Why You Should Know About Bubble Charts

This is the second in a series of tips on different chart types. The idea is to fill up your toolbox with a variety charts for making sense of data. This week, I give you the bubble chart.

Active Ingredients (What is a bubble chart?)

A bubble chart is really just a souped-up scatterplot. Like the scatterplot, it’s a graph with plotted points that show the relationship between two sets of data. Here’s a scatter plot showing the relationship between the height and girth of black cherry trees:

We can see that there is a relationship between height and girth. As trees get taller, they also tend to get wider. The scatterplot becomes a bubble chart when we size the points according to another measure, in this case the volume of the trees.

Black cherry tree data: https://vincentarelbundock.github.io/Rdatasets/datasets.html

Now we can see that as height and girth increase, the volume of black cherry trees also tends to increase. Sometimes folks add another measure or dimension to bubble charts using color, such as in this example.

Source: Smartphone Sales Growth Worldwide | Mekko Graphics

Uses

Use a bubble chart when you want to show the relationship between two measures plus a bit more. In the bubble chart above, we can see that as the cost of smartphones (position on X-axis) increased, the growth in sales (position on Y-axis) decreased AND that sales were particularly high in China, Emerging Asia, and North America in 2017 (size of bubbles) AND that the boom markets with cheap phones were mainly emerging markets (color of bubbles). That’s a lot of information in a fairly small space.

Warnings

When you cram too much information into bubble charts, viewers struggle to see core relationships and trends. So don’t use too many data points, too many sizes, or too many colors. Scroll down to the end of this tip to see a bubble chart that confuses more than elucidates.

Put your most important measures on the X and Y axes. Remember that humans are really good at discerning position along a common scale. So viewers are most likely to comprehend the relationship between the X and Y measures first.

Show your less important measures or dimensions with size and color. Humans can only make general comparisons when it comes to size and color. We are hard pressed to say if one shade is twice as dark as another or if one circle is three times the size of another.

To see past data tips, including those about other chart types, click HERE.

There’s too much information in this bubble chart!

Source: European Beer Consumption | Mekko Graphics

I found most of these bubble charts on Grafiti.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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Why You Should Know About Heat Maps

My 2020 gift to you? A quick and dirty introduction to a bunch of different chart types. Over the next several weeks, each 60-second data tip will introduce (or re-introduce) you to a particular chart type. I’ll give you need-to-know information in a format akin to the “Drug Facts” on the back of medication boxes: active ingredients (what the chart is), uses (when to use it), and warnings (what to look out for when creating the chart). I’ll also add some fun facts along the way. The idea is to fill up your toolbox with a variety of tools for making sense of data. We begin with the heat map.

Active Ingredients (What is a heat map?)

A heat map is a chart that uses variations in color to show differences among categories (e.g. people living in different zip codes) or differences across a scale (e.g. people with different income levels). In a lot of cases, it’s simply a table with color added to the cells.

Uses

Consider adding color to a table to quickly see patterns. Tables have a least one advantage over charts. They cram a lot of data onto a single screen or page. But it’s hard to see patterns when looking at a regular table on a spreadsheet. Take a look (but only for a few seconds) at this table showing the number of shelter beds used by individuals and families each month in Chicago.

Source: https://catalog.data.gov/dataset/performance-metrics-family-support-services-homeless-shelter-system-monthly-utilization-8d7ff/resource/3f5b8d3d-bafe-4dfd-87ae-54f4797d1e46

Are any patterns jumping out at you? Now take a few more seconds to look at this version, which uses color instead of numbers — aka a heat map:

Are patterns more apparent now? In a few seconds, this is what I saw:

• More variation by year than by month.

• Shelter bed usage was particularly high in 2015.

• Less seasonal variation than I’d expect. I expected darker colors during the winter months.

With a little more time, more patterns might emerge. And more questions too. This heat map shows number, rather than percentage, of beds used. So, perhaps, more beds were used in 2015 because the number of beds available increased. After more examination and exploration, you might decide to use another chart, which zooms in on a subset of the data. But the heat map is a great first step to understanding data.

Warnings

When creating heat maps, you will use discreet colors to show differences among different categories and a color scale (light to dark) to show differences among different levels, from low to high values. Sometimes folks use the stoplight color system (red, yellow, and green) to show the categories: good, okay, and bad. For example, fundraising amounts over a certain number might be considered good. The problem with this approach is that it doesn’t work for people with red-green color-blindness. If you want to draw attention to good or bad amounts, it’s better to just highlight the good or bad numbers with one color and not color the others.

Color provides only a general understanding of differences in data. To provide a more specific understanding you may want to add numbers, as well as color, to cells as in the chart below. And, in general, don’t use too many colors in your heat map palette. It will be easier to read if you keep it simple.

Fun Fact

Heat maps are thought to have originated in the 19th century. Loua created this chart in 1873 to show the characteristics of 20 districts in Paris.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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Invisible Assumptions Driving Your Organization

People and organizations have ideas about what leads to what. We are aware of some of these ideas. But others are so ingrained that we mistake them for facts of life.

Psychologists call the visible ideas explicit theories and the invisible ones implicit theories. Both explicit and implicit theories affect how we perceive and act in the world. If, for example, we believe—either explicitly or implicitly--that hard work leads to success, we are more likely to perceive evidence that supports our theory (aka confirmation bias) and to work hard ourselves and encourage it in our offspring, clients, and employees.

If this idea is explicit, then we are more likely to examine it, compare it to the ideas of others, and even test it. However, if it’s implicit, then it will probably never occur to us to examine it because we are not fully aware that we believe it or that things could be any other way. (For more on implicit and explicit theories, check out this.)

Implicit theories also affect what data we gather and use. If we have an implicit hard work theory, we might gather data to assess what type of work or effort is most likely to lead to success but, unless we make the implicit theory explicit, we are not likely to collect data to see if clients who put in more training hours actually had more success.

Every organization has implicit theories. Do any of these seem familiar?

• Meetings make people feel included/empowered.

• With more money, we could be more effective.

• Special events help to cultivate new donors.

Some of these assumptions might be true, at least under certain circumstances. But no matter how data-driven we are, we are not going to collect data to test ideas we are not fully aware of.

Staff meetings are great places to listen for implicit theories. Next time your colleagues and you are discussing an issue, see if you can detect a few of them. What assumptions lie behind your assessments, decisions, and actions?

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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Photo by PoL Úbeda Hervàs flickr.com/photos/polubeda/

Top 3 Things You Should Know About Rankings (And How To Visualize Them)

1. We love lists, listicles, and rankings in particular, because they make assessment easier. We hate information overload. Ranked lists show us what information is most important and so ease decision-making.

2. We are more likely to remember items at the top and bottom of the lists and forget the items in the middle. So shorter lists are easier to retain.

3. People also BELIEVE shorter lists more than longer ones.

 3 Ways to Visualize Rankings

1. Bar Charts (like the one above)

2. Rank Charts (which are good for showing how ranking changes over time)

3. Stacked Bar Charts (scroll down to see the mother of all stacked bar charts showing the top 100 colleges by diversity)

For more on the power of rankings, check out this podcast from  the Kellogg School Of Management At Northwestern University. And here is the full version of the viz shown above:

You can find the ARTICLE in which this viz appeared on the World Economic Forum website.

I found this chart on Grafiti.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Squeeze MUCH More Information From Your Surveys (Repost)

Surveys are such a common data source for nonprofits. And I get so many questions about how to make better use of survey data, that I’m reposting this tip which originally appeared in June 2018.

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Surveys provide answers to many nonprofit questions.  How do participants like this program? What are barriers to enrollment? What types of services do community members lack and need?

It’s easy enough to create a survey on Survey Monkey or the like. It's harder to get an adequate number of responses.  And even when you do, the respondents might not fairly represent the larger group you want to know about. But let’s say you get past this hurdle. There’s still a major hurdle ahead of you: extracting meaning from your data.

Surveys include different types of questions. Perhaps the most common one is the Likert scale question. You have seen them a million times. Respondents are asked to indicate how much they agree or disagree with a particular statement using a five to seven point scale.

Let’s say you want to know participants’ feelings about a program. Your Likert scale statements might be; “I feel that I can ask the instructor for help when I’m confused” or “I feel comfortable interacting with the other participants in the program.” Survey Monkey will give you each respondent’s rating of each statement and will also give you the average rating. What meaning can you extract from these numbers?

Many organizations will use just the averages to determine where they are doing well and where they need to worker harder or differently. But there is so much more information in those numbers than averages can tell you, including:

The extremes: Averages can’t tell you what were the lowest or highest ratings on any given statement.

What most respondents said: Averages also can’t tell you if the average is three because most people responded with a “3” or because half responded with a 5 and half responded with a 1.

What subgroups think and feel: Even though the overall average might be high, the average might be low for some subgroups within your group of respondents. Perhaps respondents from a certain neighborhood, for example, had very different opinions than the group overall.

You can extract and show this information using data visualization tools like Tableau that allow you to interact with your data. The viz below shows the range of responses to each survey statement and the proportion of responses for each rating. Moreover, the interactive version allows you to “drill down” into the data and see how the results change overtime. We could also construct the visualize to show us results for different subgroups.

If you are going to go to the trouble of conducting a survey, make sure to squeeze all of the information you can from the data you collect.

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Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

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See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.

When to (and NOT to) Use a Map

Maps can be a powerful way to show your data. But not always. Maps work best when . . .

1) Your audience already knows the geography.

Most Americans have a basic understanding of the size, demographics, land use, weather, and history of different regions of the U.S. It’s that foundational knowledge that makes maps like the following so effective. We think: wow, cows would take up all of the midwest if we put them all together, and urban housing would require only a portion of New England. Or, if only white men voted, just a few states in New England and the Northwest would go Democratic.

Source: Bloomberg

Source: Brilliant Maps

But when we are not familiar with the geography, maps are much less illuminating. For example, if you don’t know Ireland well, then this map does not shed much more light on the matter than the simple bar chart in the upper left hand corner. It tells us which clans are most prevalent, which is all the map also shows us unless we know more about the different regions.

Source: Brilliant Maps

2. You are showing the significance of proximity or distance.

Even if your audience is not familiar with the geography (and sometimes especially when they are not familiar with it), maps can be an effective way to show proximity or distance. This map of the Eastern Congo shows us how close armed groups (in green) are to internally displaced people (in purple). Just naming the cities or regions where these two groups are would not be effective for audience unfamiliar with the geography.

Source: Brilliant Maps

I found all of these maps on Grafiti.

Data Viz for Nonprofits helps organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

How Data Viz Can Save Your Thanksgiving

Your next data challenge may involve turkey. And I’m here to help. This week we take a break from nonprofit data and consider Thanksgiving data. If you are in charge this year, and you have a medium to small oven and fridge, you have to be strategic. When should you cook, chill, and reheat each dish to make the most of your time and oven/fridge space?

I give you my color-coded gantt chart. I used it last year, and it worked like a charm. I took all my recipe data and came up with this chart to make sure I had my timing right. Made it in good old Excel. Nothing fancy, but it did the trick. Feel free to adapt it to your recipes or perhaps your next fundraising event!

Happy Turkey/Tofurky Day.

Data Viz for Nonprofits helps organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

Embrace Redundancy

Redundancy gets a bad rap. It’s almost always mentioned as something to avoid. Graphic novelist Nate Powell cautions. “As a visual storyteller, a lot is learning what to include so you're not being redundant between images and text.”

But some research by Michelle Borkin and colleagues points in a different direction. They found that “redundancy was helpful in improving visualization recall and in improving viewers’ understanding of the data being presented. This was true whether it was data redundancy (like labeling your bar chart values, or dual-encoding data) or message redundancy (including the same message in a title, an annotation, a label, a pictogram, etc.)” (Quote from: “How to get people to remember your visualization” by Mike Cisneros and Lilach Manheim.)

Other psychological research also suggests that redundancy can be a friend to memory. (See this research, for example.) And it makes intuitive sense. Perhaps redundancy is good for the same reason you want it on the space shuttle: if one component doesn’t work, it’s good to have a back up. Perhaps it’s just the power of repetition. In either case, it’s a good way to make sure your primary message gets through.

Check out the example below. The main message is that there are a lot of vaccines in development. This is communicated by the title, the number of circles, and the data labels.

Source: https://beta.grafiti.io/facts/1048338

And for more on how to capture attention with data visualizations, check out this data tip and this one and this one too!

Data Viz for Nonprofits helps organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

The Most Important Element of Any Chart

After you have chosen a chart type and labored to make it clear and engaging, you still haven’t done the most important part. You haven’t given it a title. And it turns out that “YOUR TITLE IS SUPER IMPORTANT. It’s the thing people look at for the longest, the thing they build the strongest association with, and the thing they’re most likely to remember” according to data visualization research described in “How to get people to remember your visualization” by Mike Cisneros and Lilach Manheim.

So don’t give the title short shrift. Consider what your viewers/readers might want to know. What would capture their attention? What would make them want to learn more? Below are some visualizations posted on Grafiti. The “before” images show their original titles. The “after” images show my new and improved titles. See what you think.

And for more on how to capture the attention with data visualizations, check out this data tip and this one too.

Data Viz for Nonprofits help organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

Add This To Make Your Charts Memorable

Look at the two images below.  Which catches your attention? Which do you think you are more likely to remember?

Edward Tufte and other data viz gurus have warned us against “chartjunk” which is anything that is not necessary to comprehend the information represented on a chart, map, or graph. The idea is to get rid of distractions and focus attention on the data.

But some research by Michelle Borkin and colleagues points in a different direction.  In an experiment, they showed participants charts with and without various elements that might be construed as chartjunk like photos and drawings. They found that such images not only did not hinder memory or understanding of visualizations, they appeared to serve as “visual hooks into memory.”  Such visual hooks are important because what we perceived is based, in part, on what we expect to perceive due to memories of past experiences.

The idea of not loading up a chart with a lot of junk competing for attention is still a good one. But some images, if they are closely related to the data and connect with what folks already know, can help viewers to focus on and absorb information.

And for more on how to capture the attention with data visualizations, check out this data tip.

Data Viz for Nonprofits helps organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

 M. A. Borkin et al., "Beyond Memorability: Visualization Recognition and Recall," in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 519-528, 31 Jan. 2016.

 P. Kok et al., “Associative Prediction of Visual Shape in the Hippocampus,” in Journal of Neuroscience 1 August 2018, 38 (31) 6888-6899.

 Photo by Nery Zarate on Unsplash

Data TMI? It's A Thing

TMI* is a problem in many realms. It has become a parenting truism to only answer the question asked when our kids ask about sex. “Don't tell the kid every single thing you know about a topic; keep it pretty simple and let them ask you for more detail if they need it,” says  Dr. Carol Queen, a sexologist.

I think the same principle applies to data dashboards. Those of us who create dashboards have a tendency to add too many charts, too many filters, too many measures, too many dimensions. The idea is to anticipate almost any question the user might have and make it answerable with the dashboard. But usually, that’s TMI. We overwhelm the user. It’s not clear why or how to use the dashboard and so it’s not used at all.

So listen to the sexologist. When designing dashboards, focus each one on just a few questions that your intended users have. Then beta test them with a few of those users. If they want more detail, they will ask for it.

Data Viz for Nonprofits help organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

* too much information

Details, Details (And When To Include Them)

What I remember most about the movie “Inside Out” is a scene about forgetting. And it has helped to shape my thoughts on presenting data.

In the 2015 Pixar film, memories are shining orbs sent through vacuum tubes to “Long Term,” a mammoth storage room with, nevertheless, limited capacity. So “Mind Workers” continuously cull the memory orbs, discarding the unnecessary ones – old phone numbers, piano lessons, names of past presidents – into the “Memory Dump.” I remembered this scene most recently when:

1) I heard Bryan Caplan interviewed on NPR. He’s an economist who wrote the thought-provoking book The Case Against Education. One argument Caplan makes against education is that we mostly forget it.  He cites studies that show little retention of both facts and generalized skills post college.

 2) My 12- and 14-year-old daughters’ shared with me YouTube channels like Oversimplified, In A Nutshell, and Crash Course. These funny, brief videos explain stuff I’ve forgotten (or perhaps never learned, who knows?) like the origins of the French Revolution and how the immune system works.

So here’s the question: Given that our brains are continuously purging information, particularly details, and retaining, at best, big picture stuff that can be contained in a 10-minute video, should we not bother with the details in the first place? My short answer is no. For me, it’s about who should spend time on the details and when.

If you are presenting data in any form, it’s incumbent upon you to know the details of the data -- what the trends are overall and by subgroup, who or what is not represented in the data, where the outliers are. And then the idea is to transfer aspects of this knowledge in the right form for the right people, paying as much attention to what you exclude as to what you include.

Some of the people will only need the biggest picture, but even they should to be tipped off to any exceptions to the rule hidden in the data. They also need to know where to go to learn more if and when they want to. Some of the people will need a more detailed rendering of the data, but don’t give them so many trees that they can’t see the forest. Indeed, they may retain the details more if you give them a general picture first which serves as a scaffolding on which they can attach details presented later.

And here’s hoping you retain the gist of this data tip!

Data Viz for Nonprofits help organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

Freedom From Pivoting

Often, perhaps most of the time, we do best by looking at our data from a number of different vantage points. Sure, we want the broad view: how are all of our participants doing in the program? But we also want to know how particular types of participants are doing such as those in certain age brackets, those who got a certain set of services, those who persisted in program for longer or shorter periods of time, etc. To see our data broken down into such subgroups can be a pain in programs like Excel*. You have to generate a different pivot table for each type of breakdown. But in programs like Tableau, you can create “parameters” that allow you to toggle among different breakdowns easily and painlessly. With some Tableau know-how, you can see how different types of survey respondents responded to different types of question as in the example below.

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Data Viz for Nonprofits helps organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

*If you have a version of Excel with the “slicer” feature, you can have more flexibility in looking at your data from different angles.

Plug Your Logic Model Into Real-Time Data

Want to see this rather boring logic model come to life?

A logic model (aka causal chain, model of change, roadmap, or theory of change) is a type of flow chart showing how an intervention or program is supposed to work. It tells a story about how one thing leads to another. It’s a great way to plan for solving a problem. But logic models are hypothetical, best case scenarios. And, well, reality can bite.

Another problem with logic models is that they get more play during the planning and proposal-writing phase of a project than during implementation. During the daily work of a project, logic models are taking it easy, gathering dust in files and on servers.

But what if we could plug a logic model into the real world? What if we could see how our plan is playing out in reality and make adjustments along the way?

You can do just that with data viz software like Tableau. The current that animates such “living logic models” is real-time data. A living logic model compares theory to reality by showing progress to date. It also allows you to track the progress of subgroups and individuals. So it helps you to plan, to ask the right questions, and to make mid-course corrections.

A living logic model is more understandable and tangible than a traditional one. The user can scroll over any component in the model to learn more about it. Such descriptions can include photos and web links for interested users.

A living logic model shows progress to date. Color saturation indicates the status of each component. And the user can click on any component to see what subgroups might be driving progress, stagnation, or regression.

Play around with this living logic model for a tutoring program to get an idea of its potential for your organization. It’s best viewed in full screen mode. If you’d rather not learn Tableau to make one yourself, I’d be happy to create one for you. Just shoot me an email at amelia@nonprofitviz.com.

Data Viz for Nonprofits help organizations to effectively and beautifully present their data on websites, reports, slide decks, interactive data dashboards and more. Click HERE to learn more about our services and HERE to set up a meeting to discuss how we can meet your particular needs.

How To Make Your Data Riveting

Attention is like a bouncer at the entrance to our brains. For anything to get inside and make a difference in how we think and act, it first has to win our attention. And I don’t have to tell you (although here I go anyway) that we each have limited attention and lots of things are competing for it. So if you aim to influence others’ thoughts and actions with data, give some consideration to the nature of attention.

What wins people’s attention? 1) Stuff that stands out and 2) Stuff related to our desires or goals. The former wins our “exogenous” attention. The latter wins our “endogenous” attention.

Say you are at a crowded cocktail party. You are going to notice stuff that stands out like loud noises or bright lights. But you will also notice stuff that does not stand out but is of particular interest to you such as that woman standing in far corner whom you were hoping to see. You may also notice if one of your favorite songs is playing softly in the background.

We can make use of this understanding of attention when we visualize data by:

• Making the most important aspects stand out.  Vary the size, color, and space around text and data points. For example, make the title much larger than the rest of the text or color all of the data points gray except for the ones you want to call attention to.

• Pointing to aspects that may interest your intended viewers. Use titles, subtitles, data labels and captions to highlight and explain aspects of the data that may be particularly engaging for your intended audience.

Check out the before-and-after vizes below to see how I’ve applied these techniques to focus my audience’s attention.

BEFORE

AFTER

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.

Wait, What? Numbers That Bewilder

Numbers can bewilder our hunter-gatherer brains. For more than 95 percent of human history, folks were not processing written numbers or words. But they were processing visual information in the form of color, shape, and size. It’s not surprising that our brains, evolved over many thousands of years, are better at understanding data in visual form than in word and number form. So when numbers confuse, try “translating” them to the visual.

Here’s a great example of a number that makes me scratch my head: “54% more students with monitors improved attendance than students without monitors.” The statement relates to a fictional program that (like some non-fictional programs) pairs students with monitors to boost their attendance. At first blush, to me, that sounds pretty impressive. It sounds like this: if 10% of the students without monitors improved their attendance, then 64% (10% + 54%) with monitors improved their attendance. Or, put another way, six times as many kids with monitors improved their attendance as kids without monitors.

But my brain just made a wrong turn. That 54% is showing what statisticians call “relative difference.” And the problem with this type of stat is that indicators with low values have a tendency to produce large relative differences even when the “absolute difference” is small.

Okay, still bewildered? No worries, I give you now a picture for your primitive brain. Let’s say, in our fictional program, there are 10 students per class. In one class, all of the kids got paired with monitors. In the other class, none of the kids did. The picture below shows how many kids in each class improved their attendance.

So the difference (aka “absolute difference”) is 1.4 (4.0-2.6) which means that 1.4 more kids in the class with monitors improved their attendance. How did that measly 1.4 become 54%? Well, relative difference is calculated as the absolute difference divided by the “standard” which, in this case, is the class without monitors. So 4.0 minus 2.6 divided by 2.6 or .54, which when expressed as a percentage is 54%.

If relative difference requires varsity level processing for many of us, then percentages are junior varsity. So if I were visualizing the difference between the two groups, I would stay away from both and use an icon chart, like the one above. I might make it even more concrete by showing 25 person icons in each group since the typical elementary school classroom has 25 students. I would then use color to show that 6.5 students out of 25 without monitors had improved attendance and 10 students out of 25 with monitors had improved attendance. So, if you bring the program to a typical classroom, you might expect it to improve the attendance of an additional 3 to 4 kids.

Bottom line? Numbers can be like road signs pointing us in the wrong direction. To move folks in the right direction, make your message concrete and visible.

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.

Avoid This Danger When Choosing Metrics

I’m all about making data clear and easy-to-digest. But there is a danger in it. The clarity may cause you accept what the data seems to tell you. You may not linger. You may not reflect.

Writer Margaret J. Wheatley warns us that “without reflection, we go blindly on our way, creating more unintended consequences, and failing to achieve anything useful.”

Economist Charles Goodhart recognized this danger in the metrics we create to measure our progress. At first, a certain metric may seem like a good indicator of progress. If we want kids in an after-school track program to increase their endurance, we might measure how far they run at the beginning of the program and then again at the end.  Makes sense, right? We might then try to motivate students by offering them free running shorts if they increase their miles by a certain amount. But, that’s when students might start gaming the system. They can increase their miles not only by training hard and running farther over time but also by running very short distances at the start. This is the kind of unintended consequence that Goodhart warned us about. His law states: “When a measure becomes a target, it ceases to be a good measure.” 

The solution? First, reflection. Consider the potential unintended consequences of each of your metrics, particularly those tied to incentives. Second, use multiple metrics to provide a more balanced understanding of progress.  In our running example, in addition to the change in miles participants run, you might also measure resting heart rates at the beginning and end of the program, knowing that a lower resting heart rate generally indicates a higher level of cardiovascular fitness.

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data. 

How To Continuously Update Your Outdated Annual Report

What were you doing on this date last year? If you don’t remember, and if it seems like a long time ago, then reconsider your annual report. Showing your donors, board members, and other stakeholders what you were doing a year ago (or even more, depending on how long it takes to produce your annual report) is not always the best strategy. What if you could describe to them – not only in words but also numbers – what is happening right now? Programs like Tableau make this possible. You can create an online multi-page report, complete with photos, illustrations, and interactive charts. But the numbers on those charts will show what is happening now rather than a year ago. And, of course, you also can show trends over time. Even the free version of Tableau (called Tableau Public) allows you to connect charts to real-time data. The Tableau Foundation has its own “living” rather than “annual” report. To take a look, click here and scroll down!

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data. 

The Ideal Text to Image Ratio

“Okay, I get it. I should be showing and telling because we all process visual information (color, size, length) much faster than information encoded in words and numbers. But what is the best ratio of show to tell?”

A participant in one of my data viz workshops posed this question to me, in a more polite way. And here is my answer. Like so many answers, it begins with “it depends.” There is no exact formula for the best ratio of text to images (including data visualizations or other types of images like photos.) I think it depends on the medium — report, presentation, webpage — and the audience — general, already engaged, expert.

At one end of the spectrum, where you are sharing information with highly engaged experts, particularly in print form, I think you should include more text so that you ensure a precise and careful communication that includes a lot of specifics. In this case, the split might be 30/70 in favor of text.

At the other end of the spectrum are communications to a general audience that is not necessarily familiar with your subject matter. Then I would go for at least a 50/50 split. Also, the images should be easy to understand, and the overall amount of information, whether in text or images, should be limited to the key points.

In the middle of the spectrum, you might have a somewhat informed audience or a diverse audience including experts and novices. Here I would offer a smorgasbord with plenty of sign posts. I know I’m mixing my metaphors, but stick with me. The idea is to make your report or webpage accessible to the casual reader, who is going to peruse the titles, subtitles, pull quotes, images, and charts. These elements of the smorgasbord, together, should tell a general story without the aid of the text. Charts, in particular, should have sufficient titles and labels so that someone can consume them without reading the surrounding text. And some of these elements — titles, subtitles, and pull quotes— also act as sign posts, directing the reader to the content of greatest interest. Indeed, if done well, the sign posts can turn a casual reader into an engaged one.

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data. 

Data Viz Don'ts

This week’s 60-second tip is to head on over to Sarah Leo’s article on Medium the next time you have 8 minutes to spare and read about mistakes she and others at The Economist have made when visualizing data. It’s called “Mistakes, we’ve made a few: learning from our errors in data visualization.”

First, it will make you feel better about your own mistakes. Even the pros get it wrong sometimes.

Second, it will test your data viz prowess. If you have been digesting my bite-sized morsels of data viz wisdom on a regular basis, then use this article as a little quiz. Take a look at Leo’s “before” and “after” vizes and figure out why the “after” version is better.

Third, it may give you some new ideas on how to visualize your own data!

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data. 

Error by Roselin Christina.S from the Noun Project