Data Visualization: How to Avoid an Epic Fail
As I prepare for my upcoming A2 Academy Visualizing Your Big-Data: What to Use, When & Why lecture, I was looking for some examples of effective and ineffective data visualizations. The lecture is part of the All Analytics Big Data series, which is online and has no registration fee.
A few posts ago I shared an example from a humble marketing dashboard and today I’m sharing a graph from a trouble ticket system. To its defense, the author was showing how easy it was present grouped information with some code. I’m not sure if the author was seriously trying to communicate anything more than that. Just so you know I am not always picking on sad little charts. 🙂
Answering Questions with Data Visualizations
Effective data visualizations have some common characteristics, which is communicate a message clearly and quickly. It is always fun to find one that makes you scratch your head. Usually when you find an ineffective data visualization there is a marketing message [look how cool this is! or even you can do this!] not far behind.
Authors use data visualizations to answer an implied question or at least clarify a point. For instance, a manager might want to convince his boss that more support staff is needed and prepare a line chart showing the ticket arrival trend. In essence, the chart answers the question “Are more trouble tickets being opened?” or “What is the trouble ticket trend?” or even “How many trouble tickets were opened last week?”
What is the Question
I want you to study the following data visualization for a moment and ask yourself, “What question does this data viz answer?” or “What point does it clarify?”
Was it “What is the ticket arrival rate?”
At first I thought the question was “What is the ticket arrival rate” or “Which day is the busiest?” These would be logical questions for a manager trying to plan the staffing hours for a Support department. The chart appears to be time-series related with weekday as the time and sort of indicated by the title Number of ticket raised in the last week, per day by department.
So a quick analysis would be “The first of the week is rough, then it heats up again at the end of the week.” However, if you examine the x-axis you might notice that the days are not in sequential order (Sunday, Monday, Tuesday, etc). Then I thought, “Oh they must be ranking the days by arrival”. However, Friday is listed first and it was not the day with the most traffic. If the days were ranked then Monday would be the first day. I cannot really tell from this chart what the second biggest day is without more study.
Was it “Who opens the most tickets?”
Maybe the chart really meant to emphasize the department, perhaps the question was “Who opened the most tickets last week?” This would help the manager understand if one department needed additional staffing. It’s fairly easy to see that Support (blue line) opened the most tickets, but which department was second? Really, if you had to rank them, in what order do they belong?
Just like with figuring out the ranking of the days, you have to start counting in your head. If you are the manager reviewing this chart – it’s costing you time. How would you grade this chart on “efficient communication” when the manager needs a calculator or notepad to figure out what happened? [More about elements of effective data viz here.]
Have you determined what is wrong with the axis yet? The creator used the defaults and the software placed weekdays in alphabetical instead of sequential order. This is an example of what can happen when you let the software do the work for you.
Data Visualization Makeover
Is it possible that this humble data visualization is trying to communicate too much information at once? And ends up doing nothing but creating confusion. There are 2 questions: What is the daily arrival rate and Which department opened the most tickets? I think it’s confusing to have it in one chart – you spend too much time trying to figure out the information.
Here’s my suggested makeover using SAS Visual Analytics and some interactive reporting. Let’s put the data in one panel but use separate charts. In this example, the top chart shows the overall arrival rate for the week and the bottom charts provide details on who opened the tickets and the main issues. If the manager wants to see the week before, she can select it from the drop-down.
Also I added a reference line that contains the average tickets opened per week as an example. You need a baseline to know if you are getting more or less tickets than you normally receive. Probably a line that showed the average per day would be more useful since there is variation.
What I like about SAS Visual Analytics is adding the interactivity. When you look at this data, it’s noticeable that Monday had the highest tickets. The manager can click on the point and the report instantly filters to show the department with the most tickets that day and their associated issues. Support opened the most tickets and in general MS Office was the main issue.
For more details, the manager can click the department name to determine the issues. By hovering slightly, you can see there were 92 issues opened by Billing that day. And when you click the bar, the issues chart updates to show the main issues. Nice.
I think I would probably add a link to the Issues that allowed the manager to see a list of the open ticket or maybe their current status. Also I would want a tab that showed the fix response time, overdue tickets, and other similar metrics.
Even without the interactivity, when I split the chart into two charts it made it easier to understand the information at a glance. The interactivity really made the data easier to understand and answer questions on the spot. Plus … the manager could use a tablet to review this information while waiting for a meeting to start.
What do you think? What might you have added or taken away?
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