One piece of software I’m working on right now has gone through many iterations of user interface, implementation, and even overall strategy. You could argue that all of the prototypes we didn’t use were failures, and we would have saved time using a large dataset and an algorithm to eliminate the non-perfect options immediately. Or we could have simply declared it ‘good enough’ and checked it off our list. But every time we went down the ‘wrong’ path, we learned something concrete — if not about software than at least about people.Scott Stevenson
I’ve often wondered how much the because we can motivation echoes in these large scale algorithmic approaches to design decisions. This could manifest in a wide spectrum, from the typical Silicon Valley engineering pissing contests to genuine artistic curiosity and experimentation. It only comes across as negative when this empirical obsession becomes organizational dogma. Because in the hands of the right designers, mistakes are tools.
Besides, given the data, it takes a thoughtful designer to understand how to ask useful questions. It’s not two sides of a mutually exclusive coin; the extremes of Google’s organizational pathology seems to have distracted people from this fact.