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Good data communications drives good business decision-making. At least it has been the case for Bill Shander, whose work has impacted many business organizations and individuals. Bill specializes in creating interactive data stories to address business communication challenges. Though his work in “digital design for knowledge”, Bill has delivered rich, engaging and powerful content for prominent clients like World Bank, United Nations, PwC, Harvard University etc.
Bill runs Beehive Media, an information design and data visualization agency, and travels around the globe to deliver workshops and talks on how to think visually and interactively. He also teaches data visualization and storytelling course. We recently had the pleasure to pick his brain on how to create better data communications for business.
1. Given your solid background in business data visualization, what is your approach to choosing the best format to visualize a dataset that makes financial report simple and interesting at the same time?
Piles of numbers are boring, let’s face it. It’s what the numbers stand for that is exciting. But sometimes, even that can be hard to get excited about. Financial data, business analytics, demographics statistics….these are just collections of facts that don’t often have a lot of inherent interest.
Sometimes the easiest way to make them interesting to your audience is simply to use a unique visualization and good design to make your story visually compelling. Research has shown that if you make your presentation of data more engaging, your audience will be more likely to accurately remember the data.
So make your visualization beautiful: add a little eye candy, some animation, some flair. It’s neither a “nice-to-have” nor superfluous, but rather essential to have for maximum impact, especially when the underlying data and subject matter are less compelling.
One example when I’ve done this is for a just-launched project for the Boston Public Schools. I created a massive data dashboard for their 10-year master plan. The data is looking at the condition of their facilities – things like ventilation systems and roof repair requirements – not sexy stuff! But the dashboard incorporates many different interfaces and visualizations to try to bring the data to life to help administrators make $1 billion worth of design and construction decisions over the coming ten years.
2. Visualizing data is not the end of the process, we still need to present the story. How do you craft a story based on these data visuals in a way that truly engage our audience?
Story is critical to good data communications. This is, by the way, the second answer to the first question – story makes even the most boring data more interesting. This is because for tens of thousands of years, humans have communicated to each other using story. We need to have characters whose actions and motivations allow us to see ourselves in the information being presented.
A spreadsheet is far less interesting and informative than a story about a young girl who lives in Boston and goes every day to her elementary school. But even when it is just lightly drizzling outside, her mother has to always remember to pack her rain boots, because if she gets to school late, she may have to sit in the back corner of her class, where the puddles collect because the roof is in desperate need of repair (which is true in XX% of all Boston Public Schools.) This story is memorable, and you’re also more likely to remember the XX% statistic because I gave it to you in a story.
Data storytelling may not always be about the narrative as described above. Sometimes it’s just about creating a linear story-like presentation of the thesis and arguments the data is presenting. This means creating a linear experience to walk your audience through your data in the order you want them to experience it.
One example of this is the Smallholder Diaries project I created for the World Bank. It’s a linear presentation of a series of screens with text that could stand alone and explain the concepts, but also with data visualizations along the way to help bring the information home in a more compelling way.
3. What is your suggestion on visualizing massive amount of qualitative data?
Qualitative data is interesting because it’s doesn’t start as numbers – it’s things like eye color, emotions or the shapes of different species of flowers’ petals. But the first thing to realize is that all of those things can be turned into data.
For instance, color values are numeric. You have hex values, RGB values, or you can think of color as being on a spectrum from red (on the left, let’s say) to violet (on the right). So you could indicate where a color falls on that spectrum. Emotions can be measured – how angry are you on a scale from 1-10? And leaf shape can easily be measured. In fact, one of the most famous example data sets for data visualization looks at the lengths and widths of different petals of irises.
Working with qualitative data, then, often means thinking about how to turn that qualitative non-numeric information into numbers. The easiest example is definitely survey data, where you often have the data on a scale. I recently released a visualization called “Love and Happiness in Anna Karenina”, which at its core looks at the relative happiness of the key characters in that great Russian novel chapter by chapter on a scale from one to five.
I took that data and mapped out a large visualization simply showing that relative ranking of happiness. The challenge was to interweave it with some other data, such as when the key characters (lovers) are thinking and speaking about each other, when they’re together in the scene, and looking at two different couples simultaneously across the same criteria.
Similar thinking can be applied when you have non-numeric data such as text. The Anna Karenina project is a good example of this as well. I had about 900 pages of text, which I turned into data by analyzing that text for when the key characters enter and exit the scene, as well as their happiness and other attributes I already mentioned.
You usually have to quantify qualitative data to tell your story. This is what data storytelling and visualization is always about. It’s just about thinking of the end result you’re looking for (in my case, trying to figure out what drives love and happiness in the book), which will help you figure out what to quantify and how.
4. Regarding the truthfulness of data: how do we avoid fake data for false expectations?
The “truth” is hard to come by these days. It seems people’s politics are driving their beliefs in just about everything, including empirical data. People and organizations are taking advantage of this by publishing data that would not pass a real “sniff” test by an educated consumer.
As a content creator, the only thing you can do is try to source data from the very best resources (credible government, academic and nonprofit agencies) and when you get data from potentially biased sources (corporations, political parties, etc.), be careful. Be sure to annotate everything you do and cite your sources.
And if you’re uncertain of the source, make sure that annotation is prominent with plenty of caveats. Of course, if you know the data to be false or aren’t very certain of its credibility, be prepared to walk away from it!
5. There is so much data to available to us for every single subject matter. It’s simply overwhelming. How do you select data sufficient to tell a story but not too much to overwhelm the audience?
Using just the right amount of data is one of the most important choices you’ll make when telling stories with data. Too little and your story might lack proper context for your reader to understand, or may not tell a complete enough story to be compelling. Too much and it’s overwhelming and no better than a data dump. This is the communicator’s and designer’s primary consideration – editing out the noise without carving away meaning.
My first tip to explain how to do this is to use your beginner’s mind. You have to separate yourself from your knowledge of the data and try to channel your audience. What will they need to know to understand your story? What will overwhelm them? To the best of your ability, try to pare it down thinking very empathetically about them. And, of course, you can actually pick up the phone or walk down the hall and talk to real humans! Ask someone what they think and you may get some great insight into the quality and quantity of your data story content.
Once again, the Anna Karenina project is informative. I captured a decent amount of data for this project, but in the end narrowed it down to focus just on the key character attributes and actions chapter by chapter. I ignored a lot of data, such as exactly when key characters entered and exited the chapters (multiple times per chapter), and exactly when they thought or spoke about their counterparts. This might have been interesting, but it was more information than needed. If it were a shorter book with just 40 chapters, instead of 240, that level of detail might have been nice – even necessary – but I just felt it was too much for this particular project.
In addition to channeling your audience and even speaking to your audience, all I can say is that editing and focusing your story is something that improves over time and with practice. Work at it continually and you will improve!
Turning boring information into beautiful visuals doesn’t have to be a daunting task. At Visme, we help you use the drag-and-drop visual content tool with the best help content.
We will bring more expert content like this Q&A with Cole above. Stay tuned!