Why You Should Know About Treemaps

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

Active Ingredients (What is a treemap?)

As with so many charts, it’s easier to show you one than to describe it. So here you go:

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

This treemap shows the number of shelter beds used by individuals and families in various years in Chicago. There are two primary or “parent” categories: interim shelter beds and overnight shelter beds. Each of these categories is assigned a rectangle area with subcategory rectangles nested inside of it. In this case, the subcategories are years. The area of each rectangle in a treemap is in proportion to a quantity, in this case number of beds. The area size of the parent category (bed type) is the total of its subcategories (years). The parent categories, in this case, are also distinguished by color: red/orange for interim shelter beds and yellow for overnight shelter beds. Further, darker shades show more beds.

Uses

Treemaps provide a clear view of the structure of your data and allow you to compare the size of parent categories and subcategories. With the example above, we quickly can see that there were many more interim shelter beds than overnight ones. We also see a similar numbers of beds in all years except 2016, when there was a lower number of interim shelter beds.

Warnings

The treemap doesn’t look like a tree or a map, really. So why do we call it that? Well, the treemap shows a hierarchical structure (categories and subcategories) like a tree diagram (aka organizational map). But a treemap doesn’t show the different levels of the hierarchy as clearly as a tree diagram. So if you are trying to focus attention on a hierarchy with several levels, consider a tree diagram instead.

Fun Fact

A “tiling algorithm” (included in data viz programs like Tableau) determines how the rectangles are divided and ordered into sub-rectangles in a treemap. The most common is the "squarified algorithm," which keeps each rectangle as square as possible.

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

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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 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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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.

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.

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.

Bar Chart Hack #7: The Lollipop Chart

The lollipop chart provides a short and sweet ending to the 60-Second Data Tip series, “How to Hack a Bar Chart.”

A lollipop chart is nothing more than thin bars with circles on top. So why go to the trouble? Well, if you have a lot of bars of similar length, you should not go to the trouble. The circles will just make comparing the lengths of the bars more difficult.

But the lollipop chart can be helpful when you have a bunch of bars of varying lengths, and you want to set them apart in a visually interesting way. Also, you can use those circle as labels, as in the example above.

Check out these easy instructions for making lollipop charts in Tableau and Excel.

And, before we leave bar chart hacks altogether, check out this wonderful animated bar chart showing the GDP of various countries over time. Watch China fall and rise! (And thanks to my friend, Harry Gottlieb, for sharing this chart with me.)

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

Icons created by Ben Davis, Dinosoft Labs, and andrewcaliber from Noun Project.

Bar Chart Hack #6: The Funnel Chart

Today we arrive at Episode 6 of the 60-Second Data Tip series, “How to Hack a Bar Chart.” As we have discussed, bar charts are user-friendly and familiar, but familiarity can breed contempt. So this week we consider yet another variation of the bar chart called the funnel chart.

The funnel chart is used to visualize a process and how the amount of something decreases as it progresses from one phase to another.

The example below shows the decreasing number of participants at each stage of a food service training program. We can see that few of those who attend orientation make it all the way to a job. And we can see where there is the most/least drop off. This funnel is also interactive. You can see the funnels for particularly subgroups, such as men and women, by changing the filter at the top to gender. Other options are race/ethnicity and family status.

It looks cool and makes intuitive sense, but a funnel chart is just a bar chart on its side with a mirror image. Check out these easy instructions for making funnel charts in Tableau and Excel.

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

Bar Chart Hack #5: Fine Tuning

Welcome back to the 60-Second Data Tip series, “How to Hack a Bar Chart.” This week we look at some graphical fine-tuning that can transform a traditional bar chart into something that’s more engaging and more informative.

First I’ll show. Then I’ll tell.

Take a look at example A below. Then take a look at example B.

Example A

Example B

Both are bar charts showing the same data. But B wins, hands down. Why?

Chart A truncates the Y-axis making the difference between large and small counties look bigger than it actually is. Chart B, by contrast, fills in the whole bar and darkens the portion not attending school or employed, thus giving us a sense of the size of both groups (those who are in and out of of school and work) in large and small counties.

Chart B points us to the conclusion it wants us to draw with the title and annotations.

Chart B doesn’t have unnecessary and distracting visual elements such as gridlines and axes labels.

Chart B provides images to further emphasize the contrast between large and small counties.

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

Bar Chart Hack #4: Radial Charts

Welcome to Episode 3 of “How to Hack a Bar Chart.” This time we consider two bar chart species that recast the regular bar chart in circular form. They may be eye-catching but be careful how you use them.

Radial Column Chart: (aka Circular Column Graph or Star Graph). As you can see in the example above, the bars on this chart are plotted on a grid of concentric circles, each representing a value on a scale. Usually, the inner circles represent lower values and values increase as you move outward. Sometimes each bar is further divided using color to show subgroups within each category. Because we are better at assessing length along a common scale, this type of chart isn’t ideal if you want viewers to accurately compare the lengths of each bar. However, these charts are great at showing cyclical patterns. Florence Nightingale used this type of chart (which she called a polar area chart) to show a cyclical pattern in the number and causes of death in the Crimean war.

View fullsize

This work is in the public domain in its country of origin and other countries and areas where the copyright term is the author's life plus 70 years or less.

Radial Bar Chart (aka Circular Bar Chart) is simply a bar chart in which the bars curve around a circle, like runners on a circular track. As you may recall, races on circular or oval running tracks include staggered starting lines so that runners on the outer (longer) tracks run the same distance as those on the inner (shorter) tracks. But the bars on a radial chart have the same starting line making it difficult to compare lengths. So skip the radial bar chart. Not worth the effort.

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

Bar Chart Hack #3: The Combo Chart

Welcome to another episode in the 60-Second Data Tip series, “How to Hack a Bar Chart.” As we have discussed, bar charts are user-friendly familiar but like all things familiar, they can be boring and easy-to-ignore. This week we consider—in about 30 seconds— how to combine a bar chart with another type of chart to wake us and engage us.

Consider the two charts below. Both show the same data: fundraising goals vs. actual funds raised. The one on top uses bars for both categories. The bottom one uses bars for the goals and lines for actual amounts.

Which works better? I vote for the bottom one. It makes comparing values between two different categories easier because it uses not only different colors to distinguish them but different “encodings” (bars and lines).  The bottom chart gives us a clear view of when we are exceeding or falling short of our goals in any given month.

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

Bar Chart Hack #2: The Icon Bar Chart

Welcome to Episode 2 of “How to Hack a Bar Chart.” This mini-series shows you how to take something that works well and that folks understand and move it in a more creative and engaging direction. This time, you meet a close cousin of the bar chart, but this cousin is more interesting than its relative. It has icons.

This is what you should NOT do with icons: make them into bars. Here’s why: bar charts are powerful (if boring) because we can easily compare their lengths. When icons or images are used in place of bars, such comparisons are more difficult to make. See the first example below showing how many clients live in different types of homes. It’s quite a challenge to determine how many more clients live in suburban homes vs. high rises. That’s because the height of the icons are difficult to assess.

The second example makes it a little easier. But I’d argue that in both examples 1 and 2, the icons make the viewer’s job (comparing lengths) unnecessarily difficult.

The third example, introduces bars back into the bar chart and thus requires minimal viewer effort.

And the fourth further lightens the load by removing the Y-axis and directly labeling the bars and placing the bars closer together.

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

Image credits: House by ANTON icon from the Noun Project, company by Angriawan Ditya Zulkarnain from the Noun Project, Farm by Ferran Brown from the Noun Project

Bar Chart Hack #1: The Divergent Stacked Bar Chart

Last week I promised to arm you with useful bar chart hacks. The idea is to take something that works well and that folks understand but move it in a more creative and interesting direction.

So this week I give to you: The Divergent Stacked Bar Chart.

Okay, so you know what a bar chart is. And you probably know what a stacked bar chart is, even if you don’t call it that. It uses color to show the subgroups that comprise each bar (or larger group) in the chart like this:

Regular Stacked Bar Chart

Now the cool, or divergent, part. It’s easier to show you than to describe it. So take a look:

Divergent Stacked Bar Chart

As you can see, the the divergent chart aligns each bar around a common midpoint. So it’s much easier to compare, for example, positive and negative values across categories.

Stephanie Evergreen provides directions on making a divergent stacked bar chart in Excel. And here are instructions on creating such a chart in Tableau. Other data viz softwares can make this chart too.

For a much deeper dive into the data viz world’s debate over when and if to use divergent stacked bar charts, check out this article by Daniel Zvinca.

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 Hack A Bar Chart

Choosing a chart type is like making breakfast for your kids. Bar charts are Cheerios. You know they will eat it and it’s healthy. Now come the buts:

But #1:  Cheerios is boring and you wish they had a wider palate.

But #2: If you give them a quinoa breakfast bowl, it will go uneaten and you might as well have given them Cheerios.

When it comes to data visualization, Maarten Lambrechts says don't settle for Cheerios. He calls the problem “xenographobia” or the fear of weird charts. And he implores us to boost our viewers’ “graphicacy” by feeding them the equivalents of quinoa breakfast bowls in the chart world.

Here’s what I think. We should neither spook our children at breakfast time nor our funders, board members, and staff throughout the day. But we should try to slowly widen their palates. One way to do that is to take something they know and love and hack it a bit. Throw some nuts on the Cheerios. Use color in novel ways to enliven a bar chart.

Over the next several weeks, I will offer up different ways to hack a bar chart. Stay tuned!

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 Reconsider The Pie Chart

In honor of the upcoming pie-focused holiday, I am revisiting the pie chart. You’ve heard me (and perhaps countless others) disparage the pie chart. Humans, as you may recall, are not so good at distinguishing between different angles. And pie slices involve angles. We are better at assessing length along a common scale. So it’s much easier to figure out which bar in the bar chart below is longest than it is to determine which pie slice is largest.

Data viz gurus will tell you to only use the pie chart to show part-to-whole relationships. Like this one showing what percentage of all pie lovers love minced meat pie (data source: my imagination).

But I’m going to push back against the data viz orthodoxy a bit. Because I think there are two types of angles we are really good at assessing: the 90-degree angle and the 180-degree angle. Moreover, when we use those angles to divide a circle, we instantly perceive one-quarter and one-half, respectively. Nothing says “a quarter” like a quarter slice of pie. But show me quarter of a tray of brownies? Well, one-fourth is probably not your first thought.

Indeed, we are so good at one-half and one-quarter assessments, that we can immediately detect when a slice slightly misses the mark. 

But it’s more difficult when comparing bars. Can you tell which bar below is divided 75/25 and which is 73/27?

So consider a pie chart (or it’s close cousin the donut chart) when:

• Showing how the size of one or two groups relates to the whole group

• Showing group sizes that equal one-half or one-quarter of the whole

• Showing when a group size is slightly more or less than one-half or one-quarter of the whole

And during Thanksgiving, just consider pie, regardless of the shape and size of the slice.

(I know I promised you more on making data beautiful last week. Stay tuned for that tip in the upcoming weeks.)

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

Photo by Chloe Benko-Prieur on Unsplash

Photo by Alison Marras on Unsplash

Tap Into The Mighty Flowchart

Behold the power of the flowchart. They are engaging, easy to digest, and charmingly analog. Don’t forget to use them to:

• Show how simple or complex a process is.

• Guide your viewer, quickly, to the info they need. That’s the goal of my “Do You Plan To Vote?” chart pictured above. Check out the interactive version here.

• Explain all stages of a process such as how clients move through your job training program or how donors move through the cultivation process.

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

Choose The Right Viz

Data Viz UX, Episode 3

Three weeks ago, I promised to show you how to apply some UX (User Experience) tips to keep your viewers awake, engaged, and wielding data from the data visualizations (aka data viz) you create. So far, I’ve covered the first two steps: knowing your users and choosing the right data. Today, we tackle choosing the right visualization for your data.

I highly recommend Andrew Abela’s simple decision tree called Chart Suggestions—A Thought-Starter.  It’s based on Gene Zelazny's classic work Saying It With Charts. The decision tree starts with the basic question: “What would you like to show?” And provides four options:

Comparison. You have two or more groups of things or people and you want to see which group is largest/smallest or highest/lowest (or somewhere in between) on some measure. You also may want to see how these groups compare on the measure overtime.

Distribution. You have a bunch of data points (e.g. the ages of participants in a program or test scores of students in a class) and you want to know how spread out or bunched up they are. Are most of the ages, test scores (whatever) near the average? Or is there a wide range? Are there some extreme outliers?

Composition. You want to understand who or what makes up a larger group such as how many of the participants in a program are in different age brackets or how many have been in the program for different lengths of time.

Relationship. You want to know if one thing is related to another, either at one point in time or overtime. Does more participation in a mental health program correlate with less distress over time? Do those with lower incomes have higher heart rates?

Once you answer this basic question, the decision tree helps you to choose a specific chart based on the type of data you have. Abela’s chart chooser includes the types of charts you are most likely to select. But there are more rare species out there. To learn more about the wide array of ways to visualize data, check out the Data Visualisation Catalog.

However, I will leave you with a word of caution. And that word is: “Xenographphobia” or fear of weird charts. It’s a thing. And you should be aware of it. Although we might like the look of sexy charts, we don’t usually have the time or patience to figure them out. So in the interest of creating a positive and productive UX, stick with the charts folks already know how to read or are self-explanatory.

Stay tuned for the other steps in the UX process: refining the viz and testing it.

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

Column chart with line chart by HLD, Line Graph by Creative Stall, Pie Chart by frederick allen, Radar Chart by Agus Purwant, and sankey diagram by Rflor (from the Noun Project)

A Simple and Powerful Way To Extract Meaning From Your Data

Here’s an easy, quick, and powerful way to visualize your data. It leads to significant insights in seconds. 

You have probably seen quadrants graphs. People, programs, or projects are graphed along two measures, one on the Y-axis and one on the X-axis. The graph is divided into four quadrants based on the average or midpoint (or some other meaningful dividing point) of the two measures. That makes it sound more complicated than it really is. Check out the quadrants graph above.  Each circle is a participant in a tutoring program. The measures are: grade point average (Y-axes) and attendance in weekly tutoring (X-axis). So participants in the:

Top right quadrant are above average on both their tutoring attendance and their GPA.

Top left quadrant are above average on GPA but below average on tutoring attendance.

Bottom right quadrant are below average on GPA but above average on tutoring attendance.

Bottom left quadrant are below average on both GPA and tutoring attendance.

Well, if the tutoring program is designed to boost GPA, then you’d hope to see most of the participants in the top right quadrant. Or you’d at least want to see participants who are low on attendance also low on GPA. But if there are participants in the other quadrants, we need to figure out why these particular students defy our predictions. For example, what else in going on with participants with high attendance/low GPA that might be undermining their progress?

Other measure pairs of interest to many nonprofits might include:

Value/Action: How do staff members who value a certain program or curriculum actually perform in putting that program or curriculum into action? If not well, why not? (Survey data would be needed here.)

Cultivation/Donation Level: Are the donors you are cultivating the same ones making the largest donations to your organization? If not, why not?

Cost/Funds Raised: Did the highest cost fundraising events result in the most funds raised? If not, why not?

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 Lineup

Eyes beat memory according to Tamara Munzner. Her idea (which she shares with others, including myself) is simple. It’s easier to compare two things you can see at the same time than to compare something you can see to something you can only remember.

When several small visualizations are placed side by side (called “small multiples”), you can see the power of eyes over memory. Take a few seconds to check out this great small multiples viz by Doug McCune. You can quickly scan the images to make easy comparisons.

In each chart, the X-axis shows time of day, and the Y-axis shows number of crimes. Daytime crimes are displayed with yellow bars in the top half of the chart. Night-time crimes with blue bars on the bottom.

It’s easy to see that driving under the influence and drunkenness occur more often during the night and trespassing and suicide occur more often during the day. It would be much harder to draw this conclusion flipping through pages or clicking through screens.

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