Later this spring I will be speaking at an event for institutional researchers called the AIR Forum ("AIR" is the Association for Institutional Research). In speaking with the organizers, I've learned that the audience is very data-oriented and sometimes find it difficult to make their findings understandable to others. It will be my challenge to help them become better explainers.
In preparation for the event, I wrote an article that was recently published in their newsletter that focuses on antidotes for information overload.
Here are a couple of excerpts from the article:
On the diminishing returns of more data:
From the perspective of a researcher, the truth is in the data. By sharing the details of the data and accounting for exceptions and confounders, for example, researchers can present accurate and useful information that stands up to the questions and analyses of their peers.
Within the insulated world of research, this can work and can be rewarded. But outside of the research world, a different perspective is required. The audience is typically not in a position to poke holes in your assumptions. They don’t necessarily care about the exceptions. They only want to understand why they should care. Details won’t help this audience.
Ask yourself this question when you’re communicating data: Is this information for my peers or for another audience? If it’s another audience, you must switch gears and realize that more details and data come with diminishing returns. Different audiences have different needs.
On the imprecise, but effective nature of analogies:
Sigmund Freud famously said, “It is true, analogies decide nothing, but they make one feel more at home.” I love that quote because it recognizes the inherent problem with analogies—they are imprecise. It is rarely possible to find an analogy that stands up to the impeccable standards of many researchers. But are they useful and powerful in relating ideas? I think so.
Consider some of the great scientific discoveries, like Einstein’s theory of relativity. In my experience, there are two ways to understand it. The first is to have an education in physics. With the proper training, we can see the theory’s details at work. For physicists, this is likely the most powerful way to understand it. But everyone else requires something different—the details don’t work.
Thankfully, Einstein often used an analogy that provided a layperson an imprecise, yet useful way to see the big picture of relativity. The most famous is likely the train analogy, where relativity and simultaneous events are explained using people, lightning, and trains—all real and familiar things. This provides a way for non-physicists to develop a basic, yet imprecise understanding that could lead to more learning.
While you may not need to explain relativity, you may find that simple, familiar examples and analogies can be used to help your audience see why data matter and why they should care.
Read the entire article here.
Do you have ideas for avoiding information overload? Leave comment - we'd love to hear from you...