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.
On Motivation
I’ve had this (semi-vague) idea that I want to write about what keeps us excited to work on Are.na, on the occasion of its 10th (yes 10th) birthday. While writing generally is a tricky process for me, this subject motivation is even trickier.
In turning this subject around in my mind, and thinking about all the people and things that have been influential, I have some idea about what keeps us going.
Nodal points
I started thinking about all the other important “nodal points” (I don’t know what else to call this) of people, places, books, albums, websites, etc. that all played a part in shaping who I am as a person and what I think is important. These points are a combination of seeking things out myself and getting a recommendation that felt like it was actually for me. A mixture of both passive and active knowledge acquisition.
ultimately, it's the totality of those “nodal points” that indicate one’s own unique perspective. It doesn’t matter if you specifically sought out the nodal point or not, it’s the recognition that counts. When you encounter a piece of life-changing information (no matter how large the change part is), you are simultaneously discovering and creating “yourself,” becoming incrementally more complete. Your perspective (where your gaze is directed) is made up of a meandering line through these points. Learning (or maybe some precursor to learning) is a lot about developing the intuition to recognize when something you find in the world is going to be a nodal point for you.
Infinite varieties of contexts
Over the course of 10 years of using Are.na, I have fully adopted the view that any piece of information can be important to a person given the right context. And on Are.na, pieces of information can be arranged in infinite varieties of contexts – their respective meaning shifts as the proximate information shifts. In other words, the more connections a block has, the more opportunities it has to be a nodal point.
A lifelong project
One question that is still hard to answer after 10 years of working on Are.na is “what is the long term vision?” This is difficult for a few reasons.
One reason is that we have to calibrate our definition of long term with the person who is asking the question. Are.na is a lifelong project. Our ideal outcome as a company is not becoming the next Facebook (god forbid), it’s becoming the next Nishiyama Onsen Keiunkan, a hot spring hotel in Japan, and one of the world’s oldest businesses (founded in 705 AD).
Growing in the correct way
It often feels like Are.na itself has its own needs and desires—that Are.na has its own personal intuition. And that we (you and I) are figuring out what it wants to be together. We (the organization that works on Are.na) have a very defined sense of how things should be done and why building Are.na is important, but we try not to be overly dogmatic about what exactly it should evolve into.
…We’ve long had the sense that it’s possible to cultivate an experience on the Internet that is more calm, thoughtful, and introspective. And we’ve long had the view that this is possible not from a technology-oriented approach, but from an approach that is more soft, more personal, and more intuitive.
…If we are the blade, what is the shield? I think it’s speed: The dominant model for online platforms (especially social platforms) is speed and scale at all costs. But to us, growing Are.na in the correct way is more important than growing it quickly.