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.
Ancient magicians as innovation consultants
The Codex Justinianus (534 AD), being the book of law for ancient Rome at that time, banned magicians and, in doing so, itemised the types:
- A haruspex is one who prognosticates from sacrificed animals and their internal organs;
- a mathematicus, one who reads the course of the stars;
- a hariolus, a soothsayer, inhaling vapors, as at Delphi;
- augurs, who read the future by the flight and sound of birds;
- a vates, an inspired person - prophet;
- chaldeans and magus are general names for magicians;
- maleficus means an enchanter or poisoner.
I happen to have spent my career in a number of fields that promise to have some kind of claim to supernatural powers: design, innovation, startups…
It’s not hard to run through a few archetypes of the people in those worlds, and map them onto types of ancient magician.
- Those like Steve Jobs (with his famous Reality Distortion Field) who can convincingly tell a story of the future, and by doing so, bring it about by getting others to follow them – prophets.
- Inhaling the vapours and pronouncing gnomic truths? You’ll find all the thought leaders you want in Delphi, sorry, on LinkedIn.
- Those with a good intuition about the future who bring it to life with theatre, and putting people in a state of great excitement so they respond – ad planners. Haruspex.
- Those who have the golden mane of charisma: enchanters. Startup founders.
- People with a great aptitude for systems and numbers, who can tell by intuition what will happen, from systems that stump the rest of us. We call them analysts now. MBAs. Perhaps the same aptitude drew them to read the stars before? Mathematicus.