eXtension is thrilled to welcome three new faces (and one familiar face in a new role) to staff meetings.
Impact Collaborative Program Manager
After serving and guiding communities and professional development for the eXtension Foundation for many years, Ashley Griffin has accepted a new role as the Impact Collaborative Program Manager to design the process of the 2017-2018 Impact Collaboratives. Ashley has devoted her career to the work of the Cooperative Extension Service beginning as an Extension Associate coordinating equine youth activities for Kentucky and national programs then later as a Communications Specialist for the University of Kentucky, College of Agriculture, Food and Environment.
“I am especially excited about the new direction of the Impact Collaborative and the potential impact it has for all Extension professionals to create innovative approaches to local programming. These new strategies have impressive potential for professionals who are willing to embrace design thinking by uplifting and energizing the work they do every day. What an amazing time to be an Extension professional and for me to personally have the opportunity to be a part of this process. Every element of my background will be drawn upon to make the new Impact Collaborative an exciting and valuable experience to our eXtension Foundation members and the Cooperative Extension Service.”
Molly is joining eXtension after almost 19 years working for the University of Wisconsin-Extension, Cooperative Extension as an Instructional Design and Technology Specialist. She holds an M.S. In Curriculum and Instruction, concentrating in Educational Communications and Technology from the University of Wisconsin-Madison. She lives in Madison, Wisconsin with her husband, one of their two sons, and two very fat cats.
I am excited to join eXtension as the new Instructional Technologist for the Impact Collaborative because I will be able to work with a visionary, future-oriented team that supports Extension colleagues to make a difference on local issues across the country. I anticipate the breath of issues and projects will be varied. I enjoy finding solutions together that will meet the unique needs of you, your learners, and programs.
I am passionate about learning and enhancing learning through the use of technology solutions. I will share my enthusiasm, tips and techniques to help colleagues grow professionally. I’m also curious about gadgets- both tech and cooking. Ask me what’s the latest gadget I’m having fun with. I’m always experimenting with a new one.
As a creative designer and food system researcher, Melanie focuses on artistic expression, community leadership, diversity in entrepreneurship, and urban food system growth and development. As a strategic advisor, Melanie has worked on projects that inspire her and that are aligned with creating a better Earth and better communities in a world value system focused on people and planet.
She is passionate about the intersection of innovation, technology, design, research and emerging markets. Melanie has spent her last decade collaborating on community-focused projects. Her most recent work has been bridging her love of design and improvisation in ‘collaborative platform development for social impact’ including work with organizations such as the North American Food System Network, CropMobster and Open IDEO’s Food Waste Alliance, Sirolli Institute, the International Food Blogger’s Association, Sacramento’s Urban Ag Coalition and UC ANR’s Innovation Vine Collaborative.
“What excites me most about joining the Impact Collaborative is working with a creative team, exploring ways to amplify the social impact of Extension professionals by expanding on their knowledge of design thinking and building on a pre-tested program that provides skills, training and that provides creative license to explore new ideas with creative confidence in a team setting. As a Food System Impact Fellow, I get to study, explore and work with creatives shaping ideas that can impact the future. I can’t imagine a better way to spend my time.”
Jennifer Cook will serve as the eXtension Digital Green Fellow and will work to pilot the innovative technology platform of Digital Green with selected Impact Collaborative projects.
She holds an MS in Agriculture from Colorado State University. Inspired by traveling and trying new ideas, Jennifer has developed innovative programming and education for her clients in Colorado. She hopes to foster a partnership with US Extension agents and Digital Green in developing a few pilot programs that use software to streamline food system market linkages.
“I am really excited to expand Extension engagement and develop programming ideas in food systems and explore the connections with an innovative technology platform.”
I’m going to share some not-so-pretty (and not-so-effective) graphics that I have made…and presented…multiple times. Then I’ll walk you through the steps I took to create new and improved versions using my seven elements of good data visualization.
First, about the data. These data were collected by the Nebraska On-Farm Research Network. Farmers working with the network were trying to determine the optimum planting rate (population) for soybeans. Each picked several planting rates (treatments), replicated and randomized these treatments, and at the end of the growing season, recorded the yields. Their goal was to determine which planting rate maximized yield and, more importantly, profit.
Initially, this is how I presented the data, circa 2015.
Original graph
Frightening, right?
When presenting the data in live presentations, I broke the first monster graph down into a series of three slides, one for each year.
Original graph of 2006 dataOriginal graph of 2007 dataOriginal graph of 2008 data
Can you determine what the “take away” message of these graphs is?
My intended message was that research results had shown very little yield increase as soybean seeding rate increased, within the range of seeding rates we tested.
Were you able to come up with that message?
Overall, I think these graphs are much more difficult to interpret than necessary.
I set about to give these graphs a makeover for a 2017 presentation, using the seven elements of data visualization that I presented in my previous eXtension blog. I took the following steps, which illustrate my thought process in improving this graph.
Step 1: What is the point?
After thinking through my point, I boiled down my message to this: “Soybean yields increased very minimally as seeding rates increased above 90,000 seeds per acre.” A second important point was: “The lowest seeding rate was most economical because the increase in yield realized by increasing seeding rates did not offset the increase in seed cost.”
Stating this message explicitly provided much-needed direction – and helped me determine what was not important. Because I was concerned with the overall pattern in the results, the precise location and year of each study were not important.
Step 2: Choosing the right chart
Scatterplots can be an excellent way to show a relationship between two things (like soybean planting rates and yield). Connecting lines imply the continuity of the data and can allow us to compare multiple series of the data (in this case, research sites). In my case I was wanting to show a lack of difference, so I started experimenting with plotting the data in this way.
Scatterplot with connecting lines
This starts to better communicate the data and gets all the information onto one manageable sized graph, but it is still busy. The legend is extensive and doesn’t provide needed information. The Y-axis also goes from 35 to 80 (rather than 0 to 80), which is misleading and makes the differences appear to be greater than they are.
Step 3: Less is More
Here I have removed the legend (and unnecessary site and year designations) and reset the y-axis to 0 to 80.
Modified scatterplot with y-axis from 0 to 80 and legend removed
At this point, after consultation with a statistician, I decided to include only the sites where the same four planting rates of 90,000, 120,000, 150,000, and 180,000 seeds/acre were tested. This provided a better fit for the data and let us perform a more appropriate statistical test. I also updated the data to include data that had been collected in 2016 from three additional sites. Since these sites tested planting rates of only 90,000 to 180,000, I updated the x-axis to include only this range.
Scatterplot with adjusted x-axis
Now the trend is starting to be more evident.
Step 4: Use color intentionally
Since color is no longer connected to site and year designations in a legend, it can be used to emphasize other information. From presenting the data in the past, I knew that people often asked if the trends shown by the data were true for both non-irrigated and irrigated conditions. For this reason, I chose to use color to designate irrigated sites (blue) and non-irrigated sites (orange). I also made the gridlines and axis numbering a lighter grey and less prominent, since we are concerned with the overall trend rather than exact values. Already, this graph is much less overwhelming to look at.
Use color to designate irrigation
I needed to designate what the orange and blue colors were indicating. Instead of a separate legend, I included text in colors that coordinated with the colored lines on the graph. This strategy allows viewers to get almost all the information they need immediately when they are looking at the graph rather than having to look back and forth between a legend and the chart.
Leverage consistent color for labeling
Step 5. Create pointed titles and call out key points with text
At this point the trend is fairly obvious and there is room to add in the average statistics. I used a black line and a larger font to make the average more prominent directly on the graph. I noted actual values for the average statistic only. (Think how cluttered it would be to show values on the chart for every data point.) Showing the values only for the average communicates the important information – the overall trend.
Add average statistic
The last step was adding a title. Rather than a boring, uninformative title like “Yield versus planting rate for soybeans in Nebraska” I tried to bring my main point home using the title on the graph below.
Add an informative title
The final addition I made was to include source information on the bottom right. This gives credibility, lets people know where to find more info, and, in the case of data you have collected yourself, is a great way to promote Extension or your university.
Step 6: Get feedback and iterate
As I presented this data at winter meetings, a common question was “What were the average final stands for each planting population?” or “How many soybeans do I need to have at the end of the year?” To try to address these questions, I created an iteration that displayed this information with the planting populations. This version seems a little more cluttered to me, but I think it is worth it since that information was being requested.
Iteration based on audience questions
As you may recall, a secondary objective was to communicate that increasing soybean seeding rate did not pay off in terms of increased yield. Rather than create a separate graphic for this objective, I used the yield data presented in this graphic to demonstrate the very small yield increase, and then when presenting, provided a second slide with calculations of profit for each rate. This worked out well as a discussion slide.
Before and After
Here are the completed “before and after” graphics. What do you think? Does this graphic better communicate the main point? What changes would you make? Let me know in the comments!
To prompt behavior change, we must be able to effectively communicate data. Not convinced? Read this post on why data visualization matters. The goal of this article is to dig in deeper and present some foundational concepts for creating good data visuals.
A recent eXtension webinar[i] described numerous tools and programs at our disposal for creating more engaging data visualizations, so I won’t address those sorts of resources in this post. What I hope to impart are fundamental concepts of data visualizations that are cross-cutting and applicable, regardless of which tool you choose to use to create them. I have distilled these into my top seven characteristics of good data visualization.
1. What’s your point?
Our goal is to present scientific data in a clear and simple way. But do not misunderstand me; I am not advocating for over simplistic, watered down presentations of science. For example, Nate Silver’s FiveThirtyEight[ii] website, featuring data visualizations and journalism on topics of politics, economics, science, and sports, presents lots of complicated data, often using chart types that are unfamiliar and atypical. And even though we have probably all been warned against using visuals that depart from the norm, the site is ranked 618 in the U.S according to Alexa[iii], and, according to Quantcast[iv], over 371,000 visit the site each month in the U.S. Despite my lack of interest in sports in general, I have found myself browsing through numerous sports-related stories on Silver’s site. What makes these fairly complex and unfamiliar graphs engaging and worth spending time looking at? I propose that one key factor is that the authors know their point.
When you are presenting a graph or chart, do you think through what you want people to understand and walk away with? I know that I am guilty of approaching data visualization with the goal of displaying all of the data as neatly and completely as possible. While not a bad idea, more must be considered than whether I was able to fit all the information into the display. Before finalizing a graph to share with others, take a step back and ask yourself, “What is my point?” Then, determine if the graph actually conveys that, or if there is a way to make your point clearer. It could be that a different chart type or color scheme would help elucidate your point.
2. Choose the right chart
I suspect that by and large, bar charts are the most used chart type, and possibly for good reason: they are simple to read and people are familiar with them. However, they are not a one-size-fits all solution, and numerous other options should be considered. The following charts are ones I have experimented with in the last year.
Consider trying one of these chart or graphic types out this year. All of these examples were made in Excel – not with any special software – using some creative “tricks” to make them possible. At the end of this post I provide some resources to help you learn these tricks.
Simple text
When you have one or two numbers that tell the story, highlighting a single number is a great option. A caution, however: do not overuse this simple tool or it will lose its impact.
Example of simple text to communicate data
Heatmap
Tables are rarely a good tool for showing data in a live presentation, but they do give you the ability to present a lot of detail and can be useful in printed materials. Combining a table with the technique used in a heatmap – that is, adding colors that vary in intensity to show relative performance – can help readers more quickly process and see patterns. In the example below, the darker colors represent higher yields, allowing the reader to see at a glance which combination of nitrogen application rate and seeding rate results in the best yield.
Example of the heatmap technique applied to a table
Layered bar graph
A layered bar graph is essentially combining the two bars of a side-by-side double bar graph. Both the grey bars and red bars are assumed to start at the 0 point on the x-axis. This allows an easy comparison showing us how much more there is of the grey than the red. Combining the bars is a good technique for saving space and clearly illustrates the difference between the two things you are comparing, especially when you want to emphasize the relative difference and not necessarily the quantitative difference. Instead of a legend, color in the title and color of the bar segments are used to communicate information about the different elements being compared.
Example of a layered bar graph
Small multiples
Small multiples is a great tool for breaking complex information into an array of manageable and comparable information. The technique uses multiple views to show different partitions of a dataset, using a series of similar charts or graphs with the same scale and axes that can be easily compared. There are numerous uses for small multiples, and many chart types can be broken down into small multiples; this examples uses horizontal bar charts.
Example of a small multiples graph to break down complex info
3. Less is more
Eliminating unnecessary legends, gridlines, tick marks, and colors will clean up the graph and allow you to focus your learner’s attention on your point.
Eliminating the legend is a good strategy to clean up the graphic, and, if done well, makes interpretation of the graph quicker. Labeling bars directly, such as in the small multiples example, makes it easier for the viewer to process information because they do not have to look between the legend and the main part of the chart to determine what each color in the bar chart represents. Color also can be used in a similar way, such as in the stacked bar example. Colors in the title and on the average lines indicate what the grey and red categories are, making a separate label unnecessary.
Consider whether eliminating axis labels and instead labeling the points directly might be advantageous. When the actual numeric value is important, label the points directly; when the overall trend is important, leave the axis labels in place. To reduce redundancy, however, do not use both axis labels and individual point labels. One exception to this guideline would be to use the axis labels, but label a few key data points to draw attention to them.
4. Use color intentionally
I have seen many graphs like the following. In this case, each individual site was given a different color. The graph is bright and eye catching, yet the color is not used in a meaningful way. Separating the various sites with different colors is not important and only detracts from the overall point.
Example of color not used purposefully in a layered bar chart.
Color is a powerful tool and should always be used to convey a message. When I am developing a graphic, I like to first make as much of the graph as possible grey. Then I go back and begin using color to make the key point stand out. In the following graph I have used a lighter shade of red for the late planting date, and a darker shade of red for the early planting date. Color in the subtitle is used to designate what the different colors of bars represent and allows the legend to be eliminated.
Example of layered bar chart with purposeful use of color.
5. Create pointed titles and call out key points with text
The previous graph could be given a title along the lines of “Soybean Yield by Planting Date, 2008 to 2010.” However, a much more useful title could be leveraged to communicate the key point – in this case, “Planting Soybeans Early Resulted in an Average 2.7 bu/acre Yield Increase.”
Text also can be used in other strategic locations, such as the use of the word “12 On-Farm Research Sites” to designate all the sites along the x-axis rather than labeling them each “site 1, site 2, etc.” A subtitle is used to designate what the different colors of bars represent and provides additional useful information about planting dates.
6. Get feedback and iterate
This process is dynamic and, at least for me, requires lots of trial and error. Utilize the back button. Or create a separate copy before trying a bold remake, which also allows you to compare the first and second versions. On a number of occasions, once I had gotten a graph cleaned up and presentable, I realized my point would be better displayed with a completely different graph type, and I ended up starting the process over again.
Starting with a quick sketch on a sheet of scratch paper can also be helpful. Sometimes you can save time by quickly drawing out some ideas of how to display your variables before beginning your computer work. This also forces you to think through the concept rather than just defaulting to one of Excel’s recommended charts.
Getting feedback can be very valuable. Ask other people to take a look at your graph. Ask them what they think the main point is, and what they notice first. Audiences also often provide great feedback. Take note of what questions your audience have and then determine if there is a way to make your graph more clearly communicate the information they need.
7. Read up and copy other visualizations
Many of the graphics I have experimented with came from examples that intrigued me by the effectiveness with which they communicated information. I encourage you to browse websites and follow Twitter accounts that routinely produce good data visualizations. If you see something that really communicates information well, take a few minutes to look at it and think about why it is effective, then try to incorporate that into your future designs.
This is admittedly a very brief introduction to the concept of data visualization. There are lots of great resources that discuss how to pick the right chart for your data – and even walk you through how to create them. Two of my favorites that are fairly comprehensive are “Storytelling with Data” by Cole Knaflic and “Effective Data Visualization“ by Stephanie Evergreen.
Be patient with yourself – as with most things, learning to create good data visualizations takes time. Scott Berinato, author of “Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations,” sums it up well: “Simplicity takes some discipline and courage to achieve. The impulse is to include everything you know. But charts communicate the idea that you’ve been just that – busy[x].”
These seven suggestions are meant to serve as a starting point and to encourage you to begin experimenting with the way you communicate data. In the next post, I will take you through a data visualization makeover using the elements I outlined in this post.
Please take a minute to answer these three questions. Your feedback helps direct future articles and resources.
Visibility. I have talked about it recently and a few people asked me what it means. After all, Extension is the local face of the University and many of us live, work, and volunteer alongside the people we serve. As you read below, some items are about innovation or presenting our work and data clearly. Others are about connecting or making sense of large amounts of information. But is that all there is to being visible?
For me, it goes beyond marketing and includes the effort to articulate the value of our work from the viewpoint of our stakeholders. Do they see Extension as part of their success or part of their path forward? Can they help others see Extension in that light?
What are your thoughts on visibility and Cooperative Extension? What did I get right or wrong? Share your thoughts by tweeting @eXtension4u and using the #coopext hashtag.
News Roundup
Digital Green eXtension Fellowship Applications Due April 24
Are you interested in Extension approaches that have been tested and proven in other countries? Do you enjoy working with innovative digital technologies? Digital Green and eXtension are partnering to fund a Fellowship project in 2017. The Fellow will work with the 2017 Food Systems Issue Corps to explore ways to apply the Digital Green model to U.S. Extension. Learn more about the fellowship…
Related: Check out a webinar on Digital Green’s work and fellowship announcement.
Coming Soon! The eXtension Impact Collaborative for Food Systems
Hundreds of Extension Professionals have already engaged in the Issue Corps experience with program ideas covering the entire spectrum of Extension. eXtension is looking to recruit more than 200 projects and approximately 700 members for the next iteration, which will be called the Impact Collaborative for Food Systems. Get your ideas ready and watch for the call for applications coming this spring! Learn more about the upcoming call…
Five Innovation Grants Awarded
More than 90 proposals were submitted and five are being funded to conduct cutting-edge Extension work. The selected projects include elements of Makers, data science, citizen science, Internet of Things, drones, and more. The principal investigators represent Ohio State University (2), Colorado State University, Virginia Tech, and the University of New Hampshire. As these projects continue, we will share successes and lessons learned in several ways, including blogs and webinars. Read more about the Innovation Grant projects…
Data Jams for Building Evaluation Capacity
In Extension, we collect a great deal of data on the impact of our work. Much of it goes beyond numbers and sometimes the amount of data can be overwhelming. Learn how Christian Schmieder, University of Wisconsin and eXtension fellow, is using Data Jams to build skills and evaluation capacity in Extension.
It has been estimated that 90% of the world’s data has been created in the past two years alone. How can scientists and educators explain and present data in clear and concise ways amidst all this noise? Laura Thompson, University of Nebraska and eXtension fellow, offers some ideas in “Data Visualization: Why Does it Matter?”
Working Out Loud. John Stepper, author of “Working Out Loud” led a webinar that attracted more than 100 viewers and provided the opportunity to sign up for learning circles with fellow Extension professionals. View the recording… | Sign up for a learning circle…
Building Your Brand. Presenter and Key Informant Sandy Adam discussed how to create and build your professional brand in social networks to increase knowledge about and impact of your work. View the recording…
Webinar recordings and related resources are posted on the link for that webinar in Learn, often within 24 hours after the webinar ends.
“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”
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.
After serving and guiding communities and professional development for the eXtension Foundation for many years, Ashley Griffin has accepted a new role as the Impact Collaborative Program Manager to design the process of the 2017-2018 Impact Collaboratives. Ashley has devoted her career to the work of the Cooperative Extension Service beginning as an Extension Associate coordinating equine youth activities for Kentucky and national programs then later as a Communications Specialist for the University of Kentucky, College of Agriculture, Food and Environment.
Molly is joining eXtension after almost 19 years working for the University of Wisconsin-Extension, Cooperative Extension as an Instructional Design and Technology Specialist. She holds an M.S. In Curriculum and Instruction, concentrating in Educational Communications and Technology from the University of Wisconsin-Madison. She lives in Madison, Wisconsin with her husband, one of their two sons, and two very fat cats.
As a creative designer and food system researcher, Melanie focuses on artistic expression, community leadership, diversity in entrepreneurship, and urban food system growth and development. As a strategic advisor, Melanie has worked on projects that inspire her and that are aligned with creating a better Earth and better communities in a world value system focused on people and planet.
Jennifer Cook will serve as the eXtension Digital Green Fellow and will work to pilot the innovative technology platform of Digital Green with selected Impact Collaborative projects.
