When I joined Google as its first visual designer, the company was already seven years old. Seven years is a long time to run a company without a classically trained designer. Google had plenty of designers on staff then, but most of them had backgrounds in CS or HCI. And none of them were in high-up, respected leadership positions. Without 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 miniscule design decisions. There are more exciting design problems in this world to tackle.
I’ve always discussed the “Google culture” as instrumental in its search success compared with Yahoo! and Microsoft, more mature companies that have struggled to innovate and execute in some circumstances. But these paragraphs very precisely reveal what may be Google’s great Achilles Heel from a cultural standpoint: its overreliance on data and a certain kind of rational-emprical thinking.
That approach has clearly served the company very well in most areas, but there are circumstances when intuition and instinct, individual opinion and other non-quantifiable factors drive better decision making. I’m fascinated by this.
What do others think? Is Bowman on to something or is he just expressing a personal beef because he’s not a quant or engineer?