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.
The small web is beautiful
I believe that small websites are compelling aesthetically, but are also important to help us resist selling our souls to large tech companies. In this essay I present a vision for the “small web” as well as the small software and architectures that power it.
Why aim small?
Why aim small in this era of fast computers with plenty of RAM? A number of reasons, but the ones that are most important to me are:
- Fewer moving parts. It’s easier to create more robust systems and to fix things when they do go wrong.
- Small software is faster. Fewer bits to download and clog your computer’s memory.
- Reduced power consumption. This is important on a “save the planet” scale, but also on the very local scale of increasing the battery life of your phone and laptop.
- The light, frugal aesthetic. That’s personal, I know, but as you’ll see, I’m not alone.
Features and complexity
Niklaus Wirth of Pascal fame wrote a famous paper in 1995 called A Plea for Lean Software. His take is that “a primary cause for the complexity is that software vendors uncritically adopt almost any feature that users want”, and “when a system’s power is measured by the number of its features, quantity becomes more important than quality”.
Solving the problem of software bloat
But instead of just complaining, how do we actually solve this problem? Concretely, I think we need to start doing the following:
- Care about size: this sounds obvious, but things only change when people think they’re important.
- Measure: both your executable’s size, and your program’s memory usage. You may want to measure over time, and make it a blocking issue if the measurements grow more than x% in a release. Or you could hold a memory-reduction sprint every so often.
- Language: choose a language that has a chance.
- Remove: cut down your feature set. Aim for a small number of high-quality features. My car can’t fly or float, and that’s okay – it drives well.
- Say no to new features: unless they really fit your philosophy, or add more than they cost over the lifetime of your project.
- Dependencies: understand the size and complexity of each dependency you pull in. Use only built-in libraries if you can.
Raw size isn't enough
A few months ago there was a sequence of posts to Hacker News about various “clubs” you could post your small website on: the 1MB Club, 512KB Club, 250KB Club, and even the 10KB Club. I think those are a fun indicator of renewed interested in minimalism, but I will say that raw size isn’t enough – a 2KB site with no real content isn’t much good, and a page with 512KB of very slow JavaScript is worse than a snappy site with 4MB of well-chosen images.
...[Instead, it's about] an “ethos of small”. It’s caring about the users of your site: that your pages download fast, are easy to read, have interesting content, and don’t load scads of JavaScript for Google or Facebook’s trackers.