Companies seeking to extract maximum value from data and statistics can no longer be content to leave visualization in the hands of left-brain data analysts who are not naturally inclined to think of charts and graphs in an aesthetic manner. They need a specialized data visualization expert.
Bringing data to the masses requires no less. It mandates that companies bring on board an expert who can effectively translate left-brain data into right-brain art in a way that both sides fully understand.
To understand why, think about how statistics and visual art are fundamentally different. Statistical analysis and visual art have histories that go back for centuries, if not millennia. Each practice has had its pioneers: Bayes, Laplace, and Gauss on the statistical side, and Michelangelo, Rembrandt, and Picasso on the artistic side. But where do the two meet?
When it comes to finding geniuses who are truly good at understanding the beauty of mathematics and art in combination, only a few stand out. These include the Renaissance artists Dürer and Da Vinci, as well as the more modern M.C. Escher.
But not every mathematician has an eye for visual beauty. And not every artist is fluent in the language of statistics. To truly present numbers in a visualization that is effective and valuable, people must be able to translate sets and functions into figures that immediately present guidance and information to an appropriate audience.
Traditionally, this has meant creating a bar graph that simply shows a blocky and proportionate comparison of basic metrics. As these visualizations have evolved over time, they have typically become larger and more complex, as we have seen with social network analysis and word clouds. And when the audience for data only consisted of similar left-brained quantitative experts, this was sufficient.
But an additional level of nuance to data visualization has barely been tapped. We have thousands of years of experience in equating various colors with various levels of urgency or context, yet rarely take advantage of those cultural cues. Color theory shows how context, weather, and other mitigating circumstances should change color and shades. Architectural perspective can provide guidance on the shapes and structures that the eye finds most appealing.
But these concepts are rarely taken into account for data visualization. In short, the visual nuances of design, perspective, color, and other artistic components are still in their infancy when data visualization is concerned.
Thought leader Stephen McDaniel is no stranger to traditional analytics, having written SAS for Dummies and having served as an analytic consultant and instructor to many organizations. When I asked him about visualization, he replied, "Visual analytics is a legitimate field."
This view has been reflected in the business intelligence and analytics world, not just by visualization and data discovery specialists like QlikView and Tableau, but by pure-play platforms such as SAS and MicroStrategy, and even megavendors like IBM and SAP, which have poured their development dollars into better applications and visualizations.
Why are they all going in this direction? End users are demanding better and more creative visualizations than the standard charts and graphs that were used during the first 30+ years of relational databases and standard business intelligence. It only makes sense that as the artistic palette changes, the skills to wield that palette will change as well.
This is not to say that the traditional data scientist or data analyst doesn't have a place in the new world of visualization. On the contrary, companies need data-savvy individuals more than ever. But because of this transformation of data visualization capabilities, and the increased demand for data-driven decisions, companies need to consider a new specialist on their team: The data visualization expert, fluent in both quantifying and qualifying data to support business goals.
Do you agree or disagree? Read Jonathan Schwabish's Counterpoint and share your thoughts below.
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Yes, Beth, exactly. Thank you. In the vast majority of cases, there's a tool somewhere that will satisfy. Ten to twenty years ago, it might have made sense to assign a company resource to data visualization. Today, enough very smart people have made data visualization their full time job, and produced commercial quality products for visualization, that experts and specialists are no longer necessary. As for artful presentation, big companies have communications or art departments when that sort of thing is truly necessary.
I like the idea of an example, Noreen, but "good" depends on the need. Some that I find visually appealing, just because they look cool and are fun to click through, are treemap, tag cloud, and fish-eye. My question -- consider it rhetorical -- is just what sort of data can't be explored using the wealth of visualization methods we have on hand today?
@jmarkdavis, so, in a nutshell, it's all about finding the right tool?
jmarkdavis, Can you share an example of what you consider a good visualization?
I understand what you're suggesting, but think about the practicality of it. Simplification is precisely the point. If the analytics and big data tools available in a given enterprise aren't getting the job done, what is the most efficient and effective remedy? I can tell you from years of first hand experience that seeking or creating an expert capable of inventing and then building something entirely new is a losing proposition, unless of course that enterprise happens to be in the business of building and selling such tools. The smart money will conduct a product search, find a product with suitable exploration features, acquire that product, train appropriate roles, and quickly reap the benefits.
@jmarkdavis, thanks for jumping into this conversation. I don't disagree with your points but I do wonder if they're oversimplified when put into a big-data context -- especially if you're talking about using visual analytics as a way to better explore that data (so less of a means of presenting results).
I appreciate the passionate argument, but it only holds in a small corner of the problem space. Data visualization is a surprisingly old subject, and it's been given a thorough treatment by a long list of distinguished researchers. The good news is that, although advanced research continues, the day to day needs of the average jane/joe are covered. When it comes time to present data, we have ridiculously powerful tools now that are literally under our noses, and they will handle the majority of our needs right out of the box. For the record, I count 74 different chart types in Excel. Seriously, Excel!
Occasionally -- I would even say rarely -- we may find it necessary to create more complex or specialized representations; the good news here is that self-education in this Internet age is all too simple. Go read a book by an expert like Tufte. If that's not enough, you can take courses from some of the masters without even leaving your office, for pete's sake.
Let's skip over the bit about artists and left brain vs right brain (complete myth, by the way: http://www.psychologytoday.com/blog/brain-myths/201206/why-the-left-brain-right-brain-myth-will-probably-never-die). At bottom, it is obviously true that people have different strengths and interests. However, most of our daily work presenting facts and figures will not require exceptional ability. Sometimes, again rarely, a breakthrough visualization method means the difference between communication and confusion, c.f. Feynman Diagrams. Most of the time, almost all of the time, that's just not the case.
Here's the secret to effective data presentation, and it's been rediscovered so often it's not even funny. Keep it as simple as possible. And to be honest, it doesn't take an expert to do that.
"Visualization is another area where bias, judgement, and skill come into play."
Visualization does deliver some value to the data and the business, even though it might be deceptive sometimes. But as goes the saying, "a picture is worth a thousand words".
@Hyoun Well said and you poise questions that I had not considered for certain. This bias does happen with all analysis so if data visualization can migitigate this to a certain degree, then I can see the value in it's use. And I like your final point best, most companies do not consider this, so if you can tailor data effectively in this case in terms of visualization, I agree it does seperate you from the pack.
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More Blogs from Point / Counterpoint
Chances are you've already got good data visualization experts on staff, even if you don't know it yet.
Banks have tons of customer data at their disposal; unfortunately not all will use it scrupulously.
If banks want to know more about consumers, and act on it with customized offers, then let them have at it.
Using big-data to drive retail prices up and down several times a day just seems likely to make already skittish consumers more anxious.
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