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Data Storytelling: How to avoid telling Stories that Mislead

This is part 3 of a 5 part series on Data Storytelling.

Today, I’m going to tell you a story about our data that is true but misleading:

Example of data in stories that mislead

Overall, revenue for all products has been fairly even. There is a notable exception of a big jump in revenue for Product D in December, which jumped over 1,000% month over month.

However, a single product’s revenue dominates all others – Product C comprises more than 50% of the total revenue. This means that the revenue from Product C is larger than all three other products combined.

Data in an example of stories that misleadWhy is this Story Good?

This story is true, which is a good start. By using a relative scale in the first chart, the significant deviation of Product D becomes very clear. The story attempts to break down the overall trend with some insight into the composition of products, but falls short.

Why is this Story Bad?

It is very misleading. I’ve made some commons mistakes in communicating the data which make it easy for you, as the audience, to reach inaccurate conclusions.

  • Lack of Clarity. I haven’t labeled the axes of my charts or in my discussion! In the first chart the y-axis is growth rate while the pie chart is revenue in December. However, since there is no way for you to know that you can jump to all sorts of different conclusions.
  • Confusing Charts. The two charts switch which colors represent which products. Yellow represents Product D in the first chart and Product C in the second chart! If you read this quickly, you might think the fast growing product is the largest, which is not true.
  • No Context. I mention that Product D grew by over 1,000%, but not what the absolute value is! For example, a 1,000% growth from $1 is $11, a change of $10, but a 1,000% growth from $100 is $1,100, change of $1,000. Avoid using relative measurements without providing the proper context.

Finally, I used a pie chart which many people in data visualization think is a big mistake.

Tomorrow we’ll finally tell the true story of our data, and in doing so highlight how bad these past two stories really were.

Quote of the Day: “The more you leave out, the more you highlight what you leave in.” ― Henry Green