data
INSUFFICIENT DATA FOR MEANINGFUL ANSWER.
The Eyes Have It
A Research Paper by Ben ShneidermanThe trend is your friend 'til the bend at the end
A Fragment by Noah SmithIn the past, GDP and resources use have always been tightly correlated. But this is just drawing a line through some data — it’s not based on any deep theory. And in fact, these correlations can change very quickly. Just as one example, here’s energy use versus GDP since 1949.
If you were sitting in 1970, you could look at this curve and claim, very confidently, that economic growth requires concomitant increases in energy use. And you’d be wrong. Because the trend is your friend til the bend at the end.
Embracing Asymmetrical Design
An Article by Ben NadelHumans love symmetry. We find symmetry to be very attractive. Our brains may even be hard-wired through evolution to process symmetrical data more efficiently. So, it's no surprise that, as designers, we try to build symmetry into our product interfaces and layouts. It makes them feel very pleasant to look at.
Unfortunately, data is not symmetrical…Once you release a product into "the real world", and users start to enter "real world data" into it, you immediately see that asymmetrical data, shoe-horned into a symmetrical design, can start to look terrible.
To fix this, we need to lean into an asymmetric reality. We need to embrace the fact that data is asymmetric and we need to design user interfaces that can expand and contract to work with the asymmetry, not against it. To borrow from Bruce Lee, we need to build user interfaces that act more like water:
“You must be shapeless, formless, like water. When you pour water in a cup, it becomes the cup. When you pour water in a bottle, it becomes the bottle. When you pour water in a teapot, it becomes the teapot. Water can drip and it can crash. Become like water my friend.” — Bruce Lee
Goodbye, Google
An Article by Douglas BowmanWithout a person at (or near) the helm who thoroughly understands the principles and elements of Design, a company eventually runs out of reasons for design decisions. With every new design decision, critics cry foul. Without conviction, doubt creeps in. Instincts fail. “Is this the right move?” When a company is filled with engineers, it turns to engineering to solve problems. Reduce each decision to a simple logic problem. Remove all subjectivity and just look at the data. Data in your favor? Ok, launch it. Data shows negative effects? Back to the drawing board. And that data eventually becomes a crutch for every decision, paralyzing the company and preventing it from making any daring design decisions.
Yes, it’s true that a team at Google couldn’t decide between two blues, so they’re testing 41 shades between each blue to see which one performs better. I had a recent debate over whether a border should be 3, 4 or 5 pixels wide, and was asked to prove my case. I can’t operate in an environment like that. I’ve grown tired of debating such minuscule design decisions. There are more exciting design problems in this world to tackle.
The Subtleties of Color
A Series by Robert SimmonThe use of color to display data is a solved problem, right? Just pick a palette from a drop-down menu (probably either a grayscale ramp or a rainbow), set start and end points, press “apply,” and you’re done. Although we all know it’s not that simple, that’s often how colors are chosen in the real world. As a result, many visualizations fail to represent the underlying data as well as they could.
Data Farming
A Research PaperMiners seek valuable nuggets of ore buried in the earth, but have no control over what is out there or how hard it is to extract the nuggets from their surroundings. ... Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data.
Farmers cultivate the land to maximize their yield. They manipulate the environment to their advantage using irrigation, pest control, crop rotation, fertilizer, and more. Small-scale designed experiments let them determine whether these treatments are effective. Similarly, data farmers manipulate simulation models to their advantage, using large-scale designed experimentation to grow data from their models in a manner that easily lets them extract useful information.
You're Probably Using the Wrong Dictionary
As if a word were no more than coordinates
The New Oxford American dictionary, by the way, is not like singularly bad. Google’s dictionary, the modern Merriam-Webster, the dictionary at dictionary.com: they’re all like this. They’re all a chore to read. There’s no play, no delight in the language. The definitions are these desiccated little husks of technocratic meaningese, as if a word were no more than its coordinates in semantic space.
Another mind as alive as yours
In 1807, Webster started writing a dictionary, which he called, boldly, An American Dictionary of the English Language. He wanted it to be comprehensive, authoritative. Think of that: a man sits down, aiming to capture his language whole.
Dictionaries today are not written this way. In fact it’d be strange even to say that they’re written. They are built by a large team, less a work of art than of engineering. When you read an entry you don’t get the sense that a person labored at his desk, alone, trying to put the essence of that word into words. That is, you don’t get a sense, the way you do from a good novel, that there was another mind as alive as yours on the other side of the page.
Webster’s dictionary took him 26 years to finish. It ended up having 70,000 words. He wrote it all himself, including the etymologies, which required that he learn 28 languages, including Old English, Gothic, German, Greek, Latin, Italian, Spanish, Dutch, Welsh, Russian, Aramaic, Persian, Arabic, and Sanskrit. He was plagued by debt to fund the project; he had to mortgage his home.
A soft and fitful luster
Who decided that the American public couldn’t handle “a soft and fitful luster”? I can’t help but think something has been lost. “A soft sparkle from a wet or oily surface” doesn’t just sound worse, it actually describes the phenomenon with less precision. In particular it misses the shimmeriness, the micro movement and action, “the fitful luster,” of, for example, an eye full of tears — which is by the way far more intense and interesting an image than “a wet sidewalk.”
It’s as if someone decided that dictionaries these days had to sound like they were written by a Xerox machine, not a person, certainly not a person with a poet’s ear, a man capable of high and mighty English, who set out to write the secular American equivalent of the King James Bible and pulled it off.
Pathos
With its blunt authority the New Oxford definition of “pathos” — “a quality that evokes pity or sadness” — shuts down the conversation, it shuts down your thinking about the word, while the Webster’s version gets your wheels turning: it seems so much more provisional — “that which awakens tender emotions, such as pity, sorrow, and the like; contagious warmth of feeling, action, or expression; pathetic quality; as, the pathos of a picture, of a poem, or of a cry” — and therefore alive.
Most important, it describes a word worth using: a mere six letters that have come to stand for something huge, for a complex meta-emotion with mythic roots. Such is the power of actual English.
An affection for words
There’s an amazing thing that happens when you start using the right dictionary. Knowing that it’s there for you, you start looking up more words, including words you already know. And you develop an affection for even those, the plainest most everyday words, because you see them treated with the same respect awarded to the rare ones, the high-sounding ones.
Which is to say you get a feeling about English that Calvin once got with his pet tiger on a day of fresh-fallen snow: “It’s a magical world, Hobbes. Let’s go exploring!”