Today, let’s finally tell the real story behind our data:
Overall, revenue is trending downwards over the past year (dotted line). There is a clear peak in the summer and dips in the spring and fall, and based on some prior years of data the expected range (green bar) reinforces this as a seasonal trend. Below is a chart of overall revenue that shows this trend:
As you can see, April and May were slightly anomalous months in this trend, but overall the pattern of revenue is very consistent.
Hidden under this overall trend is a shift in revenue composition. Product A has been declining over the course of the year, while Product C has been increasing. In December, a significant jump in Product C revenue resulted in the first month where Product C revenue was higher than Product A.
If this trend continues, we can expect revenue to increase into the following year.
Why is this Story Good?
This story follows a lot of the best practices of data storytelling:
- Start with the big picture. Frame all of your data communication within a bigger picture. In this case, the story first states that overall revenue is trending downward and looks seasonal.
- Show context. By highlighting the expected range and linear trend of the overall revenue in the first chart, it is easier to grasp the larger pattern hidden in the data. These visual guides are important context for interpreting the raw data.
- Highlight important drivers. By identifying the two important drivers of overall revenue (Products A and C) and describing their relationship, an insight that was otherwise hidden by the top chart becomes clear. This is different from cherry picking data because we are highlighting meaningful and important drivers of the data.
Most importantly, in this case I let the data tell the story instead of trying to tell a story with the data. This is an important point because if you try to fit your data to an existing story you are going to fail to tell the entire story.
Why is this Story Bad?
It’s not, although I could have gone into more detail. Overall I’m pretty happy with it.
Like most honest and straightforward data stories, the conclusion is not nearly as clear as when we were lying or misleading. This is because real world data rarely gives us a single, clear message. You will need to rely on your expertise and your audience’s judgment to reach conclusions.
Tomorrow, we’ll wrap up everything we’ve learned so far in some general rules for data storytelling.
Quote of the Day: “All great literature is one of two stories; a man goes on a journey or a stranger comes to town.” ― Leo Tolstoy