Data Viz for Fundraising (Part 2)

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If we show data in engaging visual formats, we can conquer the primary challenges of fundraising: 1) making the case for a grant or donation (see tip here), and 2) strategizing and planning fundraising activities, the topic of this tip. Here are a few ways to boost your fundraising strategies with visualizations:

Identifying whom to cultivate: According to Andrea John-Smith of Scout Finch Consulting, we need to know four basic things about our donors: recency (which isn’t really a word, but I’ll give Andrea a pass because it really should be), consistency, frequency, and level of giving. This information will “point you to people you are probably neglecting who are jumping up and down screaming ‘I love your mission.’” A simple bar chart will help you keep track of the most recent, consistent, frequent, and generous donors.

Setting goals: A bar chart with goal lines (for individual donors, groups of donors, or certain campaigns) shows you, quickly, where you are in relation to where you want to be.

Understanding relationships among donors: You can do this with free online network diagram tools or just with a paper and pen. Create circles for each current and potential donor on your list. Use different colors to distinguish between these two types of donors. Now draw lines to show relationships among them. Such a diagram, like all data visualizations, will tell you both what you do know and what you should know. See a circle without connections? Maybe you can increase your list of prospects by researching this donor’s connections. See a donor with many connections? Consider how you might better use this donor in your fundraising efforts.

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 Prince Akachi and  mauro paillex on Unsplash

Data Viz for Fundraising (Part 1)

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By showing data in engaging visual formats, we can conquer the primary challenges of fundraising. These challenges fall into two categories: 1) making the case for a grant or donation, and 2) strategizing and planning fundraising activities. This week’s data tip is about making the case for new or continued funding with data visualizations.

Through maps, charts, and graphs, you can SHOW − rather than tell − donors and funders that your programs and services are:

NEEDED. You can show how the problem your organization addresses has increased over time, what its prevalence is geographically, the percent of a given population it affects, and the percent of the problem related to various causes.

EFFECTIVE. You can show your organization’s increasing impact over time, the percent benefitting from a program, and the geographic spread of programs related to a measure of need such as income.

EFFICIENT. You can show the percent of funds used on administration vs. programs, your return on investment, and the ratio of fundraising investment to return.

DISTINCTIVE. You can show change over time compared to the field in general or compared to a particular competitor or the paucity of similar programs or services in your geographic area.

Stay tuned! Next week’s data tip is about using data viz to strategize and plan fundraising activities.

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 #7: The Lollipop Chart

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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 bar 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

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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.

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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. 

Bar Chart Hack #5: Fine Tuning

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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.

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Example A

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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

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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.

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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.

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.


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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

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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. 

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Bar Chart Hack #2: The Icon Bar Chart

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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.

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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.

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

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

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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. 

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

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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:

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Regular Stacked Bar Chart

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

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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

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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.

A Simple Approach to Using Your Data

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When you come across discussions of data analysis and evaluation, you might think: ah yes, a worthy pursuit in a perfect world. And, in the wake of this thought, rushes another one: these are complex and technical tasks that my organization has neither time, funds, nor expertise to pursue. And so the thought dissipates, and you return to the tasks at the top of your to-do list.

These 60-Second Data Tips are about demystifying data analysis so that you can evaluate and improve your work easily and regularly. We’ve talked a lot about how to transform data into images (aka data visualizations) to make your data more digestible and useful. The best data visualizations are like mirrors that you can pass by each day to get a quick picture of how you’re looking.

In a nutshell, the purpose of collecting and visualizing data is to address this question: how are we doing? And the first step is to figure out what you mean by “we” and “doing.”

“We” can be all of your participants, visitors, funders, etc. But you should also look at subgroups of these groups, for example those in certain age ranges or those who have been in the program for different lengths of time.

“Doing” can be a measure of any input (e.g. funding or training), any interim outcome (e.g. attendance or survey scores), or any long-term outcome (e.g. employment rates, college attendance, or housing provided.)

And to understand how well you are doing, compare your work to something else: some type of standard or goal, other organizations in your field, or your past performance. A simple line chart showing change over time on a given measure will help you to compare your current performance to the past. A reference line showing a goal will help you to compare your performance to a standard. And, if you can get data from other organizations, you can plot their trends alongside your organization’s.

Answering the question “how are we doing?” from a number of different angles will give you a clear picture and will help you to focus on where change is needed and where to stay the course. Pretty simple.

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 Jeremy Bishop on Unsplash

Head Versus Gut

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Data is head food. But we often make decisions with our gut. And the gut is fueled by emotions.

It’s hard to defy our gut feelings when deciding. That’s because emotions often drive behavior. Evolutionary psychologists believe that emotions evolved to get us to do what has to be done to survive: to avoid predators, secure nutrients, resist infection, mate . . . you get the idea. So when your head says A, but your gut says B, you might feel an urgent need to do B because your survival button has been pushed.

I’m going to venture to guess that most of your decisions, like mine, are not directly related to survival. And when that’s the case, our emotions can lead us astray. In a 2016 article in the Atlantic, Olga Khazan summarizes research which suggests just how far astray. For example, anger may cause us to be trigger-happy and simplify our thinking. Happiness may lead us to make shallow assessments based on looks and likability. And depression may induce dwelling on particular issues.

It’s important to recognize our emotions and what they are telling us, rather than blindly following them. Sure, we can form hypotheses about gut feelings and see if the data support them. But when faced with a pressing decision in the real world, we often have a strong emotion and little data at hand. In these situations, we can ask ourselves: Is there a way to bring some more data to the problem quickly? Perhaps it’s data collected by other people for other purposes. We can look for statistics about organizations similar to ours or about issues that our organization addresses. Perhaps it's a quick and dirty anonymous survey of our staff, asking for their knowledge and opinions relevant to the decision. The idea is to feed the head with some data and, in the process, temper the pleadings of the gut.

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 Allure and Danger of Data Stories

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“Data” and “storytelling” are an item. You see them together all the time lately.

When I first came across the term “data storytelling,” it instantly appealed to me. “Data” suggests credibility, information that has some objective basis. But data, to many of us, is boring. Its meaning is often uncertain or unclear. Or, even worse, it’s both. “Storytelling,” by contrast, suggests clarity, a plot with both excitement and resolution. So, by coupling these two words, we seem to get the best of both worlds. Data lend credibility to stories. Stories lend excitement and clarity to data.

Indeed, that’s the point of data storytelling. As Brent Dykes, a data storytelling evangelist of sorts, noted in a 2016 Forbes article, “Much of the current hiring emphasis has centered on the data preparation and analysis skills—not the ‘last mile’ skills that help convert insights into actions.” That’s where data storytelling comes in, using a combination of narrative, images, and data to make things “clear.”

But let’s step back just a minute. Why are we so drawn to stories? According to Yuval Harari, author of Sapiens: A Brief History of Humankind, the answer is:  survival. Harari maintains that humans require social cooperation to survive and reproduce. And, he suggests that to maintain large social groups (think cities and nations), humans developed stories or “shared myths” such as religions and corporations and legal systems. Shared myths have no basis in objective reality. Reality includes animals, rivers, trees, stuff you can see, hear, and touch. Rather, stories are an imagined reality that governs how we behave. The U.S. Declaration of Independence states: “We hold these truths to be self-evident: that all men are created equal . . . “ Such “truths” may have seemed obvious to the framers, but Harari notes that there is no objective evidence for them in the outside world.  Instead, they are evident based on stories we have told and retold until they have the ring of truth.

So stories (in the past and present) are not about telling the whole truth and nothing but the truth. Instead, they are often about instruction: whom to trust, how to behave, etc. And we should keep this in mind when telling and listening to “data stories.” To serve their purpose, stories leave out a lot of data — particularly data that doesn’t fit the arc of the story. For example, you might not hear about a subgroup whose storyline is quite different from the majority. Or, indeed the story might focus exclusively on a subgroup, ignoring truths about the larger group.

Bottom line: listener beware. A story, whether embellished with data or not, is still just a story. And truth can lie both within and outside of that story.

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 Make Data Beautiful

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Data people meet designer people. Designer people meet data people. You can learn a lot from each other. Today we will focus on what graphic designers know and data analysts should learn.

A couple of tips ago, we talked about how beauty can actually help a viewer more effectively process a visualization of data. If you missed that one, click here. Now we will consider how to make data more beautiful. Luckily, we need not start from scratch. Graphic designers already know a lot about what makes anything that we look at more attractive and engaging. They have written many books and blogs on the subject. But for the purposes of a 60-second data tip, below are some composition basics to consider from Dan Scott. (Also check out his website.) Data presented in a pleasing composition is more likely to engage your viewer.

If you want it in an even smaller nutshell than the list below, here you go: “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.” Sage advice from French writer and poet Antoine de Saint-Exupery.

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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 Reconsider The Pie Chart

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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.

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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).

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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.

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

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

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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

Truth and Beauty

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Real data people care about truth, not beauty. More accurately, they care about evidence that might suggest a truth. So they don’t really embrace truth, just the pursuit of it. However, they don’t have any time for pursuing beauty. Indeed, they may see beauty as deception. A glossy chart or graph is the province of advertisers or advocates seeking to influence rather than to fully inform. As far as the look of displays of information, they advocate for clarity. They may embrace Tufte’s rule of reducing the "data-ink ratio" by removing unnecessary gridlines, labels, and what he calls “chartjunk” (i.e. non-informative elements) to let the data shine through. (For more on Tufte, see Data Tip #11.)

I’m here to argue — both to “real” data people and the rest of us — that we should not discount beauty when visualizing data. Indeed, it might be worth our while to pursue it as we pursue truth. The reason? Well, because we like pretty things. If that sounds like a flimsy explanation,  stick with me a bit longer.

Research evidence suggests that visually attractive things make us happy. (See “The Beauty-Happiness Connection” in The Atlantic for more on this.) And a positive mood, in turn, helps to expand our working memory, which allows us to process more information. So rather than being deceptive window dressing, beauty can actually more deeply engage the viewer in the pursuit of truth.

How can we make data more beautiful? Stay tuned. This is the topic of next week’s data tip.

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

To Improve Anything First Test Your Thinking

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Pop quiz. Take yourself back to seventh grade science class. Wake up from your drowsy, awkward, tween state and then answer this question: “What is a null hypothesis?”

Stay tuned for the answer. First, why are we talking about null hypotheses? Because if you are going to improve anything, you gotta get your null hypothesis on. As I’ve said before, progress in organizations – and indeed, in all of human history – happens when we admit ignorance. The null hypothesis is all about admitting ignorance.

Your science teacher didn’t tell you to make a guess (or a hypothesis) and then look for evidence to support it. Instead, your teacher said to state the opposite of what you believe (or, more specifically, that no relationship exists between two things) and then try to refute it. That opposite statement is the null hypothesis.

Why go at it backwards? The power of the null hypothesis is that it forces you to look beyond your expectations. For example, your hypothesis might be that girls do best in your life skills program based on what you’ve seen so far. The null hypothesis for such a hypothesis might be: there is no difference in performance in the life skills program based on gender. Looking for evidence to support the null hypothesis opens your eyes to other factors (besides gender) that may be at play. Perhaps kids who can sit for longer periods of time do better in the program, and those patient kids often are girls. If so, then you have some powerful information. Maybe building in some movement time will improve overall performance?

If patience and other factors you explore don’t seem to be related to performance, then maybe gender is the key factor.

I’m not suggesting that you launch highly technical controlled experiments. Instead, I’m asking you to first consider that you might be wrong and then pay attention to data that supports such a conclusion. It can point you to new and powerful strategies.

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 Andrew Shiau on Unsplash

Tap Into The Mighty Flowchart

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

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

Guinea Pig It

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Data Viz UX, Episode 5

Today we tackle the fifth and final step in the data viz user experience design process: the beta test. Let’s say you have dutifully followed steps 1-4 by profiling your users, choosing the right data and type of viz, and then refining that viz. Now you have a carefully designed visualization. But does it work on real, live people? Time to find some humans (preferably those similar to your intended users), show them the visualization, and do the following:

  • Ask them what they think the viz is about and what question(s) it is trying to answer.

  • Then ask them to try to answer several specific questions using the viz. These questions should focus on the key information you want users to easily extract from the viz.

  • Take notes. What was difficult for them to figure out? Did they miss any critical aspects of the viz? Did they come to any incorrect conclusions or interesting conclusions you didn’t expect?

Use your notes to revise! Make some aspects of the viz more prominent using color, fade other aspects to the background, add a better title or more captions, remove confusing or distracting elements, even add new data to make clearer comparisons.

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 Karlijn Prot on Unsplash

Turn A Good Viz Into A Great One

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Data Viz UX, Episode 4

Four 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 three steps: knowing your users, choosing the right data, and choosing the right type of visualization (chart, map, graph). The next leg of the journey is to turn a good viz into a great one.

There are lots of suggestions out there about what makes a visualization easy to read and engaging. I’ve culled them down to a list of ten. Each of my “10 Data Viz Suggestments” appears in a previous tip. Please follow the links below to learn more.

  1. Encode Thoughtfully

  2. Consider Your Axes

  3. Highlight What’s Important

  4. Show Order

  5. Clarify With Color

  6. Simplify

  7. Flatten Your Data

  8. Compare Side-By-Side

  9. Zoom In

  10. Stick With A Table (Sometimes)

Stay tuned for the last step in the UX process: testing the viz.

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