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Data Visualization for Extension Professionals: Why Does it Matter?

“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.

Google Trend Analysis of "Data Visualization" and "Big Data"
Google Trend Analysis of “Data Visualization” and “Big Data”

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.

[gform form=”https://docs.google.com/forms/d/e/1FAIpQLScYNJo9TjjiQ4yR7UYPYOZ6cFz0lzBe5hIjO6Qq6mjL9f447g/viewform” legal=’off’ title=’off’]

Endnotes

[i] Silver N. (2012).The Signal and the Noise: Why So Many Predictions Fail But Some Don’t. New York: Penguin Press.

[ii] http://www.vcloudnews.com/every-day-big-data-statistics-2-5-quintillion-bytes-of-data-created-daily/

[iii] https://hbr.org/2016/06/visualizations-that-really-work

[iv] http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6876023

[v] https://trends.google.com/trends/

[vi] https://www.extension.org/laura-thompson/

 

4 Comments

  • April 7, 2017

    Katie Stofer

    Hi Laura, looking forward to hearing more about your fellowship. I wanted to share a resource I have on map-based visualizations: http://edis.ifas.ufl.edu/WC163 based on some of my research. Also, I wondered about your statement that as a scientist, visualization is how you make sense of data – this is a question (at least around map-based visualization) that I’ve had for a while – do you actually visualize your data BEFORE attempting to analyze it, or do you still work from the numbers, then visualize it either to confirm or communicate?

  • April 7, 2017

    Laura Thompson

    Hi Katie, thanks for your response and for the link. I plan to dedicate a future post to the topic of geographic visualizations.

    Your question about visualization timing is interesting. Visualization is useful for both exploration and presentation. My opinion is that as data sets become larger and more complex, visualization early on in the analysis is increasingly necessary. Personally, when working with map based data I generally visualize it first, however the type of data I work with often necessitates this as preliminary processing in a GIS is required before statistical analysis. I can see potential scenarios where that could have downsides though. What are your thoughts?

  • April 11, 2017

    Katie Stofer

    Hi Laura – I agree, there are probably both positives and negatives to using a visualization for analysis early on vs later. I suppose the way around this is to have a team of researchers, some of whom work with creating the visualization and doing the preliminary processing, and some of whom wait to make their analysis until after the visualization is made (at which point they have to question the assumptions of the other part of the team, of course). I also think it may be that newer scientists to the field are more likely to use visualization earlier in the analysis because a) the tool is available where it wasn’t to scientists who started out with only numbers and b) they may learn to do the analysis this way during their studies and/or be more familiar with visualizations from earlier learning than again those who weren’t around when visualizations (hardly) existed could be.

    I’m curious about analysis with graphs, though – it seems more straightforward to use map-based visualizations as the original analysis partly due to the sheer overwhelming amount of data. Graphs (I think) tend to have less data.

    All of this is from my perspective as a social scientist who has interviewed oceanographers, however. My own disciplinary background is neuroscience, which involves a whole different type of visualization … but I guess there we would use “graphs” of things such as action potentials (recordings of the electrical signals and patterns of the neurons) and interpret the shapes. And I worked with functional magnetic resonance imaging, where we imaged blood flow of the brain and looked at images of those patterns. So maybe it’s not that different – those two examples for sure are a lot of points of data. An interesting research question, to be sure!

  • December 20, 2017

    Hi-Tech BPO

    Hi Laura,

    Interesting read indeed. Thanks.

    It has been an unsolved mystery if companies should actually visualize their data before they analyze it, or they can go ahead to work from the numbers, and then visualize it. It’s like the chicken and egg conundrum. There is no absolute answer. Or rather I would say there are various elements like the type, amount and kind of industry the data belongs to, that decides the next move. Businesses can never have analytic visuals unless they visualize their data to ensure it is clean, but without visualizing their data pre-hand, they cannot identify data errors.

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