500,351

That's the number that just caught my eye when I was looking up a past post on my blog a moment ago. If you look over to the left, you'll see it, too. Well, not exactly, as it's likely continued to tick up a little since I began writing this post. It reflects the number of views this blog has had since I launched it in December 2010. Just two words come to mind when I see it: thank you.

Thank you for reading, for your comments and your general interest in storytelling with data.

This seems like a good (albeit random) point to pause for a moment, to link back to some popular posts you may have missed and ask you to weigh in on what you'd like to see here in the future.

popular posts in case you missed them
The 10 most popular posts (based on number of page views) are listed below (plus a bonus 11th that has 10 tips and links to related posts).

1. no more excuses for bad simple charts: here's a template
2. how to do it in Excel
3. the waterfall chart
4. strategies for avoiding the spaghetti graph
5. a Google example: preattentive attributes
6. my penchant for horizontal bar graphs
7. chart chooser
8. the power of simple text
9. logic in order
10. slopegraph template
11. celebrating (almost) 100 posts with 10 tips

what would you like to see covered here in the future?
Suggestions on future topics, questions you'd like me to opine on, or data visualization challenges you're facing are welcome. Leave a comment with your thoughts or email me directly at cole.nussbaumer@gmail.com.

where should I go in 2015?
I'm also looking ahead to my 2015 public workshop schedule. If you'd like to recommend a city or location, you can do so here.

Thank you very much for reading!

a storytelling with data ad

One of my favorite indulgences on a weekend morning is to sit in the sun on our terrace and read the latest copy of Dwell magazine. A number of things in the universe have to align to make this possible: namely, the sun must be shining and the child must be sleeping. The universe aligned in just this way this past Saturday (bliss!).

I find that the design of products and the design of spaces can sometimes influence my thinking, spark an idea, or act as inspiration when it comes to the visual design work on which I focus much of my attention. On this particular read through Dwell, it was the following advertisement that caught my eye:

This ad caused me pause for a few reasons:

1. The leading stat - 1 in 5 children go to school hungry - is powerful. When it comes to communicating a number or two, tables and graphs don't usually have a place, as the numbers themselves carry a lot more attention-grabbing power.
2. The use of preattentive attributes to make certain elements of the visual distinct: the numbers at the top are in bold, all caps and underlining draw attention to the second line, and the sort of sea-green in the logo and text at the bottom emphasize the un and is (when it comes to this last point, I might have chosen different portions of text to draw attention to, but I think that's one of those things that can be up for debate and probably there was a good reason the designer chose these particular pieces - perhaps the dichotomy between un and is?).
3. The story. It's short and sweet, but still a robust example of storytelling with data, which, with the personal anecdote and picture are made to be much more human than a simple stat on its own would be.
4. The picture. Speaking of pictures, one frequent question in my workshops is about the use of pictures when it comes to visual communication. I don't use pictures a lot personally, but as mentioned above, I do definitely think there are ways to use pictures that appeal on a different level than numbers do. Here, I think the pairing of the two is effective.

What do you think of this ad as an example of storytelling with data? Is it effective? Why or why not? Leave a comment with your thoughts!

slidedocs

In my workshops, we often discuss the challenge that arises when the communication you're creating is meant to be both 1) projected or handed out in a live presentation, as well as 2) sent as a follow up for those who attended to refer back to, or to fill in those who weren't able to attend the presentation. In an ideal world, these would be two separate work products. In reality, however, this often gives rise to what Nancy Duarte calls the slideument, a single document that is meant to address both needs.

The problem, of course, is that by trying to meet too many needs at once, the slideument doesn't address any of them perfectly. If you're interested in my further pontification on the topic and some thoughts on how to address the challenges that arise with the slideument, you check out this blog post.

My interest was piqued when I saw that Nancy Duarte recently introduced a new concept for using slides, which she has termed Slidedocs. The description on her site describes slidedocs as "a visual document, developed in presentation software, that is intended to be read and referenced instead of projected." The FastCo Design article written about Duarte's slidedocs is titled "Book Written Entirely in Power Point Aims to Reinvent How Businesses Communicate" (article).

With my busy schedule this week, I haven't had a chance to give the 150 page doc more than a cursory review. But rather than wait until I have more time to consume and reflect before sharing, I figured I'd post this now and gather your inputs. What do you think? Will this revolutionize the way we communicate? Leave a comment with your thoughts. I'll add mine once I have a chance to look more closely at the detail.

failure in design(er)

Yesterday evening, our recently purchased, lovely new couch arrived. Or, rather, the large boxes that contained our recently purchased, lovely new couch arrived. Suddenly, it was very clear what we gave up by not springing for "white glove delivery".

Not to fear, though. It came with instructions. My husband and I can both read and follow instructions.

Right?

Easier said than done, it turns out. These were certainly not the worst assembly instructions that I've ever seen, but they left a lot to be desired. Perhaps a very lucky or clever individual could get it right the first time (we were neither of those, as it turns out). But you'd have to know which details were important to pay attention to.

We had several false starts, turning the diagram round and round to say: Ah, now I get it! Wait, no, now the one frame piece is too long. Oh, now I see the problem. Oops, no, now the holes don't line up. After several such instances, we recognized that the bars in the frame are not equidistant apart (and it matters which two are closest together), we realized that two of the frame bars had four holes each and the third had two holes each and that the relative positioning of the bars with respect to one another is important, we learned that FX1 and GX1 are in fact not interchangeable (even though at the top they're shown with FX1 clearly on the left and GX1 on the right, but then below are less prominently switched).

Now that we've assembled the couch correctly (finally), we could do it again without breaking a sweat. We know exactly which are the important parts in the diagram to pay our attention. But why was it so difficult the first time around?

I'm in the middle of a book I'm enjoying, The Design of Everyday Things. In it, Donald Norman asserts that when you have trouble with things, you shouldn't blame yourself (even though that tends to be people's natural tendency). Rather, it's the fault of the design and you should blame the designer. While this book focuses mainly on product design, I think many of the insights are true in the data visualization space as well. In this case, the corollary is clear: if you are struggling to understand a visual representation of data, don't blame yourself; blame the designer. Odds are, they didn't adequately take your needs as the audience into account in their design process. For those designing visual displays of information, this is a reminder to always keep your audience in mind, for, as Donal Norman says, "well-designed objects are easy to interpret and understand."

I unabashedly blame the designer of the instruction diagram for our difficulty assembling something that could have easily been straightforward. If the designer had thought about the intended users and leveraged affordances to make it clear which details were important and should be paid attention to, my husband and I would have had a much less frustrating process assembling our (now truly lovely) couch.

What design issues cause you frustration? What can we learn from this to apply in the world of data viz?

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!

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.

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>

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!)

data viz challenge... how teens research

I subscribe to updates from the Pew Research Center. It's a great way to ensure a consistent inflow of data, which is useful as I gather and examine examples for workshops and my blog. Often, the incoming email gets quickly scanned and archived. But last week one of the titles piqued my interest, so I clicked to learn more about How Teens Research in the Digital Age.

When I think back to my own research projects in high school, I have images of trekking to the county library and using large computer terminals to locate old news articles on microfiche. Or making the even longer journey into the city to utilize the massive book collection at the university library. (I grew up in the sticks and when I was younger, these truly did seem like Illiad-like voyages...adventures to the city even involved a ferry boat!).

As I suspected, the means for researching for teens today are very different. They don't even have to leave their house (or their room!) if they don't want to, since the internet, and thus the world's information, is at their fingertips.

Alright, that's sufficient prelude. You're probably wondering about this data challenge that I mentioned in the title. On to that. I was surprised reading this article how many stats were included and yet how few visuals. Only one graph, in fact:

My challenge to you is this: read the article and determine what data you find most interesting then visualize it. You can remake the above graph, or focus on bringing life to numbers included in the report by making them visual. Submit your entries via the following instructions by Sunday, November 25th (those in the states need something to do over Thanksgiving break, right?).

When complete, you can leave a comment with the story you would focus on and a link to your visual, or email it to me directly (cole.nussbaumer@gmail.com) along with any comments you'd like me to post with it, and I'll put it into Dropbox and create a comment for you with the link (if you don't already have a Dropbox account, this is a good reason to get one!).

I'll invite the creator of my favorite to write a guest blog post. Happy data visualizing!

to stack or not to stack

My husband came across the graphical focus of this post in his Google+ stream last week. The original source is a Wall Street Journal blog post summarizing a recent Forrester report, where the main story can be summed up by one of the Forrester quotes within the post, "The future is one where no single OS or vendor is dominant - Microsoft is extremely late to the market expansion into mobile and has lost its dominant position."

Here is the graph included to illustrate this point:

It's true that you can get the evidence to support the claim made from this graph: once you identify the light blue portion of the bar as Microsoft, we see clearly that it decreases over time as the orange portion (Google) and the yellow portion (Apple) become increasingly prominent. But I'm of course not satisfied with it. The color palette is strange. Color in general could be used more strategically here. We can eliminate the work of going back and forth between the legend and the data it describes. I'm also not sure how I feel about the stacked bar chart.

Let's look at a couple variations on this data viz. First, here's what it could look like if we preserve the stacked bar and use color a little differently (note: I didn't have the raw data, so the remakes below are based on me eyeballing the figures and likely aren't entirely accurate):

Other minor changes I made above:

• Added an action title so it's clear what to look for in the graph (this was included as tiny text below the graph in the original post).
• Oriented the graph title and legend text at upper left - so reader encounters how to read the data before they get to it.
• Added a title to the y-axis. Always include this!
• Added data labels to the Microsoft series. This acts to both draw more attention to Microsoft, as well as to give a quick numerical view of the decrease over time.
• Narrowed the bars. In the original, they are bordering on too thick so that our eye starts to try to compare the area rather than the height.

When it came to color, I took a look at Microsoft's logo. I nearly always use blue to highlight the areas to which I want to draw attention. In this case, I actually tried venturing out using the red color from the logo and then the green color from the logo. But both just looked a little off (the burnt red looked overly negative to me, the green a little pukey). So I went with the blue from the logo (matching by eye - it's not perfect but close). I chose a grey palette for the remaining series.

I still don't love this, for a couple of reasons.

First, I think I just don't like stacked bar charts. This is actually probably a good use case for this graph type, since this lets us emphasize the percent of total and how that's changing over time. But I still don't love it. Because the bars aren't oriented on a consistent baseline, our eyes are forced to compare differing heights starting from different points. That's fine to get a general view, which is probably all we need here. Perhaps I'm just being overly finicky.

Second, it's still a bit of work to go back and forth between the legend and the data. If you don't recognize that the legend (left to right) is in the same order as the right-hand bar (top to bottom), it could prove difficult to see quickly which series is which.

I thought I would like a line graph of this data better - it would allow me to organize the series on a consistent baseline as well as label each directly. But then I graphed it and reconsidered:

Personally, my issues with this line graph version are greater than my issues with the stacked bar. We lose clear visibility/confidence that the lines sum to the total market, 100%. I also worry that in this case, the lines make it at first glance appear that we have more data than we do. Perhaps three points per series is too few for a line graph? If not in general, than I think that is at least true in this case. The overlapping nature of the lines creates a sort of spaghetti graph (as if I had a handful of uncooked spaghetti noodles and threw them on the ground). I tried to make this better by emphasizing the main three series (Google, Microsoft, and Apple) and de-emphasizing the others. But it still isn't great.

Given this, I'm back to the stacked bar chart. I think the information comes across most quickly with that structure.

What do you think - is a stacked bar the best choice here? Or are there additional options worth considering?

In case you're interested, here is the Excel file with the makeovers.

ready, set, critique!

I just came across the following daily chart from The Economist on my Google+ stream:

See full article.

My questions to you: What story does it tell? What story could it tell? How would you change the way the information is presented to do that in an effective way?

Ready...set...critique! Leave a comment with your thoughts.

your input please: font

We have a small debate underway in my day job regarding font. Specifically, which should be our default or standard font for analyses, presentations, etc. This led me to the question: when it comes to font choices, where do best practices end and personal preferences begin?

I'm aware of some relevant research conducted by psychologists Song and Schwarz in 2008 at the University of MI at Ann Arbor, where they showed college students recipes for sushi and asked them to estimate 1) how long the recipe would take them to make and 2) how inclined they were to do so. The only thing that varied between the recipes was the font in which it was written. What they found in a nutshell was that the fussier the font, the more difficult the students judged the recipe and the less likely they were to want to attempt making it. For me, the translation for data visualization broadly is that the more complicated it looks, the less likely your audience is to take time with it.

But back to my specific question: if both fonts are straightforward to read (no legibility issues), how do you choose?

To try to answer this question, I initially planned on doing some research; I quickly grew impatient with this. My brief attempt in Google searches taught me that there is no shortage of font fodder on the internet. There are conflicting lists of the "best" fonts (example). Others have done much more research in this area than I care to (example). I was struck that there don't even seem to be consistent opinions on questions I thought would be easy (e.g. serif vs. sans serif... sans, obviously, right? not according to Wikipedia).

So rather than continue down this slightly frustrating path, I thought I'd pose the problem to you to see if any consensus in the form of the wisdom of crowds emerges. Here are the fonts we're considering:

The quick brown fox jumps over the lazy dog

1234567890 (Calibri)

The quick brown fox jumps over the lazy dog
1234567890 (Open Sans)

The quick brown fox jumps over the lazy dog

1234567890 (Arial)

Specifically, when it comes to the open debate at work: my colleague and I are in agreement that Calibri should not be our default font. I think our reasoning when you boil it down is probably simply because we don't like it vs. anything scientific. Where we differ is on the question of Open Sans vs. Arial. I won't bias you by revealing which I prefer (though my sans serif comment and the text on this blog serve as a pretty big hint).

My questions to you are: If you were weighing in on this decision, what factors would you consider? Which font do you prefer? Why? Leave a comment with your thoughts!

input please: competing visualizations

As part of my day job, I consult internally on data visualization. I was recently working on some mock visualizations for a project being undertaken by my team. Since I can't talk about the details here, I've created another scenario to use to explain some generalized versions of the visuals and show the iteration process that I went through to get to what I thought was clearly the best visual.

Challenge: of the four versions of the visual I put together, the project manager favors the one I like the least.

So I thought I'd try to leverage the wisdom of the crowd (you, in this case) and see where that takes us. I won't bias you by telling you which visual the PM favors and which I do (though if you've been reading for a while or have attended one of my workshops, it will likely be clear). Rather, I'll simply describe a scenario and then show you the made up data and the four mock ups that I put together. Then I'll let you be the judge.

What I'd like to know from you is: which is your favorite and why?

Here's the scenario*: you are going to administer a personality test and your challenge is to visualize the results to give back to the individual in a way that is straightforward to understand. There are five different personality profiles, each of which has four measures that are expressed on a scale with possible values 0, low, medium, or high.
*Note that this scenario is totally made up (to protect some confidential stuff), but should provide you the sort of context you'll need to evaluate the effectiveness of the visuals.

Here is the made up data that I used for my mock ups:

Here are the four mock up visuals I put together based on the above data:

Which do you like best? Why? Leave a comment with your thoughts!