How are Triathlons and Analytics Similar?

How are Triathlons and Analytics Similar?

Recently, I finished my second Ironman 70.3 triathlon (for the record, that is NOT me in the photo). The triathlon is made up of three segments: swim, bike, and run. While the finishing time is reflected as a single value, there are 5 distinct times that comprise the total. Not surprisingly, the times for the swim, bike, and run portions are included, but also in the mix, are the two transition times. A transition is the time spent between the different events. Once an athlete crosses the finish line, these five times are summarized for the final result.

During this last race, I had some extra time (way more time than I expected) to ponder how the results are conveyed, and how there is so much more to the triathlon story than just the final time and the sum of the parts. There is the impact of the weather, how much training time was spent in each discipline, how effective that training was, conditions of the course, etc. Yet, when the dust settles and the results are in, it’s only these handful of metrics that are displayed.

The same scenario exists in analytics and business intelligence deployments. So much emphasis is placed on displaying the final results in a dashboard or report, that a lot of supporting context is lost. Context that could help to justify the results being seen, highlight and validate that previous business decisions have paid off, or worse, had a negative impact. This is why the current trend, one that isn’t being adopted quick enough in my opinion, is to start telling data stories. 

A data story is where in addition to analytic assets, contextual information is included to help guide and inform the user. This way, users with varying levels of data literacy can all arrive at the same interpretation of the data. Now, special care must be taken to not lead the witness by framing the story with bias from the author (Just the facts, ma’am. Just the facts), but by and large, data stories are much more effective at delivering the desired message to the masses.

Being a data geek, a triathlon, and the whole training process, is FULL of metrics and various data points, so it’s a little disappointing that the results are displayed the way that they are. Hmm, maybe a data story should be built around all of the data gathered while training for an event like a triathlon …

“Just the facts, ma’am. Just the facts”

“Just the facts, ma’am. Just the facts”

Today I had the opportunity to attend Tableau’s “Design tricks for great dashboards” webinar. The speaker was Andy Cotgreave, a visualization expert and Tableau veteran. During the webinar, Andy touched on ‘framing’. As a level set, framing is about the context in which data is presented, which is critical because author bias can creep in to lead the witness, err, dashboard viewer into arriving at a biased opinion. This has been a concern of mine for years and it was great hearing Andy describe the problem so effectively.

Although the view of the data is static and there should be only one version of the truth, we are all unique individuals with different opinions, backgrounds, etc, thus, we don’t all interpret the data in the same way. I’ve always stated that if you showed one report, without context in a management meeting, each member of the audience would interpret the report differently. Essentially, a report can be twisted to fit a lot of different narratives. During the webinar, Andy used these two visuals to explain this scenario.

The first is a well-known infographic (created by Simon Scarr) that was very impactful. This striking visual depicts the loss of life as a result of the military engagement in Iraq. Certain design choices were made, like the deliberate choice of colour (red) and using the visual metaphor of dripping blood, to convey a very polarizing view.

But what if some simple changes were made to the infographic? I’m not talking wholesale changes to the layout and charts, I’m simply talking about tweaking the colour, orientation, and headline. As you can see below, taking the EXACT same infographic, rotating it 180 degrees, swapping out the red for a blue, and modifying the headline totally changes the narrative to something more positive.

At first glance of the original infographic, my visceral opinion was negative, and I had thoughts about how destructive and senseless war can be. When viewing the modified infographic, my first impression was “hey look, fewer people are dying”. Definitely a more positive narrative than the original infographic invoked.

It is our role, as the data literate, to ensure that when building visualizations our personal bias and opinions don’t influence the interpretation of the results. Easier said than done though. When tasked with creating new visualizations, I focus on the questions that the audience is looking to answer. Once the questions are understood, the focus shifts to providing the facts required to answer those questions. My preference is to not inject headlines or commentary into the visualizations.

  • But without the commentary aren’t the visualizations open for interpretation, thus propagating the ‘multi-version of the truth’ scenario?

Definitely, especially when the audience is non data literate and doesn’t have the experience in interpreting analytics. So how do we bridge the gap to resolve this? To me, the best way to solve this problem is by focusing on finding correlation, and to a certain extent, causation (this is a slippery slope to injecting personal opinion though, so beware) and adding that as context to support the analytics. When the data to support the decision-making process resides across different data sources, or BI platforms, there is an opportunity to tell a larger, more complete, data story. When commentary is placed into each visualization, legibility may be impacted when bringing together multiple artifacts into the data story. Not only that, an opportunity is lost to establish correlations that transcend single visualizations and/or platforms. An effective data story should contain:

  • The facts required to answer the audience’s questions
  • The necessary visualizations to convey the data story
  • Non-biased commentary that guides the audience to correlations in the data
  • When using multiple analytics solutions to tell the story, the emphasis should be on the data within the visualization and not technology that created the visualization

By sticking to the facts, a greater emphasis will be placed on the raw data and the correlation versus forcing the audience into one potentially polarizing view or opinion.