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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. q card gas station 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. year 6 electricity unit 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.

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. electricity and magnetism worksheets middle school 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.)

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.

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.

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. k gas oroville 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.

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)?”

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)?

Organizations tend to talk in a certain language. electricity nightcore We speak to our colleagues, clients, funders, and board members using particular terms. Anyone new to an organization learns this quickly. We also have common understandings about the needs we are addressing, the services we provide, and the people we serve. This common language helps us communicate efficiently. Unless we are speaking with someone far removed from our work (usually at cocktail parties), we can speak in this sort of code to others without long explanations of terms.

The problem with our common languages is that they may obscure what we see. Various studies suggest just how powerfully language affects perception. For example, the Himba tribe in Namibia has no word for blue. In a study, tribe members who were shown a circle with 11 green squares and one blue struggled to distinguish between the blue square and the green ones. However, the Himba have more words for types of green than exist in English. So, in the same study, they could distinguish between squares of slightly different shades of green much better than we can. gas mask bong nfl To see the colored squares, check out Kevin Loria’s fascinating article in Business Insider. Other studies suggest that language can affect our understanding of space, time, causality, and our relationships with others. (See Can Language Influence Our Perception of Reality? by Mitch Moxley in Slate .)

We can’t turn a language switch and suddenly see our organizations differently. But we can be aware of our language and ask questions about the terms we use and the assumptions we make. Data can make the invisible visible to us if we ask the right questions. For example, we might have a short hand profile of the typical student who is persistently truant. We might assume that truant students struggle with academics. But is this true? Even if they have, in general, lower grades than non-truant students, their grades may be the result rather than the cause of their truancy. What do the numbers show?

“Standard deviation” sounds like an oxymoron. Any high school student knows that you can’t be both standard and a deviant at the same time. 1 unit electricity cost in india And any high school stats class will clarify, in the first week or so, what standard deviations are really about. And most high school students will hold that knowledge for a semester and then delete it to make space for more valuable knowledge.

60-Second Data Tip #13 addressed what averages obscure. The answer was: how spread out your data points are around the average. static electricity diagram The standard deviation tells you just how spread out they are. You might think of the average as the “standard” (the person at your high school who was average in every way). And you might think of the rest of the data points as “deviants” with some deviating from average just a bit (perhaps a kid with a nose ring who was otherwise sporty) and others deviating a lot (full-on Goth).

A standard deviation close to 0 indicates that the data points tend to be very close to the mean. As standard deviation values climb, data point values are farther away from the mean, on average. So a job-training program aiming for an average wage of $17 per hour among participants might want to see a pretty low standard deviation in wages to feel confident that the large majority has reached the goal.

There is a certain breed of nonprofit staff who roll their eyes at the mention of “evidence-based practices” or “KPIs” or other data jargon. I myself have experienced mild nausea when listening to someone try to quantify what seems unquantifiable: what a child feels after learning to paint or what a homeless adult feels upon acquiring an apartment.

What we perceive is based not just on what we actually observe but also on what we expect to observe. This is how it works. First, the brain evaluates which of a variety of probable events are actually occurring. Then it uses this information, along with signals from the outside world (aka data), to decide what it is perceiving. And here’s the surprising news: there are far more signals coming from within the brain that affect our perception than data signals from the outside.

The esteemed philosopher and psychologist, William James, noted in The Principles of Psychology (1890): “Millions of items of the outward order are present to my senses which never properly enter into my experience. Why? Because they have no interest for me. My experience is what I agree to attend to. Only those items which I notice shape my mind .”