Fixing Data Visualization for Today’s World

We are making data visualization harder than it need be. The root of the problem is that the thinking behind data visuals grew up in an old world of paper. If we re-focus on the purposes of data visualization in business today, then we will get better results faster.

The Old World

The classic thinking about data visualization is found in a brilliant book by Edward Tufte called “The Visual Display of Quantitative Information.” Here’s what Wikipedia has to say:

“Tufte encourages the use of data-rich illustrations that present all available data. When such illustrations are examined closely, every data point has a value, but when they are looked at more generally, only trends and patterns can be observed. Tufte suggests these macro/micro readings be presented in the space of an eye-span, in the high resolution format of the printed page, and at the unhurried pace of the viewer’s leisure.”

Ah, does the old world nature of this thinking jump out at you? Phrases like “the printed page” and “unhurried pace” show this book was not written for the modern corporation. There is much of value in Tufte, however, we must free ourselves from the past if we are to do visualization well today.  

The starting point is to understand the three purposes of visualization:

  • Seeing
  • Telling
  • Wowing

Seeing: The First Purpose of Data Visualization

The first purpose of visualization is to help an analytics professional make sense of the data. It’s about “seeing” what is there. The mode of operation is interactive, iterative, and informal. The analyst will pose a question in their mind, pull up the appropriate visual, draw some conclusion which almost inevitably raises another question which calls for another visual.

It will look something like the following: An analyst is asked to look at employee turnover so they start by pulling up a chart on employee movement.

Then they wonder why that turnover is occurring, so they pull up data on what’s driving it.

Data visualization of employee resignation drivers

Based on that, they wonder if they are likely to have a turnover problem next year so they pull up some more data on predicted turnover.

Data visualization of predicted employee resignation

The analyst pulls up visualizations, one after another, as they work to understand what is going on with turnover.

I’ve only shown three graphs here for this example, but an analyst might look at dozens as they try to get some insight on employee turnover issues. It’s hard to tell how much data you’ll need when you begin your analytics projects, but speed and medium are key. You want 30 visualizations done in one hour on the screen, not one visualization is done in 30 hours for a printed textbook. It doesn’t have to be perfect at this stage–it just needs to be informative.

Further, the data visualizations above are rich with information and ideal for an analyst as they dig for insights, but in the next section, I show why, when it comes time to speak with the C-suite, less is always more.

Telling: The Second Purpose of Data Visualization

The second purpose of visualization is to explain something to executives. To achieve this purpose, we have to realize that executives are rushed, distracted, and not nearly as interested in the details as you are. They generally don’t want data-rich illustrations. They want a simple image they can understand at a glance that makes your one key point.

In the example below, we can see the movement of diverse and non-diverse candidates through the recruitment pipeline. Depending on what the organization’s diversity goals are, the information shown in this visual may represent success or a challenge still to be tackled. Regardless of the goal, the simplicity of this image makes it self-evident what the state of diverse hiring is like at this organization. This one visual will sit at the heart of a presentation asking for permission to take action on this issue.

Data visualization showing employee movement

You can see good examples of this kind of “make one point” visual in The Economist. The site Consultants Mind website has several good examples of these short, sharp graphs.

These simple visuals can be hard to prepare because you have to cut through all the interesting information in your head to find one or two crucial points that executives need to see. This extra effort to narrow down to a simple message is worth the time and executives will generally be delighted to see you get right to the point.

You may need to answer some questions about the data; to do this, you will need some backup slides which contain more data. These ones don’t need to be as clean since at that point you are engaged in a discussion rather than delivering a crisp presentation.

What about the case where the executives demand a deep dive into the data? In this case, it’s best to ditch the PowerPoint slides and take them right into your analytics software where you can answer their questions on the fly. In other words, our purpose is no longer telling a conclusion to executives, rather it is them exploring the data to see what it says. Different purpose, different approach.

Wowing: The Third Purpose of Visualization

The third purpose of data visualization is to wow an audience. It would be remiss to write an article on data visualization without mentioning the work of Hans Rosling. You can see his stunning TED talk videos that use brilliant animated graphics to wow the audience. If you need a standing ovation from a global audience, then this is the way to go.

The slightly disappointing truth is that most people analytics pros will never have the audience to justify this kind of presentation nor the resources to prepare it. Stay true to your purpose and admire Rosling’s work without feeling a need to emulate its sophistication.

In fact, what really wows senior leaders is not the sophistication of the presentation, but the fact that the data clearly addresses a business issue they are struggling with. For example, if leaders are fearful of a massive way of retirements, then data showing the likely range of outcomes, where the impact will be greatest, and specific advice on what they should do next will be well received. Relevance will provide all the “wow” you’ll ever need.

Conclusion

There are many useful lessons from the classic literature on data visualization, but we have to beware of the assumption that we are presenting a graph in a book that will be carefully poured over by dedicated readers – that’s not the use case we typically have in business meetings.

So our two main purposes of visualization should be:

  1. as an analyst seeing what’s there
  2. as a presenter telling one or two key facts to executives.

Clarity of purpose will keep you on track.

Author Photo
David Creelman |
David Creelman is CEO of Creelman Research. He is well-known globally for his research on people management, especially his book Lead the Work: Navigating a world beyond employment, co-authored by John Boudreau and Ravin Jesuthasan. Recently he has focused most of his efforts on helping companies get on the right path with people analytics. He was made a Fellow of the Centre for Evidence-based Management in Europe for his contributions to the field. He is also a winner of HRPS’s Walker Award.