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.
95%-ile isn't that good
Reaching 95
Reaching 95%-ile isn't very impressive because it's not that hard to do. I think this is one of my most ridiculable ideas. It doesn't help that, when stated nakedly, that sounds elitist. But I think it's just the opposite: most people can become (relatively) good at most things.
Note that when I say 95%-ile, I mean 95%-ile among people who participate, not all people (for many activities, just doing it at all makes you 99%-ile or above across all people). I'm also not referring to 95%-ile among people who practice regularly. The "one weird trick" is that, for a lot of activities, being something like 10%-ile among people who practice can make you something like 90%-ile or 99%-ile among people who participate.
Mistakes at the top
Personally, in every activity I've participated in where it's possible to get a rough percentile ranking, people who are 95%-ile constantly make mistakes that seem like they should be easy to observe and correct. "Real world" activities typically can't be reduced to a percentile rating, but achieving what appears to be a similar level of proficiency seems similarly easy.