Connect the dots to see the real picture hiding in feedback

April 25, 2017 by Dr. Jian Huang

So, there is a lot of data at our disposal these days. Lots of it. And along with all this data often comes the feelings of “I am not getting what I need from the data” or “it’s just a lot of wasted time and effort”.

Well, maybe it’s a matter of not looking at it right. Maybe you need a better means of visualizing the data. Plotting curves, bar charts, pie charts are the expected forms of visualization. They are used everywhere. However, simplistic visualizations such as these don’t always tell a compelling story…

Data visualization is not just visual. Good data visualization shows “the unseen” within the data. There are many research journals focused on the subject, each seeking to bring attention to this burgeoning field. So what is the fuss about?

Successful and useful visualization shows meaningful “relationships” hidden from the naked eye. Connecting the dots, so to speak.

When a visualization is designed for the purpose of showing relationships, even the simplest methods seem magical. For instance, when plotting quarterly revenue curves – you might find it useful to see how revenue growth relates to time, is there a time where the relationship changes? In other words, you find inflection points with the data. That’s powerful because it reveals relationships and tells a story beyond just the data points.

Since we do feedback analytics at Survature, we know this space well. There are virtually no survey platforms that offer useful analytics. I know this is a bold claim. Please correct me if you disagree. Let me give a couple of examples as evidence.

The first example is simple. Anyone who uses surveys to collect information on a regular basis will often create pivot tables to analyze results. An HR director might want to review employee loyalty across departments, to find the relationship between a workforce and areas of function. In other words, to establish benchmarks and seek anything that deviates from the benchmark, both good and bad … How do people do that? Manually in a spreadsheet most often.

To give another example. You have employees rate a list of characteristics of their workplace: empowerment, support, career opportunity, teamwork, communication, sense of urgency, quality driven, accountability, …. In addition to the rating results, wouldn’t it be better if you also knew how important these workplace characteristics are to the workforce? And even a step further, to see the relationship between their importance and the ratings?

In this second example, if you have both the ratings and their importance but they are presented in separate bar charts, one showing the rating for each of those items, and the other showing the importance of each of those items. The most powerful information – the relationship between rating and importance – is still missing, isn’t it?

We at Survature have built a unique feedback platform that captures unique behavior data and presents proven visualizations that reveal the relationships between ratings and importance. We help our users connect the dots that create serendipity moments which reveal how to innovate, solve problems and make positive changes.

If you need data analytics for feedback and other business priorities, don’t underestimate what good visualization can do to help you realize the value hidden in your data. Plotting data points may or may not be enough. The litmus test is whether or not you see relationships in the data.

The promise of big data is about being more aware of what’s going on than ever before. Linking together more aspects of understanding is a start - you can go much further by finding meaningful relationships and making powerful connections between the dots ….

Jian Huang is the Chief Executive Officer at Survature providing the vision for reinventing the way the world experiences surveys. He is a professor of computer science at the University of Tennessee (UT) researching data analysis, visualization, and human-computer interaction. His research has received funding from the National Science Foundation, the US Department of Energy, the US Department of Interior, Intel, NASA, and UT-Battelle. Jian received his PhD from the Ohio State University.