工业设计外文翻译---不需要设计师的设计

Design Without Designers

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原文：

Design Without Designers

I will always remember my first introduction to the power of good product design. I was newly arrived at Apple, still learning the ways of business, when I was visited by a member of Apple's Industrial Design team. He showed me a foam mockup of a proposed product. "Wow," I said, "I want one! What is it?"

That experience brought home the power of design: I was excited and enthusiastic even before I knew what it was. This type of visceral "wow" response requires creative designers. It is subjective, personal. Uh oh, this is not what engineers like to hear. If you can't put a number to it, it's not important. As a result, there is a trend to eliminate designers. Who needs them when we can simply test our way to success? The excitement of powerful, captivating design is defined as irrelevant. Worse, the nature of design is in danger.

Don't believe me? Consider Google. In a well-publicized move, a senior designer at Google recently quit, stating that Google had no interest in or understanding of design. Google, it seems, relies primarily upon test results, not human skill or judgment. Want to know whether a design is effective? Try it out. Google can quickly submit samples to millions of people in well-controlled trials, pitting one design against another, selecting the winner based upon number of clicks, or sales, or whatever objective measure they wish. Which color of blue is best? Test. Item placement? Test. Web page layout? Test.

This procedure is hardly unique to Google. has long followed this practice. Years ago I was proudly informed that they no longer have debates about which design is best: they simply test them and use the data to decide. And this, of course, is the approach used by the human-centered iterative design approach: prototype, test, revise.

Is this the future of design? Certainly there are many who believe so. This is a hot topic on the talk and seminar circuit. After all, the proponents ask reasonably, who could object to making decisions based upon data?

Two Types of Innovation: Incremental Improvements and New Concepts

In design—and almost all innovation, for that matter—there are at least two distinct forms. One is

incremental improvement. In the manufacturing of products, companies assume that unit costs will continually decrease through continual, incremental improvements. A steady chain of incremental innovation enhances operations, the sourcing of parts and supply-chain management. The product design is continually tinkered with, adjusting the interface, adding new features, changing small things here and there. New products are announced yearly that are simply small modifications to the existing platform by a different constellation of features. Sometimes features are removed to enable a new, low-cost line. Sometimes features are enhanced or added. In incremental improvement, the basic platform is unchanged. Incremental design and innovation is less glamorous than the development of new concepts and ideas, but it is both far more frequent and far more important. Most of these innovations are small, but most are quite successful. This is what companies call "their cash cow": a product line that requires very little new development cost while being profitable year after year.

The second form of design is what is generally taught in design, engineering and MBA courses on "breakthrough product innovation." Here is where new concepts get invented, new products defined, and new businesses formed. This is the fun part of innovation. As a result, it is the arena that most designers and inventors wish to inhabit. But the risks are great: most new innovations fail. Successful innovations can take decades to become accepted. As a result, the people who create the innovation are not necessarily the people who profit from it.

In my Apple example, the designers were devising a new conception. In the case of Google and Amazon, the companies are practicing incremental enhancement. They are two different activities. Note that the Apple product, like most new innovations, failed. Why? I return to this example later.

Both forms of innovation are necessary. The fight over data-driven design is misleading in that it uses the power of one method to deny the importance of the second. Data-driven design through testing is indeed effective at improving existing products. But where did the idea for the product come from in the first place? From someone's creative mind. Testing is effective at enhancing an idea, but creative designers and inventors are required to come up with the idea.

Why Testing Is Both Essential and Incomplete

Data-driven design is "hill-climbing," a well-known algorithm for optimization. Imagine standing in the dark in an unknown, hilly terrain. How do you get to the top of the hill when you can't see? Test the immediate surroundings to determine which direction goes up the most steeply and take a step that way. Repeat until every direction leads to a lower level.

But what if the terrain has many hills? How would you know whether you are on the highest? Answer: you can't know. This is called the "local maximum" problem: you can't tell if you are on highest hill (a global maximum) or just at the top of a small one.

When a computer does hill climbing on a mathematical space, it tries to avoid the problem of local maxima by initiating climbs from numerous, different parts of the space being explored, selecting the highest of the separate attempts. This doesn't guarantee the very highest peak, but it can avoid being stuck on a low-ranking one. This strategy is seldom available to a designer: it is difficult