I don’t even know.
If you hate accidental auto-sharing, you’re in luck. Now you have to be watching or reading something for at least 10 seconds before Facebook apps can auto-share the activity to your Timeline.
A simple, good change. I’ve actually avoided clicking on things inside Facebook, worried that one would be a “landmine” — some bit of clickbait tied to Open Graph sharing. Why was I clicking on it in the first place? That’s another story…
Facebook adds delay to OpenGraph frictionless sharing
“The future is already here, it’s just not evenly distributed.” - William Gibson
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The first issue includes a report on Steve Jobs squabbling with Woz over the former’s plan to kill off the Newton, the news that Internet Explorer can now display HTML mail, and a reminder to watch the second series of The X-Files on BBC 1.
How Facebook’s other app recognises you…
On Thursday Facebook had the third-largest I.P.O. ever. In the week leading up it, my colleague Amanda Cox spent some time thinking how to best explain and contextualize this offering to readers. What follows is a series of sketches from Amanda, who shared her project folder with me for this…
Data Art vs. Data Visualization: Why Does a Distinction Matter?
Visual Business Intelligence
Two distinct approaches to presenting data graphically exist today—data visualization and data art—and rarely do the twain meet. They differ in purpose and in design. When we fail to distinguish them from one another, we not only create confusion, but do great harm as well.
There are as many definitions of data visualization as there are definers, but at the root of this term that has been around for many years is the goal that data be visualized in a way that leads to understanding. Whatever else it does, it must inform. If we accept this as fundamental to the definition of data visualization, we can judge the merits of any example above all else on how clearly, thoroughly, and accurately it enlightens.
By data art, I’m referring to visualizations of data that seek primarily to entertain or produce an aesthetic experience. It is art that is based on data. As such, we can judge its merits as we do art in general.
Either one, done well, is worthwhile, assuming that it fits the task at hand. If the task is to help a particular group of people understand something, then data art is not appropriate, no matter how well it is executed. If the task is to entertain or engage an audience in a particular emotional experience, then data visualization probably isn’t appropriate. If the situation requires that both objectives are achieved, then a deeply informing and aesthetically beautiful visualization would be in order. Although it is quite easy to make any data visualization aesthetically pleasing, it takes a great deal of skill as a visual designer and information communicator to make one beautiful.
People make better decisions when they’re based on understanding. For information to be understood, it must often be presented in visual form. This is because patterns, trends, outliers, and a sense of the whole as opposed to its parts require a picture for the human brain to see and comprehend. Data visualization is essential. Visualizing data effectively is vital. Anything less is frivolous, costly, and harmful.
How in particular is data art—visualizations that strive to entertain or to create aesthetic experiences with little concern for informing—harmful when it masquerades as data visualization?
It suggests that data cannot be visualized without training in the graphic arts. As such, it works against the democratization of data. In fact, anyone of reasonable intelligence and a little training can present data effectively. It’s vital that this ability spreads more broadly across the population, because it can play a role in making a better world.
It features ineffective practices as exemplars of data visualization. It encourages people to present data in ways that are difficult to perceive and understand simply because they are prettier or more entertaining, which is rarely relevant to the task.
It keeps the practice of data visualization spinning its wheels, never able to progress beyond the mistakes of the past. Best practices of data visualization have emerged through many years of research and experience. “Those who cannot remember the past are condemned to repeat it” (Santayana).
I am personally and painfully acquainted with each of these problems. For this reason, I try to differentiate data art from data visualization and encourage others to do so as well.
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