Part 1: Nail Down Your Audience & Your Big Idea
The better you understand your audience, the better your presentation will be, and the better results you’ll get.
Imagine you’re a 4th grade science teacher who just wrapped up an experimental pilot summer learning program. You surveyed the participating kids at the beginning and end of the program to determine how their attitudes about science may have changed. You believe the data shows a great success story, and you want to continue the program.
But if you’re headed into this presentation thinking you just want to tell folks about it, then fuggetaboutit. 99% of the time, you’re not merely informing your audience; you’re persuading them.
As the 4th grade science teacher, who might your audience be? The angle you take for each audience will be different.
- Parents of students who participated: You’re persuading them it was worthwhile.
- Parents of prospective students: You’re persuading them this program is THE pathway to a successful career and adulthood.
- Other teachers who might be running a similar program or would like to: You’re persuading them to do it.
- The budget committee that controls the funding for your program: You’re persuading them to give you money.
In each of these cases, you must narrow down your argument into one sentence that articulates your unique point of view and conveys what’s at stake. Nancy Duarte calls this the “Big Idea.”
Let’s say you’re presenting to the budget committee and advocating for more funding. Here’s an effective Big Idea:
“The pilot summer learning program was successful at improving students’ perceptions of science and, because of this success, we recommend continuing to offer it going forward; please approve our budget for this program.”
Now what? In our next posts, we’ll consider how to use data effectively and ethically to convince the budget committee to approve our budget request.
Part 2: Present Data
Clean water is necessary for biological life to survive. We need it to live and stay healthy…but what if it’s evil?
- Water can be extracted from rocket fuel.
- Water is the main ingredient in pesticides.
- Water is the #1 cause of drowning.
- 100% of people exposed to water will die.
Sure, all of the above are true, but that doesn’t mean you should avoid water — that would be impossible, anyway! These are instances of presenting data unethically.
So how can you present data ethically? First, consider your message. The facts may not be distorted, but the way the information is presented may be altered to intentionally or unintentionally exaggerate or understate the facts.
Here are a few unethical strategies to avoid when you present data in graphs and charts.
In this case, the Y-axis does not start at zero, so the data is exaggerated.
In the charts below, the differences between the interest rates are negligible — they’re 0.002% different from one another. But the chart on the left is much different than the chart on the right because the Y-axis begins at 3.140% on the left.
Takeaway: Start your Y-axis at zero like the chart on the right.
Area as Quantity
With area as quantity, the data is distorted because the area of the bars or pieces within the chart that represent the data don’t match their values.
In the chart below, the people on welfare and the people with a full-time job are not significantly different — only 6.9 million — but the chart, whose X and Y axes are not labeled, makes it seem like a much more significant difference.
Takeaway: Make the area of bars, circles, or whatever you use to represent the data proportional to the values of the data.
Correlation as Causation
Sometimes when we see a chart whose data almost matches, it can be easy to think that one thing caused the other.
In this example, the data might lead you to think that the increase in murders caused more people to buy ice cream, or that murders increased because more people bought ice cream. It’s more likely they aren’t causally related at all. One didn’t cause the other; it’s simply a coincidence that they seem related. We call this “correlation.”
In actuality, the rates of murder and ice cream purchases are dependent on the weather: the hotter it is, the more ice cream is sold and the more murders are committed. That means the two are correlated, not causal.
Takeaway: In similar data, one thing might not have caused another; they could be simply correlated (coincidental).
For more funny examples, visit Spurious Correlations.
When it comes to pie charts, it can be difficult for the brain to interpret results. In this example below, the pieces of the pies look strikingly similar from one pie chart to the other.
Comparison data is better presented in bar charts or line graphs, where your audience can easily see the differences in data.
Takeaway: For comparison analyses, use bar charts or line graphs instead of pie charts.
Now that you know how to ethically present your data, where does IntelliBoard fit in? Our next post will show you how to use IntelliBoard data to effectively tell your story and convince your audience.
Part 3: Tell Your Story with Data from IntelliBoard
IntelliBoard can show you a wealth of information about your data. But instead of showing your audience endless reports, you can use IntelliBoard data to get straight to the point.
Some key design choices and storytelling techniques can help you persuade your audience further:
Use Arrows and Boxes to Point Out Info
Direct your audience’s attention to what matters using arrows or boxes that highlight information.
Zoom in on Info
Rather than showing your audience an entire graph, chart, or report, which can make it hard for your audience to read even on a big screen, zoom in to make it readable. In the example below, we’ve even used a box to highlight the information we want them to pay attention to.
Use Storytelling Devices
Stories can reach your audience in an immediate and convincing way. Depending on your audience and the length of time you have to present, you may want to incorporate certain storytelling techniques into your presentation.
Instead of simply throwing information at your audience using the techniques we discussed above, you could also narrate a story about one real or theoretical person in your data.
Written for #OEB21 by Tonya Riney of Intelliboard.