“We face danger whenever information growth outpaces our understanding of how to process it.”[i]
The ability to generate data has greatly increased in recent years, across all sectors, including agriculture. In fact, according to VCloud News, 90% of the world’s data has been created in the last 2 years alone[ii]. This “big data” is harnessed to improve health, save money, and improve efficiencies. In this era of “big data,” challenges lie not only in storing and processing data, but distilling and presenting it so it becomes meaningful and offers insights for our intended audience. Scott Berinato, senior editor at Harvard Business Review, encapsulates this idea in “Visualizations That Really Work”: “Decision making increasingly relies on data, which comes at us with such overwhelming velocity, and in such volume, that we can’t comprehend it without some layer of abstraction.”[iii]
The goal of this post is to discuss how we, as scientists and educators, can present data in clear and concise ways.
Enter data visualization.
What is Data Visualization?
Simply put, data visualization is how we make sense of, and communicate, data.
However, this term can encompass a variety of things and varies by profession – computer programmers, statisticians, graphic designers, business analysts, scientists, journalists, and professional speakers all approach the topic of data visualization differently.
I am not a computer programmer, nor am I a graphic designer. I am a scientist by training, and therefore a practitioner of data visualization. I experiment, and I have much to learn.
I have been convinced of the importance of paying attention to how we visualize data, as much by my own struggles to decipher cluttered, burdensome graphics as by any well-crafted argument. Unfortunately, scientific data is often presented in overly complex charts – charts that make data hard to interpret and consequently remember. This is true for information delivered to both the scientific community and Extension audiences. In fact, it could be argued there is a tendency within the scientific community to over-complicate things, as if making our data more convoluted will impress people with our vast knowledge.
Thankfully, scientific data presentation does not have to be cumbersome and overly complex; effective visualizations can make the message clear and memorable.
Why Should Extension Professionals Worry about Data Visualization?
Intuitively, we know that good information, when poorly communicated, cannot prompt desired behavior change. You can’t act on information you don’t understand – and having information does not equal understanding.
There is research evidence that supports this. Pandey, Manivannan, Nov, Satterthwaite, and Bertini (2014)[iv] tested the assumption that “visualization leads to more persuasive messages” by showing participants data in both chart and table form. When participants didn’t have strong beliefs about a topic, the visual information presented in charts was more persuasive than textual information presented in tables in changing their attitudes. Simply, data visualizations lead to greater impact.
So why is there not more emphasis on this important aspect of how we communicate data?
A quick Google Trend[v] analysis shows a rapid increase in searches for “big data” since 2011, while searches for “data visualization” stay relatively stagnant. Why the lack of interest and emphasis on visualizing our data? Surely as we increase the quantity of data we collect, the need for effective data visualization increases correspondingly, if not increasingly more.
In Cooperative Extension, our goal is to have impact – for people to make behavior changes as a result of information we share. In order for this to happen, we need to effectively communicate data. Unfortunately, many obstacles get in the way of effective data communication. I believe one of these obstacles is simply ignorance of the fact that data can be communicated poorly.
Lack of awareness and attention to the issue may be partly to blame, but it may not be all our fault. After all, in the past, data visualization has been left to specialists such as data scientists and professional designers. But now, due to enhanced computing capabilities, new software and tools, and the ability to quickly collect and process massive quantities of data, most Extension professionals routinely produce charts and figures – without formal training in data visualization.
As a 2016 eXtension fellow[vi], my goal is to bring awareness and promote discussion of the topic of data visualization. If Extension is to fulfill the mission of bridging the gap between scientists and the public, so the public can act on the information scientists provide, we must communicate data well.
Fortunately, numerous books, videos, podcasts, and blogs are dedicated to the finer points of good data visualization. As a starting point, in my next post, I offer what I consider my top seven elements of good data visualization.
Please take a moment to complete the anonymous survey below. Information submitted will be used to guide my work during this fellowship.
[i] Silver N. (2012).The Signal and the Noise: Why So Many Predictions Fail But Some Don’t. New York: Penguin Press.