Online tableau training, tableau tips, & video tutorials

Storytelling

Memberships

The Parallels Between Storytelling and Data Visualization

Preview

Instructor

Ryan Sleeper

Undeniable links between graphs and old-school storytelling

This video discusses the three elements found within every story and how those elements equate to the practice of visualizing data in Tableau.

Hi. This is Ryan with Playfair Data TV, and welcome to the storytelling track. You know, I admit I was reluctant to address storytelling in the context of data visualization for quite a while. Actually, I got kind of frustrated with this topic, because it became kind of a buzzword in data visualization.

You’d always hear people saying, you need to tell a better story. I can’t see the story in this. Why aren’t you a better storyteller? But that was very rarely backed up with specific tactics on how you could become a better storyteller with your data.

And that’s what this track is all about. And while I got frustrated with that, it inspired me to do some research, and there really are some undeniable parallels between storytelling and the practice of data visualization. I actually find it quite fascinating.

What I found is that every story contains at least these three elements. And when I say story, I mean old-school, since the beginning of spoken word, storytelling. All stories contain these three things.

They contain your characters. They contain a plot, or a storyline, if you want to think about it that way. And they contain a narrative. And the way that I equate those three elements to the practice of data visualization, is I think of the characters as the fields in the view, the dimensions and measures that we’re trying to slice and dice to glean insight from.

The storylines, or the plot, those are the insights that emerge. When I’m doing my analysis, and looking at my dimensions and measures in different ways, different insights, or storylines, are going to emerge.

And then the third, which I believe as a data visualization practitioner, that we have the most control over, I think of the narrative as the style in which we communicate those insights that emerge. This is the area that we have the most power over. As the author of this data visualization, we can communicate those insights in different ways, to hopefully help them cause action.

I’m going to give you one example of how storytelling techniques that we’re going to be talking about later in this track, help bring an old story to life. And I’m picking on soccer, European football here, but this is true of any sport. I’m a big sports fan. If I go look to see how any of my teams are doing, whether it’s NFL, NBA, Major League Soccer as you see here, Major League Baseball, I’m presented with what, the wall of numbers. And in my opinion, this is the worst possible way to look at data.

I’m picking on soccer here because, while I could use an example from any sport, soccer even affectionately refers to this as The Table, and they’ve been looking at their sports standings this way for over 100 years. So I took a shot at re-imagining the traditional soccer table and I wanted to put my own narrative on it, apply some storytelling techniques, to hopefully bring this data to life. And what I ended up with was this view.

I won’t get into all the nitty-gritty with all the different statistics here, but I do want to point out a couple of key elements. First, one of my objectives with this visualization, compared to this, is I wanted to use the same amount of space. I wanted to prove that I don’t have to have a whole bunch more room to show all these additional insights. I can just leverage the power of storytelling and using my own narrative, or style, to communicate insights, to really bring this to life in even the same amount of space.

First thing I want to point out is, instead of seeing every team at the same time, the first thing that the end-user gets to choose is their favorite team. I’m from Kansas City. We have a soccer team here called Sporting Kansas City, so I’ve selected that specific team, and now they are the focus of the data visualization.

This is going to be one of the techniques that we cover later on in this track, but I’ve made my end-user part of the story. Instead of listing every single team, the end-user has the ability to just choose what’s relevant to them, and that becomes the focus of the story. On the left hand side, there at the bottom, that’s the main insight for the season. That’s the team’s overall record. How many games they’ve won. How many they’ve lost. How many times they tied.

I would consider that a descriptive insight. It’s telling me what the record is, but it’s not telling me what to do about it. If I’m a coach or a stakeholder in the team, it’s not really explaining why our record is what it is, or how we can improve and become a better team. But everything else is prescriptive. It’s going to explain why our record is what it is.

Let’s start on the bottom right quadrant, there. This area looks at several offensive and defensive statistics, and it compares our own team that we’ve selected, to the league overall, as well as the individual conference that we play in. And these are all just absolutely packed with information.

The first chart, under where it says Total Points, under League Splits, that is the table. So in that small amount of space there, we’ve taken this view, and consolidated essentially into that one chart type. It’s basically the table flipped on its side.

All of these lines also mean something. The red lines are just reference lines that show the average for the league, or the conference. So at a glance, I can see are we doing better than average, worse than average, in these eight different contexts.

In the top right corner, we can see the running total of points we’ve earned throughout the season. This is kind of telling the whole story of the season. How good have we done recently? Do we have a good winning streak, a long area where we would just tie?

Again, the lines all mean something. The red line means we’re in playoff contention. The gold line means we’re in contention to win the entire league, as a whole.

But I can’t tell you how many more insights I was able to glean from this view, in a fraction of the time, compared to this view. And the reason this is applicable in a corporate environment, is I bet you’ve got a lot of reports around the office that look like this, million row Excel spreadsheets. And I strongly believe that it’s never too late to add your own narrative, or your own spin or style, even to an old data set.

This one that we’re looking at, was an example that’s been around for over a century. So this storytelling track is going to be all about how to breathe new life into our existing corporate reports, by leveraging some storytelling tactics.

This has been Ryan with Playfair Data TV – thanks for watching!