A design process paved with empathic observations will lead you, slowly and iteratively, to a better product.
Editor’s note: this post was originally published on the author’s blog, Exploring the world beyond mobile; this lightly edited version is republished here with permission.
If I’m ever asked what’s most important in UX design, I always reply “empathy.” It’s the core meta attribute, the driver that motivates everything else. Empathy encourages you to understand who uses your product, forces you to ask deeper questions, and motivates the many redesigns you go through to get a product right.
But empathy is a vague concept that isn’t strongly appreciated by others. There have been times when talking to product managers that my empathy-driven fix-it list will get a response like, “We appreciate that Scott, but we have so much to get done on the product, we don’t have time to tweak things like that right now.” Never do you feel so put in your place when someone says that your job is “tweaking.”
The paradox of empathy is that while it drives us at a very deep level, and ultimately leads us to big, important insights, it usually starts small. The empathic process typically notices simple things like ineffective error messages, observed user workarounds, or overly complicated dialog boxes. Empathy starts with very modest steps. However, these small observations are the wedge that splits the log; it’s these initial insights, if you follow them far enough, that open up your mind and lead you to great products.
Finding the holes in qualitative and quantitative testing.
I can’t tell you how often I hear things from engineers like, “Oh, we don’t have to do user testing. We’ve got metrics.” Of course, you can almost forgive them when the designers are busy saying things like, “Why would we A/B test this new design? We know it’s better!”
In the debate over whether to use qualitative or quantitative research methods, there is plenty of wrong to go around. So, let’s look at some of the myths surrounding qualitative and quantitative research, and the most common mistakes people make when trying to use them.
Winning organizations continually experiment.
I constantly hear how enterprises are poor at innovation, bad at product development and unresponsive to business change. So it begs the question, why do so many organizations get it wrong? And what are the key factors to consider when trying to innovate in large organizations?
Typically the factors constraining innovation are conflicting business goals, competing priorities, localized performance measures and success criteria. While these have traditionally been the tools of management — to control workforce behavior and output — in highly competitive and quickly evolving business environments they also have had the adverse effects of killing creativity, responsiveness and ingenuity.
So what are the components needed to unleash innovation in enterprise?
Liberate teams from the annual budget cycle.
The use of the traditional annual fiscal cycle to determine resource allocation encourages a culture that thwarts our ability to experiment and innovate. It perpetuates spending on wasteful activities and ideas that are unlikely to deliver value. How can we get out of the rut that is the annual budget process and encourage experimentation and innovation within our organization?
When it comes to budgets, It is hard to let go of the long-held belief that strong, centralized control provides valuable efficiencies. However well it may have served us in an era of lower complexity and slower technical advances, it now creates barriers that prevent us from adapting quickly to emerging opportunities. In this context, the resources and efforts required to gather information, communicate, and monitor rigid centralized processes outweigh any efficiencies gained. As well, a strongly controlled centralized budget process encourages competitive, rather than collaborative, internal behavior. This is counter-productive to innovation, which requires teamwork and collaboration across the organization.
If what we are trying to build is artificial minds, intelligence might be the smaller, easier part.
When we talk about artificial intelligence, we often make an unexamined assumption: that intelligence, understood as rational thought, is the same thing as mind. We use metaphors like “the brain’s operating system” or “thinking machines,” without always noticing their implicit bias.
But if what we are trying to build is artificial minds, we need only look at a map of the brain to see that in the domain we’re tackling, intelligence might be the smaller, easier part.
Maybe that’s why we started with it.
After all, the rational part of our brain is a relatively recent add-on. Setting aside unconscious processes, most of our gray matter is devoted not to thinking, but to feeling.
There was a time when we deprecated this larger part of the mind, as something we should either ignore or, if it got unruly, control.
But now we understand that, as troublesome as they may sometimes be, emotions are essential to being fully conscious. For one thing, as neurologist Antonio Damasio has demonstrated, we need them in order to make decisions. A certain kind of brain damage leaves the intellect unharmed, but removes the emotions. People with this affliction tend to analyze options endlessly, never settling on a final choice. Read more…