There is a word that stands among the worst of all words. Hearing it causes feelings of confusion, anger, and sadness. That word is report.
I take people’s data and organize it in a visual format that helps them understand and react to their world a little better, and there are perfectly acceptable terms to describe this, such as dashboard, visualization, or visualisation if you prefer the Queen’s English. Just don’t call it a report.
A giant table jammed packed with every tidbit of info that could possibly be relevant is what I’d call a report. It makes no effort to clarify or convey a point of view. It’s more like a Rorschach test that allows each user to see only what they want to see.
Visualization, on the other hand, is an active participant in wringing the truth out of data. No analytical work is complete without it. But it’s not just an add-on at the end or a pleasant veneer to a perfectly fine set of analytics. It’s vital to how analytics are understood and perceived. Unlike reporting, visualization has a unique ability to clarify murky ideas and make connections that otherwise would have been missed.
I believe in the indispensable role of visualization so strongly that I shaped my entire career around it. However, not everyone in the world of analytics is convinced, and at times that doubt makes me question if I’m on the right track.
I recently met with a guy who manages the data transformation and reporting functions for his organization, and both were a mess. The data environment was complicated and in need of consolidation, and while his team’s ability to produce vast quantities of reports was impressive, user engagement was not. As a result, his team was overwhelmed with building and maintaining a bunch of stuff no one used.
Naturally, I saw an opportunity and suggested improvements to his approach to visualization, but his response surprised me. He said, “Data’s the hard part. As long as I get that right I can slap some reports on top of it and everyone’s happy.” While it’s true that better data would have relieved some of his pain, his team’s inability to create tools that the organization found useful sure seemed connected to this blasé attitude towards visualization.
Later, after recovering from my disappointment, I had an epiphany about visualization and its unique role within data and analytics: Visualization is important but hard to execute well, and until people see it consistently produce benefits, they won’t buy in.
The other disciplines of analytics have a hardness to them that is easier to grasp, execute, and develop talent around. Data transformation calls on a well-defined set of skills and tools to accomplish a well-defined goal of getting data from there, cleaning it up, and putting it here. Data science, similarly, though somewhat more loosely, uses an array of well-defined and mathematically sound methods for solving particular problems.
Visualization, however, requires expertise in fields that are notoriously hard to define and master such as psychology, visual perception, and communication. A lot of great work has been done in recent years to harden some of the science around it. But by its nature, visualization is relational and contextual and cannot be hardened into a rigid set of rules. There are too many variables at play when it comes to communicating with and influencing human beings. What works for one set of data doesn’t work for another. What works for one group of people doesn’t work for another.
Visualization is a highly customized solution for highly complex and unique problems. Frameworks can be borrowed from similar solutions, but they are only starting points as the exceptions for each problem demand a more tailored approach. As a result, it’s hard to pull off a successful visualization project and even harder to develop talent that produces success on a repeatable basis.
If you care about being great at visualization and have had the opportunity to create projects for others, then, like me, you’ve probably experienced indifferent or resistant attitudes that make you wonder if it really matters to anyone but you. Every book you read convincingly explains that pie charts are a bad idea, but the client insists on using them. Principles of good design tell you that using green for all the positives and red for all the negatives is hard to read, but your alternative color suggestions are repeatedly rebuffed. Or no matter how well you select chart types and employ progressive reveal to tell a coherent story, all the users want at the end of the day is a table they can download into Excel.
There are endless examples you could cite that make visualization seem like a never-ending, uphill battle. But if you’ve been practicing for any length of time then you know something else to be true: When it all comes together, there is nothing more glorious than a successful visualization project. The positive impact it makes on the end users is undeniable, and that satisfying feeling of making a difference keeps you coming back for more.
The reality is that visualization matters a lot, but many are unconvinced because they have yet to see it done well. Our job is to prove that it matters by creating great projects. While the point is obvious, how to pull this off on a repeatable basis is not.
There are helpful best practices and methods, but there is no formula. What’s needed is a set of guiding principles that can adapt to the infinite variety of flavors a visualization project can take on. In other words, not just the what is needed, but the why and the how. If I care about why I’m visualizing something and I have a proper attitude that guides how I’m doing it, then my chances of creating a great what go up significantly.
In that spirit, I came up with a short statement that helps me keep the why and the how top of mind.
Help real people find meaning in their data and use it well.
When I get fuzzy about what I’m creating on any given project, I use this brief reminder to pull me back to what really matters. The beauty of it is that it succinctly captures several ideas, or imperatives if you will, that are critical to getting visualization right…
Help: Don’t just show data. Solve problems.
Real: Don’t be abstract. Connect to things that are real.
People: Don’t design for anybody. Design for somebody.
Find: Don’t tell a linear story. Guide towards discovery.
Meaning: Don’t be an agnostic. Have a perspective.
Data: Don’t force a narrative. Allow data to test a narrative.
Use: Don’t produce insight. Produce useful insight.
Well: Don’t settle. Make it matter.
Tactically speaking, there are myriad ways a project can suffer such as slow performance, confusing copy, or odd color choices. A project can recover from these as long as it hits the mark otherwise. However, no project can recover from a miss on any of the eight imperatives above. Do each of these well and more times than not you’ll create something other people want to use.
The point isn’t that these are the most important or only core ideas of visualization. Your understanding and experience may lead to a different list. Rather, when you are clear on why visualization matters and how to incorporate its essential ideas, what you create will matter to others.
Dan is the practice leader for Visual Analytics at Aspirent Consulting. He has over 15 years of experience in finance, business analytics, and visualization working with Fortune 500 companies such as The Home Depot, Coca-Cola, and Mattel.