking to set up, but in the end I think achieves the desired result. Finally, after my slides were created, I used QuickTime Player to record my computer screen and voice.

Here's my resulting video:



The Excel file used to create the graphs can be downloaded here.
The Power Point file used to create the video above can be downloaded here.

This was my first time using QuickTime Player to create a screencast. I found it to be pretty straightforward; the instructions I followed can be found here.

Note how you could use this same approach in Power Point to focus your audience's attention in a live presentation as well.

Thursday, July 11, 2013

200 words on infographics

Recently, I was asked to share my thoughts on the "future of infographics" for an article Arnie Kuenn was writing for the Content Marketing Institute. The words I shared with him are below; the full article, where four other experts weigh in with their thoughts as well, can be found here.

Infographics run the gamut, from fluffy to informative. On the former side, we are presented with elements like over-sized numbers and portions of filled in little man-shapes. The graphics appear glamorous and have a sort of sex appeal that draws you in. Unfortunately, upon further evaluation, these visual displays are often shallow and leave me dissatisfied. On the other end of the spectrum are infographics that actually inform; many of the good examples I’ve seen here are in the area of data journalism (e.g. Alberto Cairo’s work).

There are critical questions an information designer must be able to answer before they begin the design process: who is your audience? and what do you need them to know or do? It’s only after the answers to these questions can be succinctly expressed that an effective method of display that will best aid the message can be chosen. Good data visualization (infographic or otherwise) tells a story.
While I’m not certain what the future will bring when it comes to the landscape of infographics, my hope is that the trend will be away from the fluffy data-dump and towards visualizations that are thoughtfully designed to impart information and tell a story.


REMINDER! registration is open for upcoming storytelling with data workshops in San Francisco and Chicago: details can be found here.

Thursday, June 27, 2013

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 VoilĂ )
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!

Thursday, June 20, 2013

the slideument

A common question has come up in several of my recent workshops: what should I do when the document I'm creating is meant to be used both as a written report and as part of a live presentation?

In an idea world, this situation would never arise. Rather, you would prepare two distinct deliverables:
  1. A written report, where you can get away with denser content and rely more heavily on things like written words and an appendix to make sure the necessary context and explanation are present for your audience.
  2. A presentation, where slides are much less dense, font is never smaller than 16-point, and the speaker is able to verbally provide the necessary information so it need not all be physically written down.
In reality, this rarely happens. Time and other constraints lead us to create something that is meant to be a sort of mesh of these two things: the slideument.* 
*I can't take credit for this mashup, but rather must give it to Nancy Duarte, who discusses the slideument in her book, slide:ology.

So the question remains: if slideuments are the reality, what should we do? In this post, we'll take a look at some of the challenges this presents as well as some strategies for overcoming them.

The crux of the challenge is: the report needs to be able to stand on its own without a presenter there to explain it. But if you put the dense slide that meets this first need in front of your audience, you lose them immediately because they turn their attention to trying to understand what you've put in front of them and stop listening to you. Or worse yet, they see that what you've put in front of them looks overwhelming, so they tune both it and you out and turn their attention to something else altogether. In either case, you've lost some of your audience and thus your ability to communicate effectively.

One solution I'll propose is to maintain the density of information (to ensure an audience who is consuming this info on their own has the details necessary to do so) and use animation in your presentation software to enable to presenter to focus on just one piece of the visual at a time, while simultaneously ensuring that's where the audience's attention will be focused as well. In this way, we are able to lead our live audience through the pieces and communicate effectively to those digesting the information later with the written report.

Let's look at an example. Imagine that I want to show an overview of my organization's social media followers (assume this is context that sets up the interesting story that we aim to tell in the rest of the presentation/report). Imagine also that I've been given the constraint of a single slide to do this. My slide might look something like this:


While this level of detail might be fine to have on a single page of a report that someone looks at on their computer screen or on a printed page, it can be overwhelming when you put it up on the big screen. There's a lot to take in, so if you show it all at once, some in your audience may tune out entirely, while others will busy themselves reading through what they are looking at (and unfortunately, when they do this it means they've stopped listening to you).

One way we can prevent this is by only showing a single element of the page at a time. Tactically, you can do this either by having elements of the page appear and disappear or covering up elements on the page with white (either solid or semi-transparent) boxes and using animation in your presentation software to only show one item at a time. So perhaps you flash up the full slide as you tell your audience you are going to talk them through what they are looking at, piece by piece. First, focus on the membership trend over time:

When your audience can only see one element on the page, their attention will be on it and on you. After you've talked through the membership trend, you could shift to the next element: who the members are.

At this point, you could even layer on some preattentive attributes, like color, to draw your audience to a specific part of the element and talk about what's interesting there.


(Yes, the font is tiny; if you're there to explain why the blue part is interesting, perhaps the supporting text at the very bottom is best left as part of the report version and never presented). Then you could step through the remaining elements in the same manner, one by one, explaining and pausing to point out the interesting aspects of each.

Sure, it would be better if you could give each of these elements their own slide. But in the scenario where that isn't possible, perhaps you'll find these tricks helpful.

You can download the PowerPoint here. I've included a version where the graphs appear/disappear directly, as well as one that leverages transparent boxes to direct the audience's focus to a single element at a time.