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.
What's Wrong With This Model?
What's wrong with the rational model
- We Don’t Really Know the Goal When We Start
- We Usually Don’t Know the Decision Tree – We Discover It as We Go
- The Nodes Are Really Not Design Decisions, but Tentative Complete Designs
- The Goodness Function Cannot be Evaluated Incrementally
- The Desiderata and Their Weightings Keep Changing
- The Constraints Keep Changing
Deciding what to design
We Don’t Really Know the Goal When We Start
The most serious model shortcoming is that the designer often has a vague, incompletely specified goal, or primary objective. In such cases, the hardest part of design is deciding what to design.
I came to realize that the most useful service I was performing for my client was helping him decide what he really wanted.
Today, we recognize that rapid prototyping is an essential tool for formulating precise requirements. Not only is the design process iterative; the design-goal-setting process is itself iterative. Knowing complete product requirements up front is a quite rare exception, not the norm. Therefore, goal iteration must be considered an inherent part of the design process.
Evaluating goodness
The Goodness Function Cannot be Evaluated Incrementally
The Rational Model assumes that design involves a search of the decision tree, and that at every node, one can evaluate the goodness function of several downward branches. In fact, one cannot in general do this without exploring all the downward branches to all their leaves, which is possible in principle, but leads to a combinatorial explosion of alternatives in practice.
Changing constraints
The Constraints Keep Changing
The explicit listing of known constraints in the design program helps here. The designer can periodically scan the list, asking, “Can this constraint now be removed because the world has changed? Can it be entirely circumvented by working outside the design space?”
They just don't work that way
Perhaps the most devastating critique of the Rational Model, although perhaps the hardest to prove, is that most experienced designers just don’t work that way.
“Conventional wisdom about problem-solving seems often to be contradicted by the behavior of expert designers. Empirical studies of design activity have frequently found ‘intuitive’ features of design ability to be the most effective and relevant to the intrinsic nature of design. Some aspects of design theory, however, have tried to develop counter-intuitive models and prescriptions for design behavior.” — Nigel Cross
We must outgrow it
Why all this fuss about the process model? Does the model we and others use to think about our design process really affect our designing itself? I believe it does. I believe our inadequate model and following it slavishly lead to fat, cumbersome, over-features products and also to schedule, budget, and performance disasters.
The Rational Model, in any of its forms, leads us to demand up-front statements of design requirements. It leads us to believe that such can be formulated. It leads us to make contracts with one another on the basis of enshrined ignorance. A more realistic process model would make design work more efficient, obviating many arguments with clients and much rework.
The Waterfall Model is wrong and harmful; we must outgrow it.