dogfood with data

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

I do not have a dog.

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

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

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

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

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.

SF housing cycles visualized

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

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

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

I actually really like it. 

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

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

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

nice summary by UP

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

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

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

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

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

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

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

The sleep summary is followed by a movement summary:

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

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

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

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

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!

data visualization: a reflection of personal taste?

When baby Avery took a slightly longer than expected nap earlier today, I found myself able to escape to one of my favorite spaces: the lounge chair on the patio in the sun with a copy of Dwell magazine.

I find inspiration in the design of physical things: spaces, furniture, gadgets. There was a paragraph in one of the articles I was reading that gave me pause -

"But there is a clear difference between the thoughtful selection of furnishings and the heedless accumulation of 'stuff.' Good furniture at its best is the intersection of art, craft, and industry. The 'right' pieces address the needs dictated by our behavior and predilections. That's the lofty ideal. Of course, complications arise when one starts calculating the pesky realities of space, time, and budget. And so begins the tradeoff." 

- Many Voices, Many Rooms. Dwell Magazine, Vol 13, 6/13.

I can see many parallels between what editor-in-chief Amanda Dameron says here about furniture and good data visualization. I guess it all comes down to thoughtful design. But personal taste must enter the equation as well, right?

Upon reading this, I started thinking about how my own personal taste when it comes to physical items is reflected in the data visualizations I create and in my stylistic approach to data visualization in general. In the spaces in which I live and work, my tendency is mostly minimalist. The color palette tends to be neutral, with sparing, bright splashes of color. No one who is familiar with my data viz style will be surprised that the dominant color is blue.

When I started thinking about it, I realized that my design of the physical spaces in which I spend time has many similarities with the data visualizations I create. So interesting!

I'm curious: how does your personal style influence your data visualization? Leave a comment with your thoughts!

Painting pictured above is by Jill Henry, ceramic bowl by Heath Ceramics.

inspiration in physical design & infographic wall art

When my husband and I were exploring our new neighborhood and the surrounding area by foot a couple of weeks ago, we came across something exciting: a Blue Bottle Coffee inside a ceramic shop. The ceramic shop in question was Heath, which I've since learned is a long-standing San Francisco institution, renown for their mid-century design. The original factory in Sausalito is where the iconic tableware and tiles are still made today.

I've been finding data viz inspiration recently in physical design, so (caffeine in hand), the bright colors, orderly arrangement, and interesting shapes in the retail space intrigued me. As did the wall art in the sitting area of the coffee shop (pictured above; with views of the open facility where they'll start making tile later this year): eight giant panels painted on the wall forming a timeline of Heath's ceramic products from its inception in 1948 to today. Simple, functional, and thoughtfully designed - just like the ceramics.

Are there any interesting places or disciplines from which you draw data viz inspiration? Leave a comment to inspire the rest of us!

nice visuals in Bill Gates annual letter

I just finished perusing the 2012 Annual Letter From Bill Gates (Bill & Melinda Gates Foundation), after receiving a tip on the good visuals it contains from a friend.

This is a great example of data viz done well. The visuals are straightforward, clean, and easy to interpret. They reinforce the words around them and vice versa. Each strives to tell a story. Here's one example:

Here's what I like about the above visual:

• It has an action title, so you know before you even get to the data what to be looking for.
• The key takeaway is stated in words, and the red font ties it visually to the relevant points - today, developing countries have a much higher proportion of workforce in agriculture than the US.
• Font size and colors help to create a visual hierarchy of information, so it's clear what is most important and what is less important.
• Everything is labeled (overall title, axis titles, sources).

There are a couple things that bother me a little, but they are relatively minor: I find the diagonal lines in the US area unnecessary and a little distracting; I also prefer sans serif font (without the little squiggly lines) to serif, while here they've used a mix of the two.

There is one other visual in the report that I'll spend a moment on. I like it visually, but I have some problems with it as well. Here it is:

Can you identify what about the above visual comparison makes me uncomfortable? 

I also urge you to check out the other graphs in the report and leave a comment with your thoughts.

Thanks, Lauren, for the great tip!

learning from good visuals

I find I spend a lot of time here discussing less than stellar information visualizations and how they can be improved. But there is also much to be learned about data viz best practices by examining good visuals and understanding why they are effective. I paused on one such graphic last week (see original article):

Here's a quick rundown of what I like about the above visual:

• Everything is labeled (titles, axes, important values, data source), so there's no question about what you're looking at. The overall title explains what the visuals are intended to convince you of (so there's no guessing!). The points they want to highlight (2011) are shown clearly and draw attention through use of color-heavy call out boxes. 
• The use of color is intentional (not what a graphing application randomly chose); note how red means the same thing in both graphs: total. This consistency is important for easy comprehension.
• There isn't any extraneous stuff to dilute the audience's attention. Everything that's there is adding informative value.
• The visual hierarchy of information is clear. The data draws your attention through color; titles are bold so you can't help but know what you're looking at. Axis labels are less emphasized (but still clearly legible) with non-bold font. Sources and gridlines are there to help interpret the data, but are pushed to the background through size and weight so they don't compete visually with the data.
• Perhaps most importantly, the visual fits well with the article it accompanies. The graphs reinforce the main takeaways from the article and vice versa.

WSJ in general tends to have effective data representations, so if you're looking for more good examples, this is a great source. I still encourage you to maintain a critical eye when looking at any data visualization: observe what works well and what doesn't and try to emulate the effective parts in your own visuals.

Do you have any favorite sources for good data viz?

lessons in innovation

Earlier this week, Google published Think Quarterly, an online magazine of sorts that provides "a snapshot of what Google and other industry leaders are thinking about and inspired by today." The topic of the current issue is innovation.

While the focus isn't data visualization, many of the lessons shared can be applied in this space. For example:

• In The 8 Pillars of Innovation, SVP of advertising Susan Wojcicki discusses iteration as the way to strive for consistent innovation, not instant perfection, and looking for ideas everywhere. I appreciate the concept she introduces of "sparking with imagination, fueling with data."
• Head of Americas Sales, Dennis Woodside, talks about how audiences today want and expect "something more sophisticated, more considerate" than they have in the past. In Route to 2015, he's talking about marketing and advertising, but I would argue the same trend is happening when it comes to information visualization. His 4 B's are also applicable: be found, be engaging, be relevant, be accountable.
• "The most original innovations come from mucking about, not from thinking hard" (Russell Davies, Practical Magic). It's often that sort of mucking about with a dataset that leads to new insights you wouldn't have found with a hypothesis-driven approach.
• In Next Gen Innovators, Sarah Ohrvall calls out data aggregation as the trend driving the most exciting innovations in digital media in her opinion. She says: "information can be used to improve your daily life and improve the world around you" and calls out that the more people know about the impact their behavior has, the more they will change their behavior based on this knowledge. 

These are just some highlights intended to pique your interest; I highly recommend checking out the full publication. See where you can apply the innovation lessons presented.

I'll wrap this up with some words from Susan Wojcicki: never fail to fail. In data visualization (as in life), learn from the things that don't work, adjust accordingly, and try again.