Talent Is Overrated (13 page)

Consider, for example, the reading of X-rays. Reaction time doesn't play an important role, but the stakes can be extremely high. In a study, expert radiologists and first- to fourth-year residents were asked to examine several X-rays, taking as long as they wanted, and to give their diagnoses and mark what they considered the problematic areas on the X-rays. The sample X-rays used in the study showed various serious problems, such as multiple tumors or a collapsed lung.

It shouldn't be surprising that the experts performed better; they were far more likely to spot the collapsed lung, for instance. But why? The middle lobe of the lung was collapsed and produced a dense shadow, but this feature could lead to a diagnosis of a tumor. The correct diagnosis required doctors to also see subtler cues, such as hyper-inflation of the adjacent lobes. In marking the X-ray films, the experts picked out more specific features that were significant; they saw more clues to help them solve the puzzle of diagnosis. They also discriminated more finely. For example, the film showing tumors had a few hazy spots on it. The residents saw them as “general lung haziness” and figured they indicated fluid in the lungs, a sign of congestive heart failure. The experts saw correctly that each spot was a tumor.

The experts did not have sharper eyes in the usual sense. They were all looking at the same films and could see them just as clearly. The difference wasn't literally what they saw. It was what they perceived.

The superior perception of top performers extends beyond the sense of sight. They hear more when they listen and feel more when they touch. Highly trained pilots and apprentice pilots were asked to listen to a dialogue between pilots and air traffic controllers, and then to choose a diagram that best represented the situation they had just heard being talked about. The well-trained pilots were twice as good. Musicians are much better than nonmusicians at detecting very small differences in pitch and loudness of notes. Everyone in these studies is hearing the same things, but through years of practice, some are perceiving more.

The relevance of these findings for business seems obvious. Specifically, we can abstract from the research a few ways, directly applicable in business, that top performers perceive more.

 
They understand the significance of indicators that average
performers don't even notice.

 
Just as top tennis players look at the server's body, not at the tennis ball, excellent performers in other fields have learned to spot nonobvious information that's important. Sometimes these signals are profound and become widely known. More than thirty years ago, when Wal-Mart had a very different reputation for employee relations than it does today, Sam Walton found an innovative way to gauge customer satisfaction. He realized that the best indicator of how happy his customers were was to measure how happy his employees were; the way managers treated the employees was the way employees would treat the customers (a lesson the company might want to reflect on).

More often these indicators are small but telling. Certain retail executives have been known to survey the oil stains in a store's parking lot to see how well the customers are maintaining their cars and thus gauge their financial condition. In the 1980s, when fitness was a heavily hyped trend, a business research firm dug through clothing sales statistics and found that the sales volume of clothing size extra-large and larger was increasing fast, an early tip-off that America was getting fatter, not fitter. Laura Rittenhouse, an unusual type of financial analyst, counts the number of times the word
I
occurs in annual letters to shareholders from corporate CEOs, contending that this and other evidence in the letters helps predict company performance (basic finding: Egomaniacs are bad news).

Often these nonobvious indicators are well-guarded secrets. Some hedge funds, for example, use mathematical models built on reliable relationships that the fund owners have discovered in the financial markets. Renaissance Technologies uses such models, and founder James Simons has for several years made more than $1 billion a year personally from the fund. If Renaissance's proprietary models were to become widely known and applied, the fund's advantage would disappear, so it's understandable that Simons doesn't like to talk about them. More generally in business and other fields, nonobvious indicators may be so valuable that most of us never know about them.

In general, regardless of whether indicators are secret, developing and using them requires extensive practice. For example, if you play tennis, you now know one of the ways that pros return serves so well. Yet you probably won't be able to do much with that information the next time you're on the court because you haven't spent hundreds of hours learning how to read the subtle movements of your opponent's hips, shoulders, and arms. Most of the indicators used by top performers require practice to be of any use.

 
They look further ahead.

 
When excellent musicians or typists look further ahead on the page than average performers do, they are literally looking into their own future. Knowing what lies ahead for them, they prepare for it and thus perform better. They may be looking only one second ahead, but for them that extra moment makes all the difference. In other fields the time periods are obviously much greater, and the advantages just as important.

This is not about fortune-telling, or hiring Nostradamus or an astrologer. Much of the power of looking further ahead comes from the simple act of raising one's gaze and getting a new perspective, and doing it not once or occasionally, but using practice principles to do it often and get better at it. When was the last time you, in your working role, participated in a deep discussion about the state of your business five years from now? How about fifteen years from now, including a look at the future of your business's environment, competitors, regulators, and other factors? Such discussions rarely happen below the level of the CEO, yet the experience of excellent performers suggests they offer advantages for everyone.

A few companies look far into the future as a matter of policy. Japan scholar John Nathan recalls meeting with Panasonic founder Konosuke Matsushita, widely regarded as one of the twentieth century's greatest businesspeople; they were in a small boat on a pond at the company compound. Matsushita clapped his hands once. Within moments, several large fish rose to the surface, recognizing the signal for feeding. “These fish understand the long term,” he said. “They live for a hundred years.” Matsushita looked further ahead than that: He had a five-hundred-year plan for his company, which is now more than ninety years old and remains powerful in the notoriously volatile electronics industry.

Oil companies look further into the future than most because they must. Negotiating the rights to an oil field may take many years, then developing it may take another decade, and with luck it will produce oil for decades more. That's why major oil companies routinely look at forecasts of oil supply and demand one hundred years from now. The best ones look beyond the numbers to see possible causes and effects. For example, Shell's scenario planning process famously prepared it for the Arab oil embargo of the 1970s. No scenario told Shell's managers the embargo would happen, since scenarios are thought exercises, not predictions. But one of the scenarios the strategy group cooked up envisioned an accident in Saudi Arabia that raised the price of oil, causing Arab producers to rethink why they set prices as they did. Shell managers carried the analysis further and realized that Arab producers, angry with the United States for its support of Israel in the Six-Day War, might believe they could serve many purposes at once by launching an embargo or restricting supply.

Because they had done the exercise, Shell managers could see how events might lead to an embargo, and when it happened, they were much better prepared than their competitors to respond. They had seen this movie already, so they slowed refinery expansion and adapted their refineries to handle many types of crude, while competitors vacillated. The common view in the industry is that Shell came through the oil shock far better than any other major producer.

These days it's common to question whether looking further ahead is worth the effort, since short-termism seems rampant. The conventional view is that no one is looking past the next quarter. But like much conventional wisdom, this just isn't so. Look at the stock tables any day and you'll find plenty of companies, many of them in biotechnology or infotech, with no profits and no prospect of profits anytime soon, yet with considerable share prices. Investors are valuing these companies by looking years into the future. Fashions in the market come and go, but the future always counts, and looking further into it—rationally—is always an advantage.

 
They know more from seeing less.

 
This ability is essential for success in every real-life domain because we never have as much information as we want. Getting information pushes at the two constraints everyone faces: It takes time and costs money. Making sound decisions fast and at low cost is a competitive advantage everywhere.

Top performers, through extensive practice, learn this ability for decisions that are most critical in their field. Police officers learn how to decide in a split second whether to shoot. Quarterbacks learn to decide from very few cues whether to throw the ball, and if so, where. Even in business, where linebackers aren't running at you, deciding fast with sparse information is often an advantage. That's easiest to see on Wall Street, where a difference of thirty seconds can turn a winning trade into a losing one, but it's also true in other businesses where the time demands aren't quite so intense. Jack Welch, who considered people decisions the heart of his job as CEO, would sometimes make them very quickly. He met a young GE auditor named John Rice at a lunch and recalls, “I liked him instantly.” A presentation Rice gave impressed Welch, who gave Rice “a battlefield promotion” on the spot. From that career turning point, Rice became one of GE's biggest stars and a vice chairman of the company by age fifty. Welch didn't know much about Rice when he set him on the path, but he knew enough. And he knew it because intensive, disciplined people evaluations had been central to Welch's career for decades.

 
They make finer discriminations than average performers.

 
It was said of Charles Revson, the entrepreneur who built Revlon into a dominant cosmetics firm, that he could distinguish several different shades of black, a particularly difficult skill even among people who work with colors. That ability is a metaphor for making evaluations of every kind. For example, it's one thing to say that a manager is “good with people.” It's another to ask whether a manager notices when a direct report seems no longer challenged by his or her job. If so, is that seen as a problem or an opportunity? What responses are proposed? Of these, how effective or ineffective do they seem, and which, if any, are applied? It's a matter of seeing black versus seeing five shades of black, and it works in evaluating people, situations, proposals, performances, products, or anything else. In each case, seeing differences that others don't see is another way of perceiving more.

 
Note that all these crucial abilities are clearly results of training and practice. We know this because in many cases they are abilities that those in a given field work on diligently, and that instructors try hard to teach. We know it also because research shows that these abilities generally don't transfer beyond the field in which they were learned. We may be tempted to say, for example, that an excellent musician “has a good ear,” meaning an ability to make fine distinctions. But research shows that musicians who can distinguish extremely fine differences in music tones are no better than average in distinguishing different tones in speech. Deliberate practice works by helping us acquire the specific abilities we need to excel in a given field.

It may seem painfully obvious that great performers know more than average ones; we expect a great investor, for example, to know much more about his or her area of investing than average investors do. But it isn't nearly as obvious as it may seem, and in fact there was a time when many researchers believed it was not true. A bit of what they believed probably still resides in what most of us think.

These researchers thought that great performance came not from superior knowledge but from superior reasoning methods and reasoning power. You didn't really have to know much about a field if you knew the best ways to analyze a problem and think it through, and you needed to know even less if your analysis and reasoning power could be juiced by a computer. This line of thought was especially popular in the early days of computers, from the 1950s to the 1970s, when scientists were searching for ways to create intelligent machines and anything seemed possible. So heady was their ambition that in 1957 two scientists (Herbert Simon and Allen Newell) announced a computer program they called the General Problem Solver. It didn't know anything about anything in particular, but it possessed rules of logic and problem-solving strategy that could, in theory, be applied universally. It never did solve any real-world problems, but it showed the direction of much scientific thinking: You didn't need specific knowledge as long as you had a sufficiently powerful intellectual engine.

Eventually researchers began to realize that knowledge-free computing power wasn't producing the results they'd hoped for. To see how their approach wasn't working, consider one of the most celebrated attempts to produce artificial expertise, the quest for a successful chess-playing computer program. Here was the perfect setting for the knowledge-doesn't-matter approach. Just tell the computer the rules and the object of the game, and then turn it loose with its awesome speed and reasoning capacity, which no human could begin to match. The machine's triumph was inevitable.