Two Types of Data Stories and Tactics for Handling Each

When we know the outcome vs. making our user part of the story

Did you know there are two types of stories that can emerge when you’re analyzing data in Tableau? This video will discuss three specific tactics for handling each type so you can make your data visualization as actionable as possible.

Hi, this is Ryan with Playfair Data TV. And in this video, we’re going to be discussing the two different types of stories that can emerge when you’re analyzing data in Tableau. And I’m also going to be providing a few different tactics for how to handle each of those types of stories to help you have the most effective data visualization possible. The first type of story that can emerge when you’re visualizing data and analyzing data is when you as the visualization author know what the outcome of that story is going to be.

So you’re the analyst. You’ve been analyzing the data and something has emerged. You found an insight.

When that is the case, it’s your responsibility to communicate that insight in a way that increases the chances of somebody causing an action or doing an action. The second type of story is when you make your end user part of the story. What’s great about Tableau is there are certain technical features that you can integrate within your dashboard to make it so that you don’t necessarily need to know every single answer, every single question that your end user is going to ask. You can create a user experience for them that will allow them to do their own discovery and decide what’s relevant to them and find out what’s relevant to them in the business.

When we know what the outcome is, one of the best possible ways to communicate that insight in a way that somebody understands it and will hopefully take an action on it is to use comparisons. At a basic level, this is why a bar chart is so effective. It compares categorical data.

Comparisons always help us avoid the dreaded question, “So what?”. It’s one thing just to show somebody a raw number. But that’s going to make them ask so what. They don’t know why that number is what it is. There’s just no context that helps them understand what to do with that information.

So comparisons like a bar chart– and there are a lot of different charts discussed here at Playfair Data TV. Just a couple of others that come to mind, a bullet graph, which is a way to take a bar chart a step further. It adds some extra context in the form of a comparison point. A comparison point is typically a prior period. Sometimes it’s a goal if you’ve got those defined in your business.

Small multiples is another one that creates kind of a tic-tac-toe board of data visualizations and packs a lot of comparisons into a small amount of space. Another technique for communicating the story is when we know what the outcome is, is to communicate that story in writing. I just feel like this is an often overlooked technique. It’s really simple to do.

But sometimes we as the analysts get so close to our analyses that we kind of forget that this stuff is not common sense to our end user. We might be so close to a data set in an analysis that we think the insights we’re finding should be common sense. How can you not see that trend we’re seeing on that line graph?

But just remember to provide some context through writing. That could be in the form of a title, a subtitle, captions on what’s going on. And then you can literally annotate insights within any Tableau worksheet by just right clicking on it and clicking add an annotation, just to provide that context.

And then lastly, if you’ve got the opportunity– this isn’t always the case– but if you have the chance, you might as well communicate the story verbally. It doesn’t get much better than when you’re in a meeting with your data visualization up in the background behind you. You’re using good visuals that are easy to understand.

Those visuals are based on a data source that everybody trusts. And you just spell out what you found and what we should do about it. That’s a very effective way to cause action in your organization.

These are a few different ways to make your end user part of the story. This is the second type of story within Tableau and data visualization. By far, number one in my opinion is to use parameters. These are extremely flexible. They can be built with six different data types. And they’re user generated values.

We code the allowable values once. But then we transfer the control of the analysis to our end users. Whatever value they choose is what becomes part of the story. It’s a very effective way to make the end user part of the story.

Slightly easier and maybe more practical way to do it is to use dashboard actions. We’ll talk about that in a different video. But essentially, you can set up ways within your dashboard where if you click on something on one sheet, it influences something somewhere else on the dashboard. And an even more practical way to create a similar user experience is through just filters.

By showing a filter, the end user can decide which dimension members are included or excluded from a view. But in all three of these cases, what we are doing and what’s important about this is we as the dashboard author, don’t need to answer every single question. And we also don’t need to know every single question that people are asking.

We just either provide a parameter, dashboard action, and/or filter that allows them to make their own selections and make it relevant to them. I want to share one example that I found to be particularly powerful from my Tableau Public portfolio. This visualization asks one question at the top.

It says how long would it take you to earn as much as a Major League Baseball player? There is then a parameter selection that allows the end user to type in their own salary. When they do that, they become part of this visualization.

Whatever salary they type in that box becomes part of the visual for that first column where it says You. The number of blocks will actually change based on what the end user types in that box. The column in the middle is the player that they’ve selected. And then on the right is the US average. So yet another comparison.

So I’m actually kind of leveraging both here. I’ve got comparisons. I’ve also made the end user part of the story.

Just for context and perspective, the salary that’s entered in that box is $135,000. The player is Mark Texi, and the statistic is home run. And it says Mark Teixeira made $7.7 million per home run last season. At your current salary, it would take you 685 months or 57.1 years to earn that.

And this is where this tactic comes into play. I don’t know about you. But those numbers for me, as a sports fan, I hear these types of numbers all the time. And you’re almost desensitized to them. That doesn’t mean much to me.

OK, he made $7.7 million. That’s such a different scale that I can’t really put that into context. But when I made my end user part of the story, and they type in their own salary, and they see the direct comparison, that is them inside of this visual now, and they see the caption that has been calculated on their own salary, that has almost a visceral effect. It’s totally different. Now I understand and I have that perspective. And that was made possible by this power of making my end user part of the story.

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