2. Develop a problem statement. Scott Adams, the creator of the “Dilbert” comic strip, is not only a witty observer but an insightful thinker. In a Wall Street Journal editorial he described the current budget deficit in a way that could be a model for all problem statements.
Problem statement: The US is broke. The hole is too big to plug with cost cutting or economic growth alone. Rich people have money. No one else does. Rich people have enough clout to block higher taxes on themselves, and they will.
Likely outcome: Your next home will be the box that your laser printer came in.
The beauty of this problem statement lies in its brevity and simplicity (no extra charge for the wit). If you think this kind of concision is easy, just try it!
3. List the knowns and unknowns. What are the known parameters of the problem? Can you visualize and name the parts? What are the relationships among the parts? What is the nature of the problem? Is it a simple problem? A complex problem? A structural problem? A communication problem? A political problem? What remedies have been attempted in the past, and why have they failed? Why bother solving the problem in the first place?
Of course, the knowns of a problem are one thing, and the unknowns are another. The danger with unknowns is the human tendency to replace them with assumptions. It’s important to question whether the knowns are really knowns and not beliefs in disguise. Usually the best way to deal with unknowns is to let them remain a mystery while you forge ahead. They may reveal themselves as you test your hypotheses.
4. Change the frame. What happens when you make the frame bigger or smaller? Or even swap it for another one? For example, the movie industry now believes its biggest challenge is to stop piracy. But what if the problem were reframed?
Original problem statement: Viewers are accessing copyrighted content without paying for it, resulting in millions in lost revenue.
Likely outcome: If piracy isn’t stopped, there will be little incentive to make movies.
Solution: Enact tougher laws against piracy.
So far, piracy laws haven’t moved the needle, so it's unclear whether harsher ones will make a difference. They may simply drive piracy underground, or even cause a backlash among viewers. What if the problem were reframed?
New problem statement: Viewers have unprecedented free access to copyrighted material through the Internet, and there’s little chance of stemming the tide.
Likely outcome: Unless the movie industry changes its models, it will miss out on the exciting possibilities created by advances in technology.
When the problem is stated this way, it looks more like an opportunity than a threat. Systems thinker Gene Bellinger says, “It’s hard to make water flow uphill.” Maybe the solution is to increase the flow of free content, and use it to create deeper relationships with viewers so that movies become a bigger part of their lives. Or maybe access could be restricted to new content only, using the old content as advertising for the new. Or maybe free downloads can produce valuable information about audiences, thereby adding value to future marketing efforts. Then again, maybe the movie industry can simply reduce its lobbying efforts, save a little money, and let time take care of the problem. It all starts with how you frame the problem.
5. Make a simple model. Constructing a model is a practical way of visualizing the key elements of a problem. Statistician George Box once said, “Essentially, all models are wrong, but some are useful.” Models are always wrong in that they don’t serve as detailed illustrations of the problem. This is also why they’re right. The simpler you can make a model, the easier it is to understand the problem.
In The Gardens of Democracy, the authors simplify the problem of political gridlock by dividing the prevailing attitudes toward government into three main categories. Liberals, they say, believe in big what and big how. They believe the government should not only determine the direction of the country, but also supply the plan and the capital for getting there. Conservatives, however, believe in small what and small how, meaning that the public sector shouldn’t determine the direction of the country or supply the wherewithal to get there. Libertarians are more extreme than conservatives. They want a government of no what, no how. The government in this model is laissez faire, doing the absolute minimum to determine the country’s direction or to help it get there. Citizens in this model are on their own.
The authors then introduce a fourth model, big what and small how. In this model, the government has responsibility for setting the country’s overall direction, but leaves the how—the tactical solutions—to individuals and the marketplace. The government’s role is to name the game and level the playing field, while each citizen plays the game as he or she sees fit.
Obviously, these models are simplified to the extreme. Yet that’s precisely why they work. By stripping away the details it’s possible to glimpse a solution. After that, the necessary details can be added back in.
As practitioners in the creative and scientific fields master their professions, the art of framing problems eventually leads to the art of finding problems. Experience teaches which problems are worth solving, and which, if solved, would produce little significant effect. It also teaches which problems are most likely to be personally satisfying. Professionals know that even the toughest mysteries will give up their secrets under the pressure of unrelenting passion.
But where do you find problems that are both worthy and inspiring? While they could arrive from the blue, you can also hunt them down using questions like these:
What’s the either/or that’s obscuring opportunities for innovation?
Where are the usual methods no longer achieving the predicted results?
What’s the can’t-do that you could turn into a can-do?
Which problems are so big that they can no longer be seen?
Which categories or sectors exhibit the most uneven rates of change?
In which area is there a great deal of interest but very few solutions?
Where can you find too little order or too much order?
Which of your talents could be scaled up in some surprising way?
To what new areas could your passion take you?
Feeling and seeing are complementary skills that work best when they’re balanced. If we rely too much on intuition and not enough on rational thinking, we’re like likely to create self-indulgent solutions that don’t map to the real world. If it’s the other way around, we’re likely to miss out on the flashes of insight that fuel innovation.
While many of us have a natural ability in feeling or seeing or both, these can also be developed, nurtured, or strengthened with practice. In my experience, people who claim that all talent is inborn—you either have it or you don’t—are often masking insecurity. The “born geniuses” know the truth: In developing talent, hard work trumps genetics. This is even true for the next talent, the amazing metaskill of dreaming.
DREAMING
Brilliant beyond reason
Imagination is one of the more mysterious capabilities of the human mind. How is it possible to conjure up images, feelings, or concepts that we can’t perceive through our senses? How can we arrive at perfectly workable solutions without the benefit of logical thought? Is imagination learnable, or is it only the preserve of eccentric artists and mad scientists?
The metaskill of imagination is conspicuously absent from the educational system. There are no classes called “Dreaming 101.” Alexander Graham Bell, arguably one of our more prolific inventors, seemed to be unaware of the role of imagination in his own work. He laid down three rules for innovation: 1) Observe as many worthwhile facts as possible; 2) Remember what has been observed; 3) Compare the facts so as to come to conclusions.
Observe, remember, compare—then presto!—idea. Hello? Alex? Could there be anything missing between comparing and concluding? Like maybe an insight? No disrespect to the telephone, but since when does the comparison of facts produce innovation?
Let’s say I compared a
number of worthwhile facts about social media. I observed the ways people use Facebook, noted the increase in worldwide tweets, mapped the behavior of Pinterest users, and measured the market for advertising potential and investor interest. Then I compared these facts. While I might find them interesting, I would still need some insight, spark, or leap of imagination to out-innovate competitors who have access to the same facts. Bell’s formula reminds me of the Monty Python skit in which a man is interviewed about how to make a million pounds. “First,” he says, “get a million pounds.”
When people talk about “dreaming up” an idea, they’re not far from the truth. Imagination is closely linked to dream states. Neuroscientists Charles Limb and Allen Braun studied the brains of jazz musicians, revealing a “disassociated pattern of activity in the prefrontal cortex” when they played improvisational music. They found it was absent when they played memorized sequences. These disassociated patterns, they say, are similar to what happens in REM sleep. Dreaming is marked by a sense of unfocused attention, unplanned or irrational associations, and an apparent loss of control. When students exhibit this behavior in the classroom, teachers call it attention-deficit/hyperactivity disorder. When musicians exhibit it, we call it genius.
Dreams don’t simply visit us. We actively create them while we’re unconscious, not unlike the way we create our perceptions while we’re awake. What makes dreams so fascinating is the absence of logical narrative. The word for dreaming in French is rêver—to rave, to slip into madness. Even though the scenes we create in our dreams may seem random or fantastical, their emotional trajectory often makes complete sense. Our emotions are fully engaged while our reasoning is disconnected.
What if we could harness this capability at will? Wouldn’t this provide the mental leap needed to connect the facts to a new conclusion? As it happens, there’s no other way to do it. Innovation needs a little controlled madness, like the controlled explosions of an internal combustion engine, to move it forward. Applied imagination is the ability to harness dreaming to a purpose. Innovators, then, are just practical dreamers.
The encouraging news from science is that people who have this talent are no smarter on average than other people. They’ve simply learned the “trick” of divergent thinking. Biographer Walter Isaacson described this quality in Steve Jobs: “Was he smart? No, not exceptionally. Instead, he was a genius. His imaginative leaps were instinctive, unexpected, and at times magical.” Jobs had the ability to make connections that other people couldn’t see, simply because they couldn’t let go of what they already knew.
In order to innovate, you need to move from the known to the unknown. You need to hold your beliefs lightly, so that what you believe doesn’t block your view of what you might find out. This is hard for most people. When asked to imagine a new tool for slicing bread, or a new format for a website, or a new melody for a song, they’ll stare blankly as if to say, “How could there be such a thing?” They may recall many of the knives, or the home pages, or popular songs they’ve known, but nothing new will come to mind. At most they might try to combine the features of two or more existing examples to come up with a hybrid.
Why is this? What’s stopping us from using our imagination? We can only guess that our world of ready-made everything has turned us into a population of idea shoppers. We expect to choose our solutions off the rack instead of building them from scratch. We mix them and mash them, never believing that real originality is within our power. And the companies that make our products are not much different. They shop for best practices to make their jobs easier, instead of imagining new practices that could set them apart or push them forward. Somewhere along the line we’ve lost our tolerance for trial and error, settling instead for the derivative, the dull, and the dis-integrated. We need to reverse this trend. If we don’t, we’ll end up low on the Robot Curve.
Originality doesn’t come from factual knowledge, nor does it come from the suppression of factual knowledge. Instead, it comes from the exposure of factual knowledge to the animating force of imagination. Depending on the quality of knowledge and the level of imagination applied to it, an idea can fall into four categories: 1) an idea adapted from the same domain; 2) an idea adapted from a different domain; 3) an idea that is new to the innovator; 4) an idea that is new to the world. These are listed in ascending order, with “new to the world” being the rarest and most valuable. The path of learning starts with the more modest forms of originality and leads to larger ones over time.
Imagination is a renewable resource. It doesn’t get depleted by use, but instead grows stronger with practice. When you learn the trick of dreaming, of disassociating your thoughts from the linear and the logical, you can become a wellspring of originality and brilliance. A client once asked architect Mark Kirkhart how he was able to produce so many fresh concepts for a single building. He said: “I have ideas I haven’t even had yet.”
Like all types of magic, dreaming is the result of practice. There are no shortcuts, only diversions and mental traps. In the following chapters I’ll let you in on the hidden discipline that allows innovators to produce their acrobatic leaps of imagination.
The answer-shaped hole
The number-one hazard for innovators is getting stuck in the tar pits of knowledge. Knowledge has a powerful influence over creativity. While it can free us to imagine new-to-the-world ideas, it can also trap us into believing opportunities are smaller than they are. When we’re stumped by a problem, or when we feel hurried to solve it, our brains can easily default to off-the-shelf solutions based on “what everyone knows.” The problem-solving mind is a sucker for a pretty fact. But what we know today may not be what we need to know tomorrow, since every challenge brings with it new requirements for understanding.
Arthur Conan Doyle, in the voice of Sherlock Holmes, expressed something similar when he said, “It is a capital mistake to theorize before one has data. Insensibly, one begins to twist facts to suit theories, instead of theories to suit facts.” To avoid jumping to conclusions, we need to hold off solving a problem until we can perceive the general shape of its solution. There are three steps in generating the answer to a problem: 1) discover what is; 2) imagine what could be; and 3) describe the attributes of success. Let’s take them one by one.
What is. This is the body of known facts about a problem. Why is it a problem? What is its history? What is the conventional thinking about it? How have similar problems been solved in the past? In other domains? Other cultures? And what are the practical constraints of the problem?
Constraints are the limitations imposed by the subject matter, or by the context, of a problem. They might have to do with budgets, time, manpower, physics, habits, conventions, or human fears. They squeeze the problem down to a size you can focus on. They force you to writhe uncomfortably in its grip while you struggle to break free. Without constraints, solutions tend towards the ungainly, the unfocused, and the unimaginative. Unbounded challenges are anathema to innovators, draining their energy without delivering insight. Bounded challenges provide not only a starting place but a booster shot of adrenaline.
Louis Pasteur, in a famous 1854 lecture at the University of Lille, said: “Dans les champs de l’observation, le hasard ne favorise que les esprit préparés.” In the field of observation, chance favors only the prepared mind. Pasteur’s statement is often used to support the idea that hard work trumps talent, but it also suggests that the better you understand the facts and constraints, the better your chances of solving the problem.
What could be. Facts and constraints are necessary but insufficient. To envision what’s possible, you also need imagination. If innovation is determined by what’s “useful, novel, and nonobvious,” as the US patent system puts it, then you need ways to get beyond the obvious. One such way is by asking deeper questions.
For example, let’s say you run a marketing department in a large company. The director of marketing, or perhaps the CEO, asks you to address declining revenu
es by improving the company’s advertising. You could figure out how the existing campaign might be improved with stronger headlines, better product photography, or more precise targeting. Or you could go a little deeper and think about the strategy of the campaign, questioning the underlying concept. You could go deeper still and ask whether advertising is the best place to address the revenue decline. Maybe the real problem lies in product positioning, requiring a shift in brand strategy to outmaneuver the competition. Then again, you might wonder if positioning can save a product line that’s become commoditized over time. Or maybe the problem is the company itself, increasingly hampered by an outdated business model or an uninspired workforce. As the questions go deeper, the answers get bigger.
When Thomas Edison imagined the light bulb, he didn’t frame the question as, How can we create an alternative source of light? Instead he framed it as, How can we make electricity so cheap that “only the rich will burn candles”? While you can easily overreach the possibilities by thinking too big, it’s much easier to tame a wild idea than reanimate a dead one. The best problem solvers are “high yearners,” people who reach for the stars and land on the moon.
The attributes of success. The shape of the missing answer is formed at the intersection of affordances and desiderata. Affordances are the counterpoint to constraints. They consist of creative possibilities that are native to the subject, the method, the tools, or the challenge. For example, a movie about the early days of movies contains the possibility of being a silent film (The Artist). A car designed for the poor population of India contains the possibility of being extremely minimal (Tata Motors). A company with a breadth of experience but a commoditized product line has the possibility becoming a consulting firm (IBM).
Metaskills- Five Talents for the Robotic Age Page 14