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.

Choose The Right Viz

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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)

Choose The Right Data

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Data Viz UX, Episode 3

Too often we start with the data rather than the questions. It’s sort of like starting dinner with the ingredients you happen to have in the fridge (frozen pizza, grape juice, and hot sauce) rather than asking: what meal would be most healthy and satisfying and then buying the right ingredients for that meal.

Two 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. And last week I shared the first step, which is knowing your users. This week’s tip is about the second step: choosing the right data.

Ideally, you don’t start at the fridge when making dinner. And ideally, you don’t start with data when planning and evaluating your work. Instead, you decide what you need to know to improve what you do. Let’s say you run a tutoring program. You rely on talented tutors. So, you might ask: who make the best tutors? Okay, you are off to a great start. Now do the following:

Refine The Question. What do you mean by “tutors”? Only tutors in your own program or more generally? Do you want to look at only tutors with a significant degree of experience or also include newbies? What do you mean by “best”? Those who persist in the program for at least a year? Those whose students show academic improvement? Those who form close relationships with their students? After some refining, you might end up with a question like this: “Among our past tutors (2000-2018), who has persisted (>=6 months) in the program and had students whose GPA increased (>=1 point)?”

Identify Important Subgroups. Perhaps you want to see if certain types of tutors works best with certain types of students. Then you are going to need data on both tutor characteristics (such as ethnicity, gender, profession) and student characteristics.

Share Your Strategy. At this point, it’s a good idea to check in with the folks who are going to use the data to make decisions. Share with them how you have refined the question and the subgroups you intend to look at. Get their feedback and tweak your strategy.

Find the Right Data. Okay, now you can consider data because now you know what data you need. You might consider data in your own databases and data from other sources. Before settling on any data sources, always ask: Is the data credible? Is it complete? Is the data clean (e.g. have duplicates and data entry errors been removed)? Is the data connected (e.g. if you are using multiple data sources, is there a way to connect them using unique identifiers for individuals or groups)?

Turns out that if you ask good, clear questions, you get better answers — answers you can use. Stay tuned for the other steps in the UX process: choosing the right viz, refining the viz, and testing it.

And, if you have a moment, check out this great New York Times article which shows how our view of the economy depends on what questions we ask and what data we choose. (Turns out G.D.P. is kind of like the hot sauce in our fridge. We use it because we have it. But we’d be better off with different 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.

Know Your Data Viz User

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Data Viz UX, Episode 2

UX is a hip (if inexact) acronym for user experience. As I explained in last week’s data tip, it’s about how user-friendly a product or service is. In that tip, I promised to show you how to apply some UX tips to keep your viewers awake, engaged, and wielding data from the data visualizations (aka data viz) you create. It involves knowing your users, choosing the right data, choosing the right viz, refining the viz, and testing it.

Today’s topic: knowing your users. I will offer brief tips on the other topics in the weeks to come. To know your data viz user, answer the following questions:

Who are they? Your intended user can range from just yourself to all living sentient beings. Answer this question as exactly as possible.

What do they want to know? What decisions are they looking to make? Are they monolithic or diverse in their interests?

How engaged are they? Do they have a deep or casual interest in the topic? Or somewhere in between?

What do they already know? Consider how savvy they are both about the topic and about data analysis. While you don’t want to assume too much knowledge, you don’t want to patronize either.

With a clear picture of your intended users in mind, you are ready to consider what data to present. Stay tuned. That’s next week’s 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.

How to make a good data experience for your funders, board, and staff

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Data Viz UX, Episode 1

“UX” is one of those terms that pretty much everyone has heard. But those of us outside the tech or corporate worlds might politely nod at its mention and then wonder, “Wait, what's UX again?”

It means “user experience.” (No, experience does not begin with an X. Just another way in which the tech world is cooler than the rest of us.) And what exactly is user experience? It’s the experience we have with any product or service. The experience you had assembling 482 parts to make an Ikea dresser? Bad UX. The experience you had buying all your holiday gifts online and avoiding the mall? Good UX.

Data visualizations come with their own UX. There are some pretty sexy charts out there that dazzle at first sight but ultimately frustrate when you try to extract meaning from them.

So how can you keep your funders, board, and staff awake, engaged, and wielding data from the visualizations (aka vizes) you create? It involves knowing your users, choosing the right data, choosing the right viz, refining the viz, and testing it.

That’s way more than I can do in 60 seconds. But I promise to feed it to you in 60-second bites, starting next week. Until then, notice your own experiences trying to extract meaning from charts, maps, and graphs. What goes down easily? What makes you choke?

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