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Data Storytelling: How to avoid telling Stories that Lie

This is part 2 of a 5 part series on Data Storytelling.

Today, I’m going to tell you a story about our data (provided yesterday) that is great news.

Revenue is growing quickly, with December jumping significantly over November. You can see this in our revenue chart below:

Example of data stories that lie

This growth was led by a 1,150% jump in revenue for Product D! If the trend continues we would expect overall revenue to exceed $100,000 in January for the first time. This is an exciting time for the business, and this growth is a direct result of the strategic decisions I made in June of last year.

Why is this Story Good?

It’s not, it’s a lie.

Why is this Story Bad?

I’ve used some very common tricks in data storytelling to trick you into reading more into it than is really there. Specifically:

  • Manipulating Scale. When looking at a chart, you are used to comparing the size of bars to determine relative size. By having the y-axis start at an arbitrary value, instead of zero, I changed the relative size to make it seem like there is significant growth where there isn’t.
  • Cherry picking. I’ve selected only a few data points that make my case (growth rate of Product D), instead of considering the full breadth of the data and what it really conveys.
  • Jumping to conclusions. I’ve jumped to conclusions based on the data despite there being no clear logical basis. Why would the decisions I made last June have affected revenue in December?

In this case I was being malicious and purposefully lying. Tomorrow we’ll cover how you can have the best intentions but still inadvertently mislead your audience.

Quote of the Day: “Artists use lies to tell the truth. Yes, I created a lie. But because you believed it, you found something true about yourself.” ― Alan Moore, V for Vendetta