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.
Pair Design: Better Together
Pair design is the counterintuitive practice of getting more and better UX design done by putting two designers together as thought partners to solve design problems. It’s counterintuitive because you might expect that you could split them up to work in parallel to get double the design done, but for many situations, you’d be wrong. This document will help explain what pair design is, how it works, and tour through the practicalities of implementing it in your practice.
It involves two brains
It involves two brains on a project at the same time. This doesn’t mean part time, checking in with each other on work that’s been accomplished separately.
Pair design really means being in the same room, working on the same problem, with both brains focused on the problem simultaneously for the duration of the project.
A distinct and complementary stance
Each person in the pair takes a distinct and complementary stance toward the design problem as they work together. One generates solutions. That is, one individual materializes solutions to the problem at hand for discussion and iteration. The other synthesizes the proposed solutions.
Gens and synths
Gens are generally comfortable drawing and drawing in front of their partner. Additionally, the generator needs to have “fearless generativity,” to be able to come up with a dozen pretty good solutions to a problem even with incomplete information.
Designers in the synthesizer role need to be skilled at describing designs and explaining rationale in writing. The role requires the designer to be detail oriented and have a strong memory, to keep the big picture of the system, stakeholders, and users in mind as a reference for designs on the table.
We come as a team
There is a legend at Cooper of one team who found pairing with each other so powerful and fruitful that when they left that company, they sought out opportunities and even interviewed at other organizations as a pair.
Starting off with pair design
It’s better to start small. Find the “genniest” designer you can and pair her with the “synthiest,” have them work through a few projects as a pair to see how it goes, evolve a process that works for your organization, smooth out the wrinkles, and become resident experts. Then, split them up, assign them with new pairs, and begin to spread.
What are the benefits of pair design?
It Makes for Better Design
- Pairing forces constant iteration: idea testing and course-correction.
- It brings to bear two brains and two stances.
It Makes for Better Designers and Better Design Organizations
- They are happier.
- Pair design makes it easier to focus on core aptitudes.
- They cross-pollinate: a mechanism for a learning organization.
Pair Design Makes for a More Effective Process
- Pairing avoids the problem of dueling whiteboards.
- It encourages designers to materialize ideas early.
- It encourages designers to vocalize their rationale.
- It encourages constant course-correction.