Four wheels? Check.

Power locks? Ch… Hey, wait a minute! Where are the power locks?!?

Recently, I bought a new car and one thing took me by surprise.

Almost every brochure, salesperson, and YouTube video mentioned power locks and windows. Does anyone ever make a car purchase decision based on a single feature especially power locks or power windows or other table stakes?

And if not, why even mention it?   Instead, why not focus on the items that really make the car stand out and the things that differentiate it from all the other vehicles on the market.

In the automobile industry Subaru’s current TV ad campaign would be an example.  Instead of focusing on features, Subaru focuses on outcomes, like happy, safe families, and not features.

So, assume the target buyer is truly the business person (you know, someone who has a need to drive the bottom line, but probably doesn’t care about the fiddly technical details.) What can the business intelligence industry learn from the Subaru ads?  Perhaps instead of merely listing debatable features in marketing materials, banner ads and presentations, such as “easy dashboards” and “more widgets” the focus was on outcomes and how they help you achieve them. What if the focus was on the following outcomes:

• Present your data in a way that people understand

• Have collaborative meetings around your data

• Make your data presentations living documents

These outcomes could then be tied to specific features and how those features drive the desired outcomes.  Just a thought.  By the way, did you see Subaru’s sales results its most recent fiscal year?

I’m just sayin’.

TED founder Chris Anderson shared an informative guide to giving presentations in his recent HBR article (“How to Give a Killer Presentation“). His advice is hard-won and audience-tested. For those of us who create data-focused presentations his instructions tie to many of the same challenges we face when designing a dashboard or creating a report. There is one big qualification: TED Talk presenters regularly spend six to nine months preparing for their moment in the TED spotlight. You and I don’t have that luxury — or perhaps we should feel lucky?

Anderson starts by highlighting the power of stories:

“We all know that humans are wired to listen to stories, and metaphors abound for the narrative structures that work best to engage people. When I think about compelling presentations, I think about taking an audience on a journey.”

I love the notion of a presentation as a journey. Unlike the presentations Anderson is referring to where presenters memorize their script word for word, data-rich presentations often need to be flexible to fit needs and questions of the audience. I equate it to playing the role of a safari guide.

“If you frame the talk as a journey, the biggest decisions are figuring out where to start and where to end. To find the right place to start, consider what people in the audience already know about your subject—and how much they care about it.”

This reminds me of Nancy Duarte, in her book Resonate, who talks about helping the audience bridge the gap between what is and what could be.

“The most engaging speakers do a superb job of very quickly introducing the topic, explaining why they care so deeply about it, and convincing the audience members that they should, too.”

With data, we often feel a need to be objective and unemotional in sharing it — but it is at a high cost in effectiveness. If you watch any TED Talk, it is clear how much point of view and passion is added to the basic content, and how effectively this stirs the audience.

“The biggest problem I see in first drafts of presentations is that they try to cover too much ground.

Much of the early feedback we give aims to correct the impulse to sweep too broadly. Instead, go deeper. Give more detail.”

Too many data reports attempt to cover every conceivable question. It is better to try to be a data gourmet than a data gourmand.

“Of course, it can be just as damaging to overexplain or painstakingly draw out the implications of a talk. And there the remedy is different: Remember that the people in the audience are intelligent. Let them figure some things out for themselves. Let them draw their own conclusions.”

It is a powerful trick to be able to lead your audience to water, but not push their noses into it. Ultimately you want to inspire action. Most people are more inspired to take action on something they ‘discovered’ than something they were told. It is just basic parenting.

“Many of the best talks have a narrative structure that loosely follows a detective story. The speaker starts out by presenting a problem and then describes the search for a solution. There’s an “aha” moment, and the audience’s perspective shifts in a meaningful way.”

And so too it is in data visualization. Better to deliver the “Aha” than the “Wow.”

For more on data storytelling check out our giant list of resources for data storytelling and our 30 days to data storytelling learning guide.

There’s been this ongoing discussion in the Juice office for a while about how good data visualizations are like Tootsie Rolls: they’re sweet, sticky, and wrapped in wax paper… um, not really (unless you’re Timo Elliot - thanks for the laugh, Timo; well played.) But we do believe that there are great data visualization lessons to be learned from what works in everyday life. We call these not-so-Fewian examples “Everyday Visualizations”. Everyday Visualizations are physical items that are arranged in such a way as to communicate some state or measurement of our surroundings. For instance, you might glance at a budding tree and “measure” that spring is just starting.

Well, the other day Frederica, one of our followers in Italy, sent us a few pics epitomizing some particularly thought provoking examples she noticed:

“There are 5 people in line in front of me.”

“It’s not going to rain today in Naples, Italy.”

These pics got us thinking again about all that can be learned from the everyday. As a result, we decided to post a few of the best examples we’ve seen. Here are a few we’ve collected over the months.

Everyday Visualizations in the Office

“More than half a charge.”

“Phweww… I won’t have to reload paper… this time.”

“Water-cooler analytics: we’ll be on a new bottle by this time tomorrow.”

Everyday Visualizations about food!

“Just enough water for a cup of tea.”

“M&M’s I’m going to eat today.”

“Viz-nerds like wine, too.” (or is it: Wine-nerd like vis?)

(yep, that’s right, we didn’t include a single “pie” chart. )

What other categories can you think of where you see everyday visualizations? Weather? Traffic? Electricity? Send us some of your pics to info at juiceanaltyics dot com and we’ll post the best ones. Enjoy!

READ, DO, WATCH, PLAY

We learn best from a diversity of inputs. That’s partly why our previous 30 days exercise sheet was such a huge hit.

It’s critical for analysts and presenters of data to share information in a way that people just get it. Enter data storytelling – a magical elixir to all your data communication woes! Well, maybe not quite. But you should be aware of recent efforts using this timeless approach to deliver information so naturally – through stories.

That’s why we’ve created 30 Days to Data Storytelling.

This exercise breaks down a structured (yet casual) introduction to data storytelling through a variety resources. We wanted to provide a diversity of depth and inspiration. Feel free to skip around or follow our 4 week sequence. Print it and post it near the water cooler or slap it to your virtual desktop.

Enjoy!

image by Tobyotter via Flickr

As a boy, it wasn’t the history, the architecture or an admiration of the faith involved to build these churches.  Those were concepts beyond my ability, knowledge or frankly interest at the time.  What I have come to realize only in the past couple of years is that the windows were meant for me. At the base level, I needed something that could grab my attention and hold it. What I have discovered is that from this standpoint, I am no different than the illiterate masses of the Middle Ages or Renaissance.

I discovered that hundreds of years ago, with a need to engage the European population and educate them on scripture, someone decided it wasn’t the job of masons, who built structures that would last for centuries, but storytellers and designers who could make kids, like me, stop and look.  This was the intent all along as stained glass windows were referred to as “biblia pauperum”, which meant “poor man’s bible”.

Now, with two years under my belt at Juice and hundreds of churches visited, it is interesting to apply the history and beauty of stained glass windows to the field of data visualization and presentation graphics. I now have a better handle on the true value of a designer.  For “design” to work for me, in any type of artistic endeavor, the designer should make me feel that it was designed specifically for me and make it beautiful at the same time to help lengthen my otherwise short span of attention.

As the noise about data visualization and data storytelling grows, it is nice to see that current leading experts in the field also value (and have not forgotten) these 2 design principles provided by our European ancestors.

Consider these two examples:

Design for your audience

• In a recent blog dated 5/10/13, Stephen Few highlights this important customized approach as the 2nd of 7 tenets for best practices of quantitative Data presentations.
  

Beauty’s role in dashboard design

• Back in November of 2009, even before I joined the team, Juice published a frequently downloaded “Guide to Creating Dashboards People Love to Use”. The guide noted that “modern web design has moved on to seek a union of utility, usability and beauty. We must find a similar union when displaying data in business.” (bold and italics added)

What will we learn from these impactful stories, built and told on stained glass sanctuary walls?  Will we preserve the most important principles found on those magnificent etchings? Today, our stories are accessed and downloaded from cloud-based applications and displayed in high resolution graphics on state-of-the-art devices. Yet our challenge is the same: capture the attention and imagination of our viewers – in a user-centric and aesthetically pleasing way.

Hello – I’m a professor at Georgia Tech and I’ve been working in the data visualization research area for over 20 years. My friends at Juice asked me to write a short guest blog entry providing perspectives from the academic data visualization community and exploring ways to foster more industry-academia collaboration. I’ve found that we don’t work together often enough, which is too bad because each side has a lot to offer to the other.

I personally have benefited from business collaborations in many ways. Since data visualization research is so problem-driven, industrial interaction provides an excellent way to learn about current problems and data challenges. In my graduate course on information visualization student teams design and implement semester-long data visualization projects. I encourage the teams to seek out real clients with data who want to understand it better. Some of the best projects over the years have resulted from topics suggested by colleagues working in industry. Additionally, I often employ guest lecturers such as the guys at Juice to come and speak with my students and provide their own insights about creating visualization solutions for clients.

I hope that in some ways my class is benefiting industry as well and helping to train the next generation of data visualization practitioners. Students learn about all the different visualization techniques and their particular strengths and limitations. They also get hands-on practice both designing visualizations for a variety of data sets and using current “best practice” tools and systems. The course has become a key piece of the Master’s degree in Human-Computer Interaction here at GT.

Another opportunity for interaction is academic research forums such as conferences and workshops. Coming up this October in Atlanta is IEEE VIS, the premier academic meeting for data visualization research. VIS consists of three conferences: Information Visualization (InfoVis), Visual Analytics Science & Technology (VAST), and Scientific Visualization (SciVis). Last fall, the meeting garnered over 1000 attendees for the first time.  VIS is an excellent forum to learn about the state of the art in data visualization research, see the latest systems from commercial vendors, and just rub elbows with like-minded friends and colleagues.  Recent papers at VIS presented tools such as Many Eyes and D3, introduced techniques such as Wordles and edge bundling, or just pondered topics such as storytelling and evaluation.  And the meeting has much more than just research papers – It also includes numerous workshops, tutorials, panels, and posters. This year for the first time we have added an “Industrial and Government Experiences Track”. This program is designed to highlight real world experiences designing, building, deploying and evaluating data visualizations. The presentation mode for this track will be posters on display throughout the meeting with multiple focused interaction sessions. Each submission should include a 2-page abstract about the project and a draft of the poster. They are due on June 27th.  More details about the track can be found on the meeting home page.

I hope to see many of you at VIS in October here in Atlanta!

The colored map and detailed drill-down show 37 measures, yet they forgot to include one important freedom: your freedom to communicate data with ease and create interactive infographics in minutes. Don’t worry, we’ve got a heaping-helping of info-liberation. So before you send an angry e-mail to your congressman, take a look at what we put together with Slice in under an hour:

Like the Freedom site, we want users to be able to choose a metric and be able to see which states are freedom-loving and which are freedom-hating (I’m look at you, South Dakota, with your anti-bachelor party policies). That’s our new “map slice” in action, which can color states based on our data or overlay colored bubbles to visualize locations.

To add even more data exploring fun, we created a visualization to let you compare two states side by side.  Check out how North Dakota totally dominates California on freedoms.

Having flexed my information visualization freedom muscles, I’m off to ride my bike without a helmet while drinking a 32-ounce soda.

Choose a time below and sign up to watch our live webinar.

For the East-coast lunch-eating friends: May 1, 2013 12:00 PM EDT

For our West-coast lunch-eating friends: May 2, 2013 3:00 PM EDT

From Edward Tufte’s Visual Explanations, a diagram based on Salman Rushdie‘s description of the Indian epid Kathasaritsagara or Ocean of the Streams of Story.

The hot new concept in data visualization is “data storytelling”; some are calling it the next evolution of visualization (I’m one of them). However, we’re early in the discussion and there are more questions than answers:

• Is data storytelling more than a catchy phrase?
• Where does data storytelling fit into the broader landscape of data exploration, visualization, and presentation?
• How can the traditional tools of storytelling improve how we communicate with data?
• Is it more about story-telling or story-finding?

Many of the bright minds in the data visualization field have started to tackle these questions — and it is something that we’ve been exploring at Juice in our work. Below you’ll find a collection of some of the best blog posts, presentations, research papers, and other resources that take on this topic.

Note: I’ve excluded a lot of excellent sites and articles that use the phase data storytelling, but treat it as fresh way to talk about data visualization.

1. Blog Posts

Storytelling with Data: What Are the Impacts on the Audience? by Nick Diakopoulos
“I realize there’s a whole lot of inspiration out there, and some damn fine examples of great work, but I still find it hard to get a sense of direction…We need to know what makes a data story “work”. And what does a data story that “works” even mean?”

A Data Scientist’s Real Job: Storytelling by Jeff Bladt and Bob Filbin
“In short, we’re tasked with transforming data into directives. Good analysis parses numerical outputs into an understanding of the organization. We “humanize” the data by turning raw numbers into a story about our performance.”

Coffee & Empathy: Why data without a soul is meaningless by Om Malik
“The idea of combining data, emotion and empathy as part of a narrative is something every company — old, new, young and mature — has to internalize. If they don’t, they will find themselves on the wrong side of history.”

Look ma, no story! by Moritz Stefaner
“Tools have no stories to them. Tools can reveal stories, help us tell stories, but they are neither the story itself nor the storyteller. Portraits have no story to them either. Like a photo portrait of a person, a visualization portrait of a data set can allow you to capture many facets of a bigger whole, but there is not a single story there, either.”

Discussion: Storytelling and success stories by Andy Kirk
“I just wanted to share my view on the distinction I personally make between the two main types of visualisation function: exploratory and explanatory”

The secret to storytelling is in the editing by Garr Reynolds
“Although it is a film about the role of editing in filmmaking, the lessons and principles are applicable to other creative work such as writing, and storytelling of all kinds, including presentations.”

Visualising data: can you see stories? by Chris Twigg
“Narrative can on the one hand be broken down into a set of universal laws and principles that may transcend mediums. Stories have temporality in common (they deal with time) as well as causation (they deal with cause and effect of something). On the other hand there are the more media specific narrative affordances as for example in the way that film, opera, novel and data visualisation – because of their physicality and the dimensions open to them – would be able to give a different ‘staging’ of a story.”

Data Visualization as Storytelling: A Stretched Analogy by Zach Gemignani
“For practitioners of the craft, connecting our work to stories feels satisfying — it is a call to raise our standards and an opportunity to enhance the influence of our field. Stories evoke images of rapt audiences, dramatic arcs, and unexpected plot twists. Unfortunately this analogy is a stretch.”

Why good storytelling helps you design great products by Braden Kowitz
“It’s not uncommon for designers to confuse a beautiful looking product with one that works beautifully. A great technique for creating smarter, better products is to approach them using story-centered design.”

How might rhetoric inform information design? (Quora) and related blog post by Stewart McCoy

2. Presentations

How to Tell Stories with Data (Really) by Edward Segal
PDF version

Visualising Workflow: Findings Stories and Telling Stories by Andy Kirk

Storytelling with data visualization: Questions and challenges by Albert Cairo

Storytelling with Data by Jonathan Corum

3. Research Papers

Visualization Rhetoric: Framing Effects in Narrative Visualization by Nick Diakopoulos (Summary, Research Paper)
“We carefully analyzed 51 narrative visualizations and constructed a taxonomy of rhetorical techniques we found being used. We observed rhetorical techniques being employed at four different editorial layers of a visualization: data, visual representation, annotations, and interactivity. The five main classes of rhetoric we found being used include: information access (e.g. how data is omitted or aggregated), provenance (e.g. how data sources are explained and how uncertainty is shown), mapping (e.g. the use of visual metaphor), linguistic techniques (e.g. irony or apostrophe), and procedural rhetoric (e.g. how default views anchor interpretation).”

Narrative Visualization: Telling Stories with Data by E. Segel and J. Heer
(Abstract, Research Paper)
“We systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration).”

Storytelling: The Next Step for Visualization by Robert Kosara and Jack Mackinlay
“Presentation and communication of data have so far played a minor role in visualization research, with most work focused on exploration and analysis. We propose that presentation, in particular using elements from storytelling, is the next logical step and should be a research focus of at least equal importance as each of the other two.”

What Storytelling Can Do for Information Visualization (PDF) by Nahum Gershon and Ward Page
“Effective presentations using the storytelling approach require skills like those familiar to movie directors, beyond a technical expert’s knowledge of computer engineering and science. Creating a presentation is not just a matter of being literate in visual media and storytelling but depends on a frame of mind that caters to other modes of human information processing and thinking.”

The Enchanted Imagination: Storytelling’s Power to Entrance Listeners
“While storytelling has flourished, there has not been a concomitant surge in research of the art form. One element of storytelling has remained nearly unconsidered, and it is, perhaps, the most profound and influential characteristic of storytelling: its power to entrance those who listen.”

4. Tools, Examples, and Other Resources

Hans Rosling’s TED Talks
“What sets Rosling apart isn’t just his apt observations of broad social and economic trends, but the stunning way he presents them. Guaranteed: You’ve never seen data presented like this. By any logic, a presentation that tracks global health and poverty trends should be, in a word: boring. But in Rosling’s hands, data sings. Trends come to life. And the big picture — usually hazy at best — snaps into sharp focus.”

How to Give a Killer Presentation
“We all know that humans are wired to listen to stories, and metaphors abound for the narrative structures that work best to engage people. When I think about compelling presentations, I think about taking an audience on a journey.”

Robert McKee, Godfather of Storytelling (Wikipedia)
Rather than simply handling “mechanical” aspects of fiction technique such as plot or dialogue taken individually, McKee examines the narrative structure of a work and what makes the story compelling or not. This could work equally as well as an analysis of any other genre or form of narrative, whether in screenplay or any other form, and could also encompass nonfiction works as long as they attempt to “tell a story”.

Stories Through Data
Exploring storytelling in data visualization. A collection of visualizations sorted by Chris Twigg’s narrative analysis framework.

13pt Information Graphics
Gallery of examples from the studio of Jonathan Corum, an information designer and science graphics editor at The New York Times.

A free and collaborative taxonomy of Data Storytelling tools by Philippe Nieuwbourg
“To summarize my investigations around data storytelling tools I created a mind map. This map will be an up-to-date taxonomy / ontology / typology, of software available on the market, to create stories around data.”

DataStorytelling.tv
“An independent website, dedicated to storytelling around data.”

Pixar’s 22 Rules of Storytelling
“Give your characters opinions. Passive/malleable might seem likable to you as you write, but it’s poison to the audience.”

Bob Beamon’s Long Olympic Shadow by Kevin Quealy and Graham Roberts (NYT)

I don’t like to knock our own stuff, but a little healthy introspection is always a good thing. Consider our popular ChartChooser tool. In spite of it’s carefully crafted name (it was core of an ad campaign akin to peanut butter: Choosy chart choosers choose ChartChooser — no, not really), we’ve come to believe that ChartChooser isn’t so useful for the “Chooser” part; it is useful because the “Chart” part is nicely formatted, downloadable PowerPoint and Excel charts.

Here are the filtering choices for ChartChooser:

I’ve been at this a while and I still don’t always know how to connect what I’m trying to express with words as vague and broad as ‘Composition’ or ‘Relationship’.

It isn’t entirely ChartChooser’s fault. Basic chart types are by nature broad and flexible in their usage. How can we make it easier for someone to make that leap from their question to a visualization that best answers it?

We believe one part of the solution is to make visualizations more purposeful. That is, create re-usable ways of expressing data that are carefully designed to answer common questions that people pose about their data.

While it’s true that everyone’s data is unique, what we’ve learned is that in most cases, the things they want to know about their data aren’t so unique. The same sets of question patterns show up time after time. It’s almost like a game of Mad Libs:

• Which are my top performing _plural noun_?
• Which _plural noun_ are the most significant outliers when measured by _ measure_ and _ measure_?
• Which _plural noun_ have improved or declined the most over the last _time period_?
• How does _singular noun_ compare to _singular noun_ across my important performance measures?

Our goal is to draw straight, obvious lines between questions like these and a visualization that directly and simply expresses an answer.

If you consider the last data Mad Lib question above, our match-up visualization is a good example: compare two things side by side to see relative performance. The Match-up was inspired by the traditional tale-of-the-tape graphics that you used to see in boxing matches.

Like a lot of our visualizations in Slice, we’ve added a number of key features that really help the user quickly understand and explore the data. Here are a couple examples:

We’ve put together a whole collection of these purposeful visualizations, such as a funnel visualization for sales conversions and other processes; a leaderboard for ranking top items across a bunch of measures (try it free here), and a comments visualization for reviewing and exploring survey verbatims, tweets, and other descriptions. And we’ll be making more. What questions do you ask of your data?