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.
A Plea for Lean Software
Software's girth has surpassed its functionality, largely because hardware advances make this possible. The way to streamline software lies in disciplined methodologies and a return to the essentials.
Measured by the number of its features
A primary cause of complexity is that software vendors uncritically adopt almost any feature that users want. Any incompatibility with the original system concept is either ignored or passes unrecognized, which renders the design more complicated and its use more cumbersome. When a system's power is measured by the number of its features, quantity becomes more important than quality. Every new release must offer additional features, even if some don't add functionality.
Essential vs. nice to have
Customers have trouble distinguishing between essential features and those that are just "nice to have." Examples of the latter class: those arbitrarily overlapping windows suggested by the uncritically but widely adopted desktop metaphor; and fancy icons decorating the screen display, such as antique mailboxes and garbage cans that are further enhanced by the visible movement of selected items toward their ultimate destination. These details are cute but not essential, and they have a hidden cost.
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Increased complexity results in large part from our recent penchant for friendly user interaction. I've already mentioned windows and icons; color, gray-scales, shadows, pop-ups, pictures, and all kinds of gadgets can easily be added.
Dependence is more profitable than education
A customer who pays—in advance—for service contracts is a more stable income source than a customer who has fully mastered a product's use.
Customer dependence is more profitable than customer education.
What I find truly baffling are manuals—hundreds of pages long—that accompany software applications, programming languages, and operating systems. Unmistakably, they signal both a contorted design that lacks clear concepts and an intent to hook customers.
The most rewarding iterations
Initial designs for sophisticated software applications are invariably complicated, even when developed by competent engineers. Truly good solutions emerge after iterative improvements or after redesigns that exploit new insights, and the most rewarding iterations are those that result in program simplifications.
Evolutions of this kind, however, are extremely rare in current software practice—they require time-consuming thought processes that are rarely rewarded. Instead, software inadequacies are typically corrected by quickly conceived additions that invariably result in the well-known bulk.
Never enough time
Time pressure is probably the foremost reason behind the emergence of bulky software. The time pressure that designers endure discourages careful planning. It also discourages improving acceptable solutions; instead, it encourages quickly conceived software additions and corrections. Time pressure gradually corrupts an engineer's standard of quality and perfection. It has a detrimental effect on people as well as products.