Hidden flaws in strategy
Can insights from behavioral economics explain why good executives back bad strategies?
MAY 2003 • Charles Roxburgh
..After nearly 40 years, the theory of business strategy is well developed and widely disseminated. Pioneering work by academics such as Michael E. Porter and Henry Mintzberg has established a rich literature on good strategy. Most senior executives have been trained in its principles, and large corporations have their own skilled strategy departments.
Yet the business world remains littered with examples of bad strategies. Why? What makes chief executives back them when so much know-how is available? Flawed analysis, excessive ambition, greed, and other corporate vices are possible causes, but this article doesn’t attempt to explore all of them. Rather, it looks at one contributing factor that affects every strategist: the human brain.
The brain is a wondrous organ. As scientists uncover more of its inner workings through brain-mapping techniques,1 our understanding of its astonishing abilities increases. But the brain isn’t the rational calculating machine we sometimes imagine. Over the millennia of its evolution, it has developed shortcuts, simplifications, biases, and basic bad habits. Some of them may have helped early humans survive on the savannas of Africa ("if it looks like a wildebeest and everyone else is chasing it, it must be lunch"), but they create problems for us today. Equally, some of the brain’s flaws may result from education and socialization rather than nature. But whatever the root cause, the brain can be a deceptive guide for rational decision making.
The basic assumption of modern economics—rationality—does not stack up against the evidence
These implications of the brain’s inadequacies have been rigorously studied by social scientists and particularly by behavioral economists, who have found that the underlying assumption behind modern economics—human beings as purely rational economic decision makers—doesn’t stack up against the evidence. As most of the theory underpinning business strategy is derived from the rational world of microeconomics, all strategists should be interested in behavioral economics.
Insights from behavioral economics have been used to explain bad decision making in the business world,2 and bad investment decision making in particular. Some private equity firms have successfully remodeled their investment processes to counteract the biases predicted by behavioral economics. Likewise, behavioral economics has been applied to personal finance,3 thereby providing an easier route to making money than any hot stock tip. However, the field hasn’t permeated the day-to-day world of strategy formulation.
This article aims to help rectify that omission by highlighting eight4 insights from behavioral economics that best explain some examples of bad strategy. Each insight illustrates a common flaw that can draw us to the wrong conclusions and increase the risk of betting on bad strategy. All the examples come from a field with which I am familiar—European financial services—but equally good ones could be culled from any industry.
Several examples come from the dot-com era, a particularly rich period for students of bad strategy. But don’t make the mistake of thinking that this was an era of unrepeatable strategic madness. Behavioral economics tells us that the mistakes made in the late 1990s were exactly the sorts of errors our brains are programmed to make—and will probably make again.
Flaw 1: Overconfidence
Our brains are programmed to make us feel overconfident. This can be a good thing; for instance, it requires great confidence to launch a new business. Only a few start-ups will become highly successful. The world would be duller and poorer if our brains didn’t inspire great confidence in our own abilities. But there is a downside when it comes to formulating and judging strategy.
The brain is particularly overconfident of its ability to make accurate estimates. Behavioral economists often illustrate this point with simple quizzes: guess the weight of a fully laden jumbo jet or the length of the River Nile, say. Participants are asked to offer not a precise figure but rather a range in which they feel 90 percent confidence—for example, the Nile is between 2,000 and 10,000 miles long. Time and again, participants walk into the same trap: rather than playing safe with a wide range, they give a narrow one and miss the right answer. (I scored 0 out of 15 on such a test, which was one of the triggers of my interest in this field!) Most of us are unwilling and, in fact, unable to reveal our ignorance by specifying a very wide range. Unlike John Maynard Keynes, most of us prefer being precisely wrong rather than vaguely right.
We also tend to be overconfident of our own abilities.5 This is a particular problem for strategies based on assessments of core capabilities. Almost all financial institutions, for instance, believe their brands to be of "above-average" value.
Related to overconfidence is the problem of overoptimism. Other than professional pessimists such as financial regulators, we all tend to be optimistic, and our forecasts tend toward the rosier end of the spectrum. The twin problems of overconfidence and overoptimism can have dangerous consequences when it comes to developing strategies, as most of them are based on estimates of what may happen—too often on unrealistically precise and overoptimistic estimates of uncertainties.
One leading investment bank sensibly tested its strategy against a pessimistic scenario—the market conditions of 1994, when a downturn lasted about nine months—and built in some extra downturn. But this wasn’t enough. The 1994 scenario looks rosy compared with current conditions, and the bank, along with its peers, is struggling to make dramatic cuts to its cost base. Other sectors, such as banking services for the affluent and on-line brokerages, are grappling with the same problem.
There are ways to counter the brain’s overconfidence:
1.Test strategies under a much wider range of scenarios. But don’t give managers a choice of three, as they are likely to play safe and pick the central one. For this reason, the pioneers of scenario planning at Royal Dutch/Shell always insisted on a final choice of two or four options.6
2.Add 20 to 25 percent more downside to the most pessimistic scenario.7 Given our optimism, the risk of getting pessimistic scenarios wrong is greater than that of getting the upside wrong. The Lloyd’s of London insurance market—which has learned these lessons the hard, expensive way—makes a point of testing the market’s solvency under a series of extreme disasters, such as two 747 aircraft colliding over central London. Testing the resilience of Lloyd’s to these conditions helped it build its reserves and reinsurance to cope with the September 11 disaster.
3.Build more flexibility and options into your strategy to allow the company to scale up or retrench as uncertainties are resolved. Be skeptical of strategies premised on certainty.
Flaw 2: Mental accounting
Richard Thaler, a pioneer of behavioral economics, coined the term "mental accounting," defined as "the inclination to categorize and treat money differently depending on where it comes from, where it is kept, and how it is spent."8 Gamblers who lose their winnings, for example, typically feel that they haven’t really lost anything, though they would have been richer had they stopped while they were ahead.
Mental accounting pervades the boardrooms of even the most conservative and otherwise rational corporations. Some examples of this flaw include the following:
•being less concerned with value for money on expenses booked against a restructuring charge than on those taken through the P&L
•imposing cost caps on a core business while spending freely on a start-up
•creating new categories of spending, such as "revenue-investment spend" or "strategic investment"
All are examples of spending that tends to be less scrutinized because of the way it is categorized, but all represent real costs.
These delusions can have serious strategic implications. Take cost caps. In some UK financial institutions during the dot-com era, core retail businesses faced stringent constraints on their ability to invest, however sound the proposal, while start-up Internet businesses spent with abandon. These banks have now written off much of their loss from dot-com investment and must reverse their underinvestment in core businesses.
Make sure that all investments are judged on consistent criteria, and be wary of spending that has been reclassified to make it acceptable
Avoiding mental accounting traps should be easier if you adhere to a basic rule: that every pound (or dollar or euro) is worth exactly that, whatever the category. In this way, you will make sure that all investments are judged on consistent criteria and be wary of spending that has been reclassified. Be particularly skeptical of any investment labeled "strategic."
Flaw 3: The status quo bias
In one classic experiment,9 students were asked how they would invest a hypothetical inheritance. Some received several million dollars in low-risk, low-return bonds and typically chose to leave most of the money alone. The rest received higher-risk securities—and also left most of the money alone. What determined the students’ allocation in this experiment was the initial allocation, not their risk preference. People would rather leave things as they are. One explanation for the status quo bias is aversion to loss—people are more concerned about the risk of loss than they are excited by the prospect of gain. The students’ fear of switching into securities that might end up losing value prevented them from making the rational choice: rebalancing their portfolios.
A similar bias, the endowment effect, gives people a strong desire to hang on to what they own; the very fact of owning something makes it more valuable to the owner. Richard Thaler tested this effect with coffee mugs imprinted with the Cornell University logo. Students given one of them wouldn’t part with it for less than $5.25, on average, but students without a mug wouldn’t pay more than $2.75 to acquire it. The gap implies an incremental value of $2.50 from owning the mug.
The status quo bias, the aversion to loss, and the endowment effect contribute to poor strategy decisions in several ways. First, they make CEOs reluctant to sell businesses. McKinsey research shows that divestments are a major potential source of value creation but a largely neglected one.10 CEOs are prone to ask, "What if we sell for too little—how stupid will we look when this turns out to be a great buy for the acquirer?" Yet successful turnarounds, such as the one at Bankers Trust in the 1980s, often require a determined break with the status quo and an extensive reshaping of the portfolio—in that case, selling all of the bank’s New York retail branches.
These phenomena also make it hard for companies to shift their asset allocations. Before the recent market downturn, the UK insurer Prudential decided that equities were overvalued and made the bold decision to rebalance its fund toward bonds. Many other UK life insurers, unwilling to break with the status quo, stuck with their high equity weightings and have suffered more severe reductions in their solvency ratios.
This isn’t to say that the status quo is always wrong. Many investment advisers would argue that the best long-term strategy is to buy and hold equities (and, behavioral economists would add, not to check their value for many years, to avoid feeling bad when prices fall). In financial services, too, caution and conservatism can be strategic assets. The challenge for strategists is to distinguish between a status quo option that is genuinely the right course and one that feels deceptively safe because of an innate bias.
To make this distinction, strategists should take two approaches:
1.Adopt a radical view of all portfolio decisions. View all businesses as "up for sale." Is the company the natural parent, capable of extracting the most value from a subsidiary? View divestment not as a failure but as a healthy renewal of the corporate portfolio.
2.Subject status quo options to a risk analysis as rigorous as change options receive. Most strategists are good at identifying the risks of new strategies but less good at seeing the risks of failing to change.
Flaw 4: Anchoring
One of the more peculiar wiring flaws in the brain is called anchoring. Present the brain with a number and then ask it to make an estimate of something completely unrelated, and it will anchor its estimate on that first number. The classic illustration is the Genghis Khan date test. Ask a group of people to write down the last three digits of their phone numbers, and then ask them to estimate the date of Genghis Khan’s death. Time and again, the results show a correlation between the two numbers; people assume that he lived in the first millennium, when in fact he lived from 1162 to 1227.
Anchoring can be a powerful tool for strategists. In negotiations, naming a high sale price for a business can help secure an attractive outcome for the seller, as the buyer’s offer will be anchored around that figure. Anchoring works well in advertising too. Most retail-fund managers advertise their funds on the basis of past performance. Repeated studies have failed to show any statistical correlation between good past performance and future performance. By citing the past-performance record, though, the manager anchors the notion of future top-quartile performance to it in the consumer’s mind.
Anchoring can be dangerous—particularly when it is a question of becoming anchored to the past
However, anchoring—particularly becoming anchored to the past—can be dangerous. Most of us have long believed that equities offer high real returns over the long term, an idea anchored in the experience of the past two decades. But in the 1960s and 1970s, UK equities achieved real annual returns of only 3.3 and 0.4 percent, respectively. Indeed, they achieved double-digit real annual returns during only 4 of the past 13 decades. Our expectations about equity returns have been seriously distorted by recent experience.
In the insurance industry, changes in interest rates have caused major problems due to anchoring. The United Kingdom’s Equitable Life Assurance Society assumed that high nominal interest rates would prevail for decades and sold guaranteed annuities accordingly. That assumption had severe financial consequences for the company and its policyholders. The banking industry may now be entering a period of much higher credit losses than it experienced during the past decade. Some banks may be caught out by the speed of change.
Besides remaining unswayed by the anchoring tactics of others, strategists should take a long historical perspective. Put trends in the context of the past 20 or 30 years, not the past 2 or 3; for certain economic indicators, such as equity returns or interest rates, use a very long time series of 50 or 75 years. Some commentators who spotted the dot-com bubble early did so by drawing comparisons with previous technology bubbles—for example, the uncannily close parallels between radio stocks in the 1920s and Internet stocks in the 1990s.
Flaw 5: The sunk-cost effect
A familiar problem with investments is called the sunk-cost effect, otherwise known as "throwing good money after bad." When large projects overrun their schedules and budgets, the original economic case no longer holds, but companies still keep investing to complete them.
Financial institutions often face this dilemma over large-scale IT projects. There are numerous examples, most of which remain private. One of the more public cases was the London Stock Exchange’s automated-settlement system, Taurus. It took the intervention of the Bank of England to force a cancellation, write off the expenses, and take control of building a replacement.
Executives making strategic-investment decisions can also fall into the sunk-cost trap. Certain European banks spent fortunes building up large equities businesses to compete with the global investment-banking firms. It then proved extraordinarily hard for some of these banks to face up to the strategic reality that they had no prospect of ever competing successfully against the likes of Goldman Sachs, Merrill Lynch, and Morgan Stanley in the equities business. Some banks in the United Kingdom took the agonizing decision to write off their investments; other European institutions are still caught in the trap.
Why is it so hard to avoid? One explanation is based on loss aversion: we would rather spend an additional $10 million completing an uneconomic $110 million project than write off $100 million. Another explanation relies on anchoring: once the brain has been anchored at $100 million, an additional $10 million doesn’t seem so bad.
What should strategists do to avoid the trap?
1.Apply the full rigor of investment analysis to incremental investments, looking only at incremental prospective costs and revenues. This is the textbook response to the sunk-cost fallacy, and it is right.
2.Be prepared to kill strategic experiments early. In an increasingly uncertain world, companies will often pursue several strategic options.11 Successfully managing a portfolio of them entails jettisoning the losers. The more quickly you get out, the lower the sunk costs and the easier the exit.
3.Use "gated funding" for strategic investments, much as pharmaceutical companies do for drug development: release follow-on funding only once strategic experiments have met previously agreed targets.
Flaw 6: The herding instinct
The banking industry, like many others, shows a strong herding instinct. It tends to lend too much money to the same kinds of borrowers at the same time—to UK property developers in the 1970s, less-developed countries in the 1980s, and technology, media, and telecommunications companies more recently. And banks tend to pursue the same strategies, be it creating Internet banks with strange-sounding names during the dot-com boom or building integrated investment banks at the time of the "big bang," when the London stock market was liberalized.
This desire to conform to the behavior and opinions of others is a fundamental human trait and an accepted principle of psychology.12 Warren Buffett put his finger on this flaw when he wrote, "Failing conventionally is the route to go; as a group, lemmings may have a rotten image, but no individual lemming has ever received bad press."13 For most CEOs, only one thing is worse than making a huge strategic mistake: being the only person in the industry to make it.
We all felt the tug of the herd during the dot-com era. It was lonely being a Luddite, arguing the case against setting up a stand-alone Internet bank or an on-line brokerage. At times of mass enthusiasm for a strategic trend, pressure to follow the herd rather than rely on one’s own information and analysis is almost irresistible. Yet the best strategies break away from the trend. Some actions may be necessary to match the competition—imagine a bank without ATMs or a good on-line banking offer. But these are not unique sources of strategic advantage, and finding such sources is what strategy is all about. "Me-too" strategies are often simply bad ones.14 Seeking out the new and the unusual should therefore be the strategist’s aim. Rather than copying what your most established competitors are doing, look to the periphery15 for innovative ideas, and look outside your own industry.
Initially, an innovative strategy might draw skepticism from industry experts. They may be right, but as long as you kill a failing strategy early, your losses will be limited, and when they are wrong, the rewards will be great.
Flaw 7: Misestimating future hedonic states
What does it mean, in plain English, to misestimate future hedonic states? Simply that people are bad at estimating how much pleasure or pain they will feel if their circumstances change dramatically. Social scientists have shown that when people undergo major changes in circumstances, their lives typically are neither as bad nor as good as they had expected—another case of how bad we are at estimating. People adjust surprisingly quickly, and their level of pleasure (hedonic state) ends up, broadly, where it was before.
This research strikes a chord with anyone who has studied compensation trends in the investment-banking industry. Ever-higher compensation during the 1990s led only to ever-higher expectations—not to a marked change in the general level of happiness on the Street. According to Tom Wolfe’s Sherman McCoy, in Bonfire of the Vanities, it was hard to make ends meet in New York on $1 million a year in 1987. Back then, that was shocking hubris from a (fictional) top bond salesman. By 2000, even adjusted for inflation, it would have seemed a perfectly reasonable lament from a relatively junior managing director.
Another illustration of our poor ability to judge future hedonic states in the business world is the way we deal with a loss of independence. More often than not, takeovers are seen as the corporate equivalent of death, to be avoided at all costs. Yet sometimes they are the right move. Two once great British banks—Midland and National Westminster—both struggled to maintain their independence. Midland gave in to HSBC’s advances in 1992; NatWest was taken over by the Royal Bank of Scotland in 2000. At both institutions, the consequences were positive for customers, shareholders, and most employees on any test of the "greatest good of the greatest number." The employees ended up being part of better-managed, stronger, more respected institutions. Morale at NatWest has gone up. Midland has achieved what was, for an independent bank, an unrealistic goal: to become part of a great global bank.
Often, top management is blamed for resisting any loss of independence. Certainly part of the problem is the desire of managements and boards to hang on to the status quo. That said, frontline staff members often resist a takeover or merger however much they are frustrated with the existing top management. Some deeper psychological factor appears to be at work. We do seem very bad at estimating how we would feel if our circumstances changed dramatically—changes in corporate control, like changes in our personal health or wealth.
How can the strategist avoid this pitfall?
1.In takeovers, adopt a dispassionate and unemotional view. Easier said than done—especially for a management team with years of committed service to an institution and a personal stake in the status quo. Nonexecutives, however, should find it easier to maintain a detached view.
2.Keep things in perspective. Don’t overreact to apparently deadly strategic threats or get too excited by good news. During the high and low points of the crisis at Lloyd’s of London in the mid-1990s, the chairman used to quote Field Marshall Slim—"In battle nothing is ever as good or as bad as the first reports of excited men would have it." This is a good guide for every strategist trying to navigate a crisis, with the inevitable swings in emotion and morale.
Flaw 8: False consensus
People tend to overestimate the extent to which others share their views, beliefs, and experiences—the false-consensus effect. Research shows many causes, including these:
•confirmation bias, the tendency to seek out opinions and facts that support our own beliefs and hypotheses
•selective recall, the habit of remembering only facts and experiences that reinforce our assumptions
•biased evaluation, the quick acceptance of evidence that supports our hypotheses, while contradictory evidence is subjected to rigorous evaluation and almost certain rejection; we often, for example, impute hostile motives to critics or question their competence
•groupthink,16 the pressure to agree with others in team-based cultures
Consider how many times you may have heard a CEO say something like, "the executive team is 100 percent behind the new strategy" (groupthink); "the chairman and the board are fully supportive and they all agree with our strategy" (false consensus); "I’ve heard only good things from dealers and customers about our new product range" (selective recall); "OK, so some analysts are still negative, but those ’teenage scribblers’ don’t understand our business—their latest reports were superficial and full of errors" (biased evaluation). This hypothetical CEO might be right but more likely is heading for trouble. The role of any strategic adviser should be to provide a counterbalance to this tendency toward false consensus. CEOs should welcome the challenge.
False consensus often leads strategists to overlook important threats to their companies and to persist with doomed strategies
False consensus, which ranks among the brain’s most pernicious flaws, can lead strategists to miss important threats to their companies and to persist with doomed strategies. But it can be extremely difficult to uncover—especially if those proposing a strategy are strong role models. We are easily influenced by dominant individuals and seek to emulate them. This can be a force for good if the role models are positive. But negative ones can prove an irresistible source of strategic error.
Many of the worst financial-services strategies can be attributed to over-dominant individuals. The failure of several Lloyd’s syndicates in the 1980s and 1990s was due to powerful underwriters who controlled their own agencies. And overdominant individuals are associated with several more recent insurance failures. In banking, one European institution struggled to impose effective risk disciplines because its seemingly most successful employees were, in the eyes of junior staff, cavalier in their approach to compliance. Their behavior set the tone and created a culture of noncompliance.
The dangers of false consensus can be minimized in several ways:
1.Create a culture of challenge. As part of the strategic debate, management teams should value open and constructive criticism. Criticizing a fellow director’s strategy should be seen as a helpful, not a hostile, act. CEOs and strategic advisers should understand criticisms of their strategies, seek contrary views on industry trends, and, if in doubt, take steps to assure themselves that opposing views have been well researched. They shouldn’t automatically ascribe to critics bad intentions or a lack of understanding.
2.Ensure that strong checks and balances control the dominant role models. A CEO should be particularly wary of dominant individuals who dismiss challenges to their own strategic proposals; the CEO should insist that these proposals undergo an independent review by respected experts. The board should be equally wary of a domineering CEO.
3.Don’t "lead the witness." Instead of asking for a validation of your strategy, ask for a detailed refutation. When setting up hypotheses at the start of a strategic analysis, impose contrarian hypotheses or require the team to set up equal and opposite hypotheses for each key analysis. Establish a "challenger team" to identify the flaws in the strategy being proposed by the strategy team.
An awareness of the brain’s flaws can help strategists steer around them. All strategists should understand the insights of behavioral economics just as much as they understand those of other fields of the "dismal science." Such an understanding won’t put an end to bad strategy; greed, arrogance, and sloppy analysis will continue to provide plenty of textbook cases of it. Understanding some of the flaws built into our thinking processes, however, may help reduce the chances of good executives backing bad strategies.
About the Author
Charles Roxburgh is a director in McKinsey’s London office
Showing posts with label McKinsey. Show all posts
Showing posts with label McKinsey. Show all posts
Monday, August 6, 2012
Mckinsey:How strategists lead
source: https://www.mckinseyquarterly.com/Strategy/Strategic_Thinking/How_strategists_lead_2993#LettersToTheEditors
How strategists lead
A Harvard Business School professor reflects on what she has learned from senior executives about the unique value that strategic leaders can bring to their companies.
JULY 2012 • Cynthia A. Montgomery
Seven years ago, I changed the focus of my strategy teaching at the Harvard Business School. After instructing MBAs for most of the previous quarter-century, I began teaching the accomplished executives and entrepreneurs who participate in Harvard’s flagship programs for business owners and leaders.
Shifting the center of my teaching to executive education changed the way I teach and write about strategy. I’ve been struck by how often executives, even experienced ones, get tripped up: they become so interested in the potential of new ventures, for example, that they underestimate harsh competitive realities or overlook how interrelated strategy and execution are. I’ve also learned, in conversations between class sessions (as well as in my work as a board director and corporate adviser) about the limits of analysis, the importance of being ready to reinvent a business, and the ongoing responsibility of leading strategy.
All of this learning speaks to the role of the strategist—as a meaning maker for companies, as a voice of reason, and as an operator. The richness of these roles, and their deep interconnections, underscore the fact that strategy is much more than a detached analytical exercise. Analysis has merit, to be sure, but it will never make strategy the vibrant core that animates everything a company is and does.
The strategist as meaning maker
I’ve taken to asking executives to list three words that come to mind when they hear the word strategy. Collectively, they have produced 109 words, frequently giving top billing to plan, direction, and competitive advantage. In more than 2,000 responses, only 2 had anything to do with people: one said leadership, another visionary. No one has ever mentioned strategist.
Downplaying the link between a leader and a strategy, or failing to recognize it at all, is a dangerous oversight that I tried to start remedying in a Harvard Business Review article four years ago and in my new book, The Strategist, whose thinking this article extends.1 After all, defining what an organization will be, and why and to whom that will matter, is at the heart of a leader’s role. Those who hope to sustain a strategic perspective must be ready to confront this basic challenge. It is perhaps easiest to see in single-business companies serving well-defined markets and building business models suited to particular competitive contexts. I know from experience, though, that the challenge is equally relevant at the top of diversified multinationals.
What is it, after all, that makes the whole of a company greater than the sum of its parts—and how do its systems and processes add value to the businesses within the fold? Nobel laureate Ronald Coase posed the problem this way: “The question which arises is whether it is possible to study the forces which determine the size of the firm. Why does the entrepreneur not organize one less transaction or one more?”2 These are largely the same questions: are the extra layers what justifies the existence of this complex firm? If so, why can’t the market take care of such transactions on its own? If there’s more to a company’s story, what is it, really?
In the last three decades, as strategy has moved to become a science, we have allowed these fundamental questions to slip away. We need to bring them back. It is the leader—the strategist as meaning maker—who must make the vital choices that determine a company’s very identity, who says, “This is our purpose, not that. This is who we will be. This is why our customers and clients will prefer a world with us rather than without us.” Others, inside and outside a company, will contribute in meaningful ways, but in the end it is the leader who bears responsibility for the choices that are made and indeed for the fact that choices are made at all.
The strategist as voice of reason
Bold, visionary leaders who have the confidence to take their companies in exciting new directions are widely admired—and confidence is a key part of strategy and leadership. But confidence can balloon into overconfidence, which seems to come naturally to many successful entrepreneurs and senior managers who see themselves as action-oriented problem solvers.3
I see overconfidence in senior executives in class when I ask them to weigh the pros and cons of entering the furniture-manufacturing business. Over the years, a number of highly regarded, well-run companies—including Beatrice Foods, Burlington Industries, Champion, Consolidated Foods, General Housewares, Gulf + Western, Intermark, Ludlow, Masco, Mead, and Scott Paper—have tried to find fortune in the business, which traditionally has been characterized by high transportation costs, low productivity, eroding prices, slow growth, and low returns. It’s also been highly fragmented. In the mid-1980s, for example, more than 2,500 manufacturers competed, with 80 percent of sales coming from the biggest 400 of them. Substitutes abound, and there is a lot of competition for the customer’s dollar. Competitors quickly knock off innovations and new designs, and the industry is riddled with inefficiencies, extreme product variety, and long lead times that frustrate customers. Consumer research shows that many adults can’t name a single furniture brand. The industry does little advertising.
By at least a two-to-one margin, the senior executives in my classes typically are energized, not intimidated, by these challenges. Most argue, in effect, that where there’s challenge there’s opportunity. If it were an easy business, they say, someone else would already have seized the opportunity; this is a chance to bring money, sophistication, and discipline to a fragmented, unsophisticated, and chaotic industry. As the list above shows, my students are far from alone: with great expectations and high hopes of success, a number of well-managed companies over the years have jumped in with the intention of reshaping the industry through the infusion of professional management.
All those companies, though, have since left the business—providing an important reminder that the competitive forces at work in your industry determine some (and perhaps much) of your company’s performance. These competitive forces are beyond the control of most individual companies and their managers. They’re what you inherit, a reality you have to deal with. It’s not that a company can never change them, but in most cases that’s very difficult to do. The strategist must understand such forces, how they affect the playing field where competition takes place, and the likelihood that his or her plan has what it takes to flourish in those circumstances. Crucial, of course, is having a difference that matters in the industry. In furniture—an industry ruled more by fashion than function—it’s extremely challenging to uncover an advantage strong enough to counter the gravitational pull of the industry’s unattractive competitive forces. IKEA did it, but not by disregarding industry forces; rather, the company created a new niche for itself and brought a new economic model to the furniture industry.
A leader must serve as a voice of reason when a bold strategy to reshape an industry’s forces actually reflects indifference to them. Time and again, I’ve seen division heads, group heads, and even chief executives dutifully acknowledge competitive forces, make a few high-level comments, and then quickly move on to lay out their plans—without ever squarely confronting the implications of the forces they’ve just noted. Strategic planning has become more of a “check the box” exercise than a brutally frank and open confrontation of the facts.
The strategist as operator
A great strategy, in short, is not a dream or a lofty idea, but rather the bridge between the economics of a market, the ideas at the core of a business, and action. To be sound, that bridge must rest on a foundation of clarity and realism, and it also needs a real operating sensibility. Every year, early in the term, someone in class always wants to engage the group in a discussion about what’s more important: strategy or execution. In my view, this is a false dichotomy and a wrongheaded debate that the students themselves have to resolve, and I let them have a go at it.
I always bring that discussion up again at the end of the course, when we talk about Domenico De Sole’s tenure at Italian fashion eminence Gucci Group.4 De Sole, a tax attorney, was tapped for the company’s top job in 1995, following years of plummeting sales and mounting losses in the aftermath of unbridled licensing that had plastered Gucci’s name and distinctive red-and-green logo on everything from sneakers to packs of playing cards to whiskey—in fact, on 22,000 different products—making Gucci a “cheapened and over-exposed brand.”
De Sole started by summoning every Gucci manager worldwide to a meeting in Florence. Instead of telling managers what he thought Gucci should be, De Sole asked them to look closely at the business and tell him what was selling and what wasn’t. He wanted to tackle the question “not by philosophy, but by data”—bringing strategy in line with experience rather than relying on intuition. The data were eye opening. Some of Gucci’s greatest recent successes had come from its few trendier, seasonal fashion items, and the traditional customer—the woman who cherished style, not fashion, and who wanted a classic item she would buy once and keep for a lifetime—had not come back to Gucci.
De Sole and his team, especially lead designer Tom Ford, weighed the evidence and concluded that they would follow the data and position the company in the upper middle of the designer market: luxury aimed at the masses. To complement its leather goods, Ford designed original, trendy—and, above all, exciting—ready-to-wear clothing each year, not as the company’s mainstay, but as its draw. The increased focus on fashion would help the world forget all those counterfeit bags and the Gucci toilet paper. It would propel the company toward a new brand identity, generating the kind of excitement that would bring new customers into Gucci stores, where they would also buy high-margin handbags and accessories. To support the new fashion and brand strategies, De Sole and his team doubled advertising spending, modernized stores, and upgraded customer support. Unseen but no less important to the strategy’s success was Gucci’s supply chain. De Sole personally drove the back roads of Tuscany to pick the best 25 suppliers, and the company provided them with financial and technical support while simultaneously boosting the efficiency of its logistics. Costs fell and flexibility rose.
In effect, everything De Sole and Ford did—in design, product lineup, pricing, marketing, distribution, manufacturing, and logistics, not to mention organizational culture and management—was tightly coordinated, internally consistent, and interlocking. This was a system of resources and activities that worked together and reinforced each other, all aimed at producing products that were fashion forward, high quality, and good value.
It is easy to see the beauty of such a system of value creation once it’s constructed, but constructing it isn’t often an easy or a beautiful process. The decisions embedded in such systems are often gutsy choices. For every moving part in the Gucci universe, De Sole faced a strictly binary decision: either it advanced the cause of fashion-forwardness, high quality, and good value—or it did not and was rebuilt. Strategists call such choices identity-conferring commitments. They are central to what an organization is or wants to be and reflect what it stands for.
When I ask executives at the end of this class, “Where does strategy end and execution begin?” there isn’t a clear answer—and that’s as it should be. What could be more desirable than a well-conceived strategy that flows without a ripple into execution? Yet I know from working with thousands of organizations just how rare it is to find a carefully honed system that really delivers. You and every leader of a company must ask yourself whether you have one—and if you don’t, take the responsibility to build it. The only way a company will deliver on its promises, in short, is if its strategists can think like operators.
A never-ending task
Achieving and maintaining strategic momentum is a challenge that confronts an organization and its leader every day of their entwined existence. It’s a challenge that involves multiple choices over time—and, on occasion, one or two big choices. Very rare is the leader who will not, at some point in his or her career, have to overhaul a company’s strategy in perhaps dramatic ways. Sometimes, facing that inevitability brings moments of epiphany: “eureka” flashes of insight that ignite dazzling new ways of thinking about an enterprise, its purpose, its potential. I have witnessed some of these moments as managers reconceptualized what their organizations do and are capable of doing. These episodes are inspiring—and can become catalytic.
At other times, facing an overhaul can be wrenching, particularly if a company has a set of complex businesses that need to be taken apart or a purpose that has run its course. More than one CEO—men and women coming to grips with what their organizations are and what they want them to become—has described this challenge as an intense personal struggle, often the toughest thing they’ve done.
Yet those same people often say that the experience was one of the most rewarding of their whole lives. It can be profoundly liberating as a kind of corporate rebirth or creation. One CEO described his own experience: “I love our business, our people, the challenges, the fact that other people get deep benefits from what we sell,” he said. “Even so, in the coming years I can see that we will need to go in a new direction, and that will mean selling off parts of the business. The market has gotten too competitive, and we don’t make the margins we used to.” He winced as he admitted this. Then he lowered his voice and added something surprising. “At a fundamental level, though, it’s changes like this that keep us fresh and keep me going. While it can be painful when it happens, in the long run I wouldn’t want to lead a company that didn’t reinvent itself.”
About the Author
Cynthia Montgomery is the Timken Professor of Business Administration at Harvard Business School, where she’s been on the faculty for 20 years, and past chair of the school’s Strategy Unit.
Elements of this article were adapted from Cynthia Montgomery’s The Strategist: Be the Leader Your Business Needs (New York, NY: HarperCollins, 2012).
How strategists lead
A Harvard Business School professor reflects on what she has learned from senior executives about the unique value that strategic leaders can bring to their companies.
JULY 2012 • Cynthia A. Montgomery
Seven years ago, I changed the focus of my strategy teaching at the Harvard Business School. After instructing MBAs for most of the previous quarter-century, I began teaching the accomplished executives and entrepreneurs who participate in Harvard’s flagship programs for business owners and leaders.
Shifting the center of my teaching to executive education changed the way I teach and write about strategy. I’ve been struck by how often executives, even experienced ones, get tripped up: they become so interested in the potential of new ventures, for example, that they underestimate harsh competitive realities or overlook how interrelated strategy and execution are. I’ve also learned, in conversations between class sessions (as well as in my work as a board director and corporate adviser) about the limits of analysis, the importance of being ready to reinvent a business, and the ongoing responsibility of leading strategy.
All of this learning speaks to the role of the strategist—as a meaning maker for companies, as a voice of reason, and as an operator. The richness of these roles, and their deep interconnections, underscore the fact that strategy is much more than a detached analytical exercise. Analysis has merit, to be sure, but it will never make strategy the vibrant core that animates everything a company is and does.
The strategist as meaning maker
I’ve taken to asking executives to list three words that come to mind when they hear the word strategy. Collectively, they have produced 109 words, frequently giving top billing to plan, direction, and competitive advantage. In more than 2,000 responses, only 2 had anything to do with people: one said leadership, another visionary. No one has ever mentioned strategist.
Downplaying the link between a leader and a strategy, or failing to recognize it at all, is a dangerous oversight that I tried to start remedying in a Harvard Business Review article four years ago and in my new book, The Strategist, whose thinking this article extends.1 After all, defining what an organization will be, and why and to whom that will matter, is at the heart of a leader’s role. Those who hope to sustain a strategic perspective must be ready to confront this basic challenge. It is perhaps easiest to see in single-business companies serving well-defined markets and building business models suited to particular competitive contexts. I know from experience, though, that the challenge is equally relevant at the top of diversified multinationals.
What is it, after all, that makes the whole of a company greater than the sum of its parts—and how do its systems and processes add value to the businesses within the fold? Nobel laureate Ronald Coase posed the problem this way: “The question which arises is whether it is possible to study the forces which determine the size of the firm. Why does the entrepreneur not organize one less transaction or one more?”2 These are largely the same questions: are the extra layers what justifies the existence of this complex firm? If so, why can’t the market take care of such transactions on its own? If there’s more to a company’s story, what is it, really?
In the last three decades, as strategy has moved to become a science, we have allowed these fundamental questions to slip away. We need to bring them back. It is the leader—the strategist as meaning maker—who must make the vital choices that determine a company’s very identity, who says, “This is our purpose, not that. This is who we will be. This is why our customers and clients will prefer a world with us rather than without us.” Others, inside and outside a company, will contribute in meaningful ways, but in the end it is the leader who bears responsibility for the choices that are made and indeed for the fact that choices are made at all.
The strategist as voice of reason
Bold, visionary leaders who have the confidence to take their companies in exciting new directions are widely admired—and confidence is a key part of strategy and leadership. But confidence can balloon into overconfidence, which seems to come naturally to many successful entrepreneurs and senior managers who see themselves as action-oriented problem solvers.3
I see overconfidence in senior executives in class when I ask them to weigh the pros and cons of entering the furniture-manufacturing business. Over the years, a number of highly regarded, well-run companies—including Beatrice Foods, Burlington Industries, Champion, Consolidated Foods, General Housewares, Gulf + Western, Intermark, Ludlow, Masco, Mead, and Scott Paper—have tried to find fortune in the business, which traditionally has been characterized by high transportation costs, low productivity, eroding prices, slow growth, and low returns. It’s also been highly fragmented. In the mid-1980s, for example, more than 2,500 manufacturers competed, with 80 percent of sales coming from the biggest 400 of them. Substitutes abound, and there is a lot of competition for the customer’s dollar. Competitors quickly knock off innovations and new designs, and the industry is riddled with inefficiencies, extreme product variety, and long lead times that frustrate customers. Consumer research shows that many adults can’t name a single furniture brand. The industry does little advertising.
By at least a two-to-one margin, the senior executives in my classes typically are energized, not intimidated, by these challenges. Most argue, in effect, that where there’s challenge there’s opportunity. If it were an easy business, they say, someone else would already have seized the opportunity; this is a chance to bring money, sophistication, and discipline to a fragmented, unsophisticated, and chaotic industry. As the list above shows, my students are far from alone: with great expectations and high hopes of success, a number of well-managed companies over the years have jumped in with the intention of reshaping the industry through the infusion of professional management.
All those companies, though, have since left the business—providing an important reminder that the competitive forces at work in your industry determine some (and perhaps much) of your company’s performance. These competitive forces are beyond the control of most individual companies and their managers. They’re what you inherit, a reality you have to deal with. It’s not that a company can never change them, but in most cases that’s very difficult to do. The strategist must understand such forces, how they affect the playing field where competition takes place, and the likelihood that his or her plan has what it takes to flourish in those circumstances. Crucial, of course, is having a difference that matters in the industry. In furniture—an industry ruled more by fashion than function—it’s extremely challenging to uncover an advantage strong enough to counter the gravitational pull of the industry’s unattractive competitive forces. IKEA did it, but not by disregarding industry forces; rather, the company created a new niche for itself and brought a new economic model to the furniture industry.
A leader must serve as a voice of reason when a bold strategy to reshape an industry’s forces actually reflects indifference to them. Time and again, I’ve seen division heads, group heads, and even chief executives dutifully acknowledge competitive forces, make a few high-level comments, and then quickly move on to lay out their plans—without ever squarely confronting the implications of the forces they’ve just noted. Strategic planning has become more of a “check the box” exercise than a brutally frank and open confrontation of the facts.
The strategist as operator
A great strategy, in short, is not a dream or a lofty idea, but rather the bridge between the economics of a market, the ideas at the core of a business, and action. To be sound, that bridge must rest on a foundation of clarity and realism, and it also needs a real operating sensibility. Every year, early in the term, someone in class always wants to engage the group in a discussion about what’s more important: strategy or execution. In my view, this is a false dichotomy and a wrongheaded debate that the students themselves have to resolve, and I let them have a go at it.
I always bring that discussion up again at the end of the course, when we talk about Domenico De Sole’s tenure at Italian fashion eminence Gucci Group.4 De Sole, a tax attorney, was tapped for the company’s top job in 1995, following years of plummeting sales and mounting losses in the aftermath of unbridled licensing that had plastered Gucci’s name and distinctive red-and-green logo on everything from sneakers to packs of playing cards to whiskey—in fact, on 22,000 different products—making Gucci a “cheapened and over-exposed brand.”
De Sole started by summoning every Gucci manager worldwide to a meeting in Florence. Instead of telling managers what he thought Gucci should be, De Sole asked them to look closely at the business and tell him what was selling and what wasn’t. He wanted to tackle the question “not by philosophy, but by data”—bringing strategy in line with experience rather than relying on intuition. The data were eye opening. Some of Gucci’s greatest recent successes had come from its few trendier, seasonal fashion items, and the traditional customer—the woman who cherished style, not fashion, and who wanted a classic item she would buy once and keep for a lifetime—had not come back to Gucci.
De Sole and his team, especially lead designer Tom Ford, weighed the evidence and concluded that they would follow the data and position the company in the upper middle of the designer market: luxury aimed at the masses. To complement its leather goods, Ford designed original, trendy—and, above all, exciting—ready-to-wear clothing each year, not as the company’s mainstay, but as its draw. The increased focus on fashion would help the world forget all those counterfeit bags and the Gucci toilet paper. It would propel the company toward a new brand identity, generating the kind of excitement that would bring new customers into Gucci stores, where they would also buy high-margin handbags and accessories. To support the new fashion and brand strategies, De Sole and his team doubled advertising spending, modernized stores, and upgraded customer support. Unseen but no less important to the strategy’s success was Gucci’s supply chain. De Sole personally drove the back roads of Tuscany to pick the best 25 suppliers, and the company provided them with financial and technical support while simultaneously boosting the efficiency of its logistics. Costs fell and flexibility rose.
In effect, everything De Sole and Ford did—in design, product lineup, pricing, marketing, distribution, manufacturing, and logistics, not to mention organizational culture and management—was tightly coordinated, internally consistent, and interlocking. This was a system of resources and activities that worked together and reinforced each other, all aimed at producing products that were fashion forward, high quality, and good value.
It is easy to see the beauty of such a system of value creation once it’s constructed, but constructing it isn’t often an easy or a beautiful process. The decisions embedded in such systems are often gutsy choices. For every moving part in the Gucci universe, De Sole faced a strictly binary decision: either it advanced the cause of fashion-forwardness, high quality, and good value—or it did not and was rebuilt. Strategists call such choices identity-conferring commitments. They are central to what an organization is or wants to be and reflect what it stands for.
When I ask executives at the end of this class, “Where does strategy end and execution begin?” there isn’t a clear answer—and that’s as it should be. What could be more desirable than a well-conceived strategy that flows without a ripple into execution? Yet I know from working with thousands of organizations just how rare it is to find a carefully honed system that really delivers. You and every leader of a company must ask yourself whether you have one—and if you don’t, take the responsibility to build it. The only way a company will deliver on its promises, in short, is if its strategists can think like operators.
A never-ending task
Achieving and maintaining strategic momentum is a challenge that confronts an organization and its leader every day of their entwined existence. It’s a challenge that involves multiple choices over time—and, on occasion, one or two big choices. Very rare is the leader who will not, at some point in his or her career, have to overhaul a company’s strategy in perhaps dramatic ways. Sometimes, facing that inevitability brings moments of epiphany: “eureka” flashes of insight that ignite dazzling new ways of thinking about an enterprise, its purpose, its potential. I have witnessed some of these moments as managers reconceptualized what their organizations do and are capable of doing. These episodes are inspiring—and can become catalytic.
At other times, facing an overhaul can be wrenching, particularly if a company has a set of complex businesses that need to be taken apart or a purpose that has run its course. More than one CEO—men and women coming to grips with what their organizations are and what they want them to become—has described this challenge as an intense personal struggle, often the toughest thing they’ve done.
Yet those same people often say that the experience was one of the most rewarding of their whole lives. It can be profoundly liberating as a kind of corporate rebirth or creation. One CEO described his own experience: “I love our business, our people, the challenges, the fact that other people get deep benefits from what we sell,” he said. “Even so, in the coming years I can see that we will need to go in a new direction, and that will mean selling off parts of the business. The market has gotten too competitive, and we don’t make the margins we used to.” He winced as he admitted this. Then he lowered his voice and added something surprising. “At a fundamental level, though, it’s changes like this that keep us fresh and keep me going. While it can be painful when it happens, in the long run I wouldn’t want to lead a company that didn’t reinvent itself.”
About the Author
Cynthia Montgomery is the Timken Professor of Business Administration at Harvard Business School, where she’s been on the faculty for 20 years, and past chair of the school’s Strategy Unit.
Elements of this article were adapted from Cynthia Montgomery’s The Strategist: Be the Leader Your Business Needs (New York, NY: HarperCollins, 2012).
Friday, October 14, 2011
Are you ready for the era of ‘big data’?
from https://www.mckinseyquarterly.com/home.aspx
Are you ready for the era of ‘big data’?
Radical customization, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyze huge volumes of data. Here’s what you should know.
OCTOBER 2011 • Brad Brown, Michael Chui, and James Manyika
Source: McKinsey Global Institute
In This Article
Sidebar: Parsing the benefits: Not all industries are created equal
Sidebar Exhibit: The ease of capturing big data’s value, and the magnitude of its potential, vary across sectors. .About the authors
Comments
..The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground.
When the executive convened a group of senior leaders to dig into the competitor’s practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization—from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business.
What this executive team had witnessed first hand was the game-changing effects of big data. Of course, data characterized the information age from the start. It underpins processes that manage employees; it helps to track purchases and sales; and it offers clues about how customers will behave.
But over the last few years, the volume of data has exploded. In 15 of the US economy’s 17 sectors, companies with more than 1,000 employees store, on average, over 235 terabytes of data—more data than is contained in the US Library of Congress. Reams of data still flow from financial transactions and customer interactions but also cascade in at unparalleled rates from new devices and multiple points along the value chain. Just think about what could be happening at your own company right now: sensors embedded in process machinery may be collecting operations data, while marketers scan social media or use location data from smartphones to understand teens’ buying quirks. Data exchanges may be networking your supply chain partners, and employees could be swapping best practices on corporate wikis.
All of this new information is laden with implications for leaders and their enterprises.1 Emerging academic research suggests that companies that use data and business analytics to guide decision making are more productive and experience higher returns on equity than competitors that don’t.2 That’s consistent with research we’ve conducted showing that “networked organizations” can gain an edge by opening information conduits internally and by engaging customers and suppliers strategically through Web-based exchanges of information.3
Over time, we believe big data may well become a new type of corporate asset that will cut across business units and function much as a powerful brand does, representing a key basis for competition. If that’s right, companies need to start thinking in earnest about whether they are organized to exploit big data’s potential and to manage the threats it can pose. Success will demand not only new skills but also new perspectives on how the era of big data could evolve—the widening circle of management practices it may affect and the foundation it represents for new, potentially disruptive business models.
Five big questions about big data
In the remainder of this article, we outline important ways big data could change competition: by transforming processes, altering corporate ecosystems, and facilitating innovation. We’ve organized the discussion around five questions we think all senior executives should be asking themselves today.
At the outset, we’ll acknowledge that these are still early days for big data, which is evolving as a business concept in tandem with the underlying technologies. Nonetheless, we can identify big data’s key elements. First, companies can now collect data across business units and, increasingly, even from partners and customers (some of this is truly big, some more granular and complex). Second, a flexible infrastructure can integrate information and scale up effectively to meet the surge. Finally, experiments, algorithms, and analytics can make sense of all this information. We also can identify organizations that are making data a core element of strategy. In the discussion that follows and elsewhere in this issue, we have assembled case studies of early movers in the big data realm (see “Seizing the potential of ‘big data’” and the accompanying sidebar, “AstraZeneca’s ‘big data’ partnership,” forthcoming on mckinseyquarterly.com in mid-October 2011).
Toggle Sidebar
Parsing the benefits: Not all industries are created equal
Even as big data changes the game for virtually every sector, it also tilts the playing field, favoring some companies and industries, particularly in the early stages of adoption. To understand those dynamics, we examined 20 sectors in the US economy, sized their contributions to GDP, and developed two indexes that estimate each sector’s potential for value creation using big data, as well as the ease of capturing that value.1
As the accompanying sector map shows (exhibit), financial players get the highest marks for value creation opportunities. Many of these companies have invested deeply in IT and have large data pools to exploit. Information industries, not surprisingly, are also in this league. They are data intensive by nature, and they use that data innovatively to compete by adopting sophisticated analytic techniques.
Back to topThe public sector is the most fertile terrain for change. Governments collect huge amounts of data, transact business with millions of citizens, and, more often than not, suffer from highly variable performance. While potential benefits are large, governments face steep barriers to making use of this trove: few managers are pushed to exploit the data they have, and government departments often keep data in siloes.
Fragmented industry structures complicate the value creation potential of sectors such as health care, manufacturing, and retailing. The average company in them is relatively small and can access only limited amounts of data. Larger players, however, usually swim in bigger pools of data, which they can more readily use to create value.
The US health care sector, for example, is dotted by many small companies and individual physicians’ practices. Large hospital chains, national insurers, and drug manufacturers, by contrast, stand to gain substantially through the pooling and more effective analysis of data. We expect this trend to intensify with changing regulatory and market conditions. In manufacturing, too, larger companies with access to much internal and market data will be able to mine new reservoirs of value. Smaller players are likely to benefit only if they discover innovative ways to share data or grow through industry consolidation. The same goes for retailing, where—despite a healthy strata of data-rich chains and big-box stores on the cutting edge of big data—most players are smaller, local businesses with a limited ability to gather and analyze information.
A final note: this analysis is a snapshot in time for one large country. As companies and organizations sharpen their data skills, even low-ranking sectors (by our gauges of value potential and data capture), such as construction and education, could see their fortunes change.
Notes1 The big data value potential index takes into account a sector’s competitive conditions, such as market turbulence and performance variability; structural factors, such as transaction intensity and the number of potential customers and business partners; and the quantity of data available. The ease-of-capture index takes stock of the number of employees with deep analytical talent in an industry, baseline investments in IT, the accessibility of data sources, and the degree to which managers make data-driven decisions.
Back to topIn addition, we’d suggest that executives look to history for clues about what’s coming next. Earlier waves of technology adoption, for example, show that productivity surges not only because companies adopt new technologies but also, more critically, because they can adapt their management practices and change their organizations to maximize the potential. We examined the possible impact of big data across a number of industries and found that while it will be important in every sector and function, some industries will realize benefits sooner because they are more ready to capitalize on data or have strong market incentives to do so (see sidebar, “Parsing the benefits: Not all industries are created equal”).
The era of big data also could yield new management principles. In the early days of professionalized corporate management, leaders discovered that minimum efficient scale was a key determinant of competitive success. Likewise, future competitive benefits may accrue to companies that can not only capture more and better data but also use that data effectively at scale. We hope that by reflecting on such issues and the five questions that follow, executives will be better able to recognize how big data could upend assumptions behind their strategies, as well as the speed and scope of the change that’s now under way.
1. What happens in a world of radical transparency, with data widely available?
As information becomes more readily accessible across sectors, it can threaten companies that have relied on proprietary data as a competitive asset. The real-estate industry, for example, trades on information asymmetries such as privileged access to transaction data and tightly held knowledge of the bid and ask behavior of buyers. Both require significant expense and effort to acquire. In recent years, however, online specialists in real-estate data and analytics have started to bypass agents, permitting buyers and sellers to exchange perspectives on the value of properties and creating parallel sources for real-estate data.
Beyond real estate, cost and pricing data are becoming more accessible across a spectrum of industries. Another swipe at proprietary information is the assembly by some companies of readily available satellite imagery that, when processed and analyzed, contains clues about competitors’ physical facilities. These satellite sleuths glean insights into expansion plans or business constraints as revealed by facility capacity, shipping movements, and the like.
One big challenge is the fact that the mountains of data many companies are amassing often lurk in departmental “silos,” such as R&D, engineering, manufacturing, or service operations—impeding timely exploitation. Information hoarding within business units also can be a problem: many financial institutions, for example, suffer from their own failure to share data among diverse lines of business, such as financial markets, money management, and lending. Often, that prevents these companies from forming a coherent view of individual customers or understanding links among financial markets.
Some manufacturers are attempting to pry open these departmental enclaves: they are integrating data from multiple systems, inviting collaboration among formerly walled-off functional units, and even seeking information from external suppliers and customers to cocreate products. In advanced-manufacturing sectors such as automotive, for example, suppliers from around the world make thousands of components. More integrated data platforms now allow companies and their supply chain partners to collaborate during the design phase—a crucial determinant of final manufacturing costs.
2. If you could test all of your decisions, how would that change the way you compete?
Big data ushers in the possibility of a fundamentally different type of decision making. Using controlled experiments, companies can test hypotheses and analyze results to guide investment decisions and operational changes. In effect, experimentation can help managers distinguish causation from mere correlation, thus reducing the variability of outcomes while improving financial and product performance.
Robust experimentation can take many forms. Leading online companies, for example, are continuous testers. In some cases, they allocate a set portion of their Web page views to conduct experiments that reveal what factors drive higher user engagement or promote sales. Companies selling physical goods also use experiments to aid decisions, but big data can push this approach to a new level. McDonald’s, for example, has equipped some stores with devices that gather operational data as they track customer interactions, traffic in stores, and ordering patterns. Researchers can model the impact of variations in menus, restaurant designs, and training, among other things, on productivity and sales.
Where such controlled experiments aren’t feasible, companies can use “natural” experiments to identify the sources of variability in performance. One government organization, for instance, collected data on multiple groups of employees doing similar work at different sites. Simply making the data available spurred lagging workers to improve their performance.
Leading retailers, meanwhile, are monitoring the in-store movements of customers, as well as how they interact with products. These retailers combine such rich data feeds with transaction records and conduct experiments to guide choices about which products to carry, where to place them, and how and when to adjust prices. Methods such as these helped one leading retailer to reduce the number of items it stocked by 17 percent, while raising the mix of higher-margin private-label goods—with no loss of market share.
3. How would your business change if you used big data for widespread, real-time customization?
Customer-facing companies have long used data to segment and target customers. Big data permits a major step beyond what until recently was considered state of the art, by making real-time personalization possible. A next-generation retailer will be able to track the behavior of individual customers from Internet click streams, update their preferences, and model their likely behavior in real time. They will then be able to recognize when customers are nearing a purchase decision and nudge the transaction to completion by bundling preferred products, offered with reward program savings. This real-time targeting, which would also leverage data from the retailer’s multitier membership rewards program, will increase purchases of higher-margin products by its most valuable customers.
Retailing is an obvious place for data-driven customization because the volume and quality of data available from Internet purchases, social-network conversations, and, more recently, location-specific smartphone interactions have mushroomed. But other sectors, too, can benefit from new applications of data, along with the growing sophistication of analytical tools for dividing customers into more revealing microsegments.
One personal-line insurer, for example, tailors insurance policies for each customer, using fine-grained, constantly updated profiles of customer risk, changes in wealth, home asset value, and other data inputs. Utilities that harvest and analyze data on customer segments can markedly change patterns of power usage. Finally, HR departments that more finely segment employees by task and performance are beginning to change work conditions and implement incentives that improve both satisfaction and productivity.4
4. How can big data augment or even replace management?
Big data expands the operational space for algorithms and machine-mediated analysis. At some manufacturers, for example, algorithms analyze sensor data from production lines, creating self-regulating processes that cut waste, avoid costly (and sometimes dangerous) human interventions, and ultimately lift output. In advanced, “digital” oil fields, instruments constantly read data on wellhead conditions, pipelines, and mechanical systems. That information is analyzed by clusters of computers, which feed their results to real-time operations centers that adjust oil flows to optimize production and minimize downtimes. One major oil company has cut operating and staffing costs by 10 to 25 percent while increasing production by 5 percent.
Products ranging from copiers to jet engines can now generate data streams that track their usage. Manufacturers can analyze the incoming data and, in some cases, automatically remedy software glitches or dispatch service representatives for repairs. Some enterprise computer hardware vendors are gathering and analyzing such data to schedule preemptive repairs before failures disrupt customers’ operations. The data can also be used to implement product changes that prevent future problems or to provide customer use inputs that inform next-generation offerings.
Some retailers are also at the forefront of using automated big data analysis: they use “sentiment analysis” techniques to mine the huge streams of data now generated by consumers using various types of social media, gauge responses to new marketing campaigns in real time, and adjust strategies accordingly. Sometimes these methods cut weeks from the normal feedback and modification cycle.
But retailers aren’t alone. One global beverage company integrates daily weather forecast data from an outside partner into its demand and inventory-planning processes. By analyzing three data points—temperatures, rainfall levels, and the number of hours of sunshine on a given day—the company cut its inventory levels while improving its forecasting accuracy by about 5 percent in a key European market.
The bottom line is improved performance, better risk management, and the ability to unearth insights that would otherwise remain hidden. As the price of sensors, communications devices, and analytic software continues to fall, more and more companies will be joining this managerial revolution.
5. Could you create a new business model based on data?
Big data is spawning new categories of companies that embrace information-driven business models. Many of these businesses play intermediary roles in value chains where they find themselves generating valuable “exhaust data” produced by business transactions. One transport company, for example, recognized that in the course of doing business, it was collecting vast amounts of information on global product shipments. Sensing opportunity, it created a unit that sells the data to supplement business and economic forecasts.
Another global company learned so much from analyzing its own data as part of a manufacturing turnaround that it decided to create a business to do similar work for other firms. Now the company aggregates shop floor and supply chain data for a number of manufacturing customers and sells software tools to improve their performance. This service business now outperforms the company’s manufacturing one.
Big data also is turbocharging the ranks of data aggregators, which combine and analyze information from multiple sources to generate insights for clients. In health care, for example, a number of new entrants are integrating clinical, payment, public-health, and behavioral data to develop more robust illness profiles that help clients manage costs and improve treatments.
And with pricing data proliferating on the Web and elsewhere, entrepreneurs are offering price comparison services that automatically compile information across millions of products. Such comparisons can be a disruptive force from a retailer’s perspective but have created substantial value for consumers. Studies show that those who use the services save an average of 10 percent—a sizable shift in value.
Confronting complications
Up to this point, we have emphasized the strategic opportunities big data presents, but leaders must also consider a set of complications. Talent is one of them. In the United States alone, our research shows, the demand for people with the deep analytical skills in big data (including machine learning and advanced statistical analysis) could outstrip current projections of supply by 50 to 60 percent. By 2018, as many as 140,000 to 190,000 additional specialists may be required. Also needed: an additional 1.5 million managers and analysts with a sharp understanding of how big data can be applied. Companies must step up their recruitment and retention programs, while making substantial investments in the education and training of key data personnel.
The greater access to personal information that big data often demands will place a spotlight on another tension, between privacy and convenience. Our research, for example, shows that consumers capture a large part of the economic surplus that big data generates: lower prices, a better alignment of products with consumer needs, and lifestyle improvements that range from better health to more fluid social interactions.5 As a larger amount of data on the buying preferences, health, and finances of individuals is collected, however, privacy concerns will grow.
That’s true for data security as well. The trends we’ve described often go hand in hand with more open access to information, new devices for gathering it, and cloud computing to support big data’s weighty storage and analytical needs. The implication is that IT architectures will become more integrated and outward facing and will pose greater risks to data security and intellectual property. For some ideas on how leaders should respond, see “Meeting the cybersecurity challenge.”
Although corporate leaders will focus most of their attention on big data’s implications for their own organizations, the mosaic of company-level opportunities we have surveyed also has broader economic implications. In health care, government services, retailing, and manufacturing, our research suggests, big data could improve productivity by 0.5 to 1 percent annually. In these sectors globally, it could produce hundreds of billions of dollars and euros in new value.
In fact, big data may ultimately be a key factor in how nations, not just companies, compete and prosper. Certainly, these techniques offer glimmers of hope to a global economy struggling to find a path toward more rapid growth. Through investments and forward-looking policies, company leaders and their counterparts in government can capitalize on big data instead of being blindsided by it.
Are you ready for the era of ‘big data’?
Radical customization, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyze huge volumes of data. Here’s what you should know.
OCTOBER 2011 • Brad Brown, Michael Chui, and James Manyika
Source: McKinsey Global Institute
In This Article
Sidebar: Parsing the benefits: Not all industries are created equal
Sidebar Exhibit: The ease of capturing big data’s value, and the magnitude of its potential, vary across sectors. .About the authors
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..The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground.
When the executive convened a group of senior leaders to dig into the competitor’s practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization—from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business.
What this executive team had witnessed first hand was the game-changing effects of big data. Of course, data characterized the information age from the start. It underpins processes that manage employees; it helps to track purchases and sales; and it offers clues about how customers will behave.
But over the last few years, the volume of data has exploded. In 15 of the US economy’s 17 sectors, companies with more than 1,000 employees store, on average, over 235 terabytes of data—more data than is contained in the US Library of Congress. Reams of data still flow from financial transactions and customer interactions but also cascade in at unparalleled rates from new devices and multiple points along the value chain. Just think about what could be happening at your own company right now: sensors embedded in process machinery may be collecting operations data, while marketers scan social media or use location data from smartphones to understand teens’ buying quirks. Data exchanges may be networking your supply chain partners, and employees could be swapping best practices on corporate wikis.
All of this new information is laden with implications for leaders and their enterprises.1 Emerging academic research suggests that companies that use data and business analytics to guide decision making are more productive and experience higher returns on equity than competitors that don’t.2 That’s consistent with research we’ve conducted showing that “networked organizations” can gain an edge by opening information conduits internally and by engaging customers and suppliers strategically through Web-based exchanges of information.3
Over time, we believe big data may well become a new type of corporate asset that will cut across business units and function much as a powerful brand does, representing a key basis for competition. If that’s right, companies need to start thinking in earnest about whether they are organized to exploit big data’s potential and to manage the threats it can pose. Success will demand not only new skills but also new perspectives on how the era of big data could evolve—the widening circle of management practices it may affect and the foundation it represents for new, potentially disruptive business models.
Five big questions about big data
In the remainder of this article, we outline important ways big data could change competition: by transforming processes, altering corporate ecosystems, and facilitating innovation. We’ve organized the discussion around five questions we think all senior executives should be asking themselves today.
At the outset, we’ll acknowledge that these are still early days for big data, which is evolving as a business concept in tandem with the underlying technologies. Nonetheless, we can identify big data’s key elements. First, companies can now collect data across business units and, increasingly, even from partners and customers (some of this is truly big, some more granular and complex). Second, a flexible infrastructure can integrate information and scale up effectively to meet the surge. Finally, experiments, algorithms, and analytics can make sense of all this information. We also can identify organizations that are making data a core element of strategy. In the discussion that follows and elsewhere in this issue, we have assembled case studies of early movers in the big data realm (see “Seizing the potential of ‘big data’” and the accompanying sidebar, “AstraZeneca’s ‘big data’ partnership,” forthcoming on mckinseyquarterly.com in mid-October 2011).
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Parsing the benefits: Not all industries are created equal
Even as big data changes the game for virtually every sector, it also tilts the playing field, favoring some companies and industries, particularly in the early stages of adoption. To understand those dynamics, we examined 20 sectors in the US economy, sized their contributions to GDP, and developed two indexes that estimate each sector’s potential for value creation using big data, as well as the ease of capturing that value.1
As the accompanying sector map shows (exhibit), financial players get the highest marks for value creation opportunities. Many of these companies have invested deeply in IT and have large data pools to exploit. Information industries, not surprisingly, are also in this league. They are data intensive by nature, and they use that data innovatively to compete by adopting sophisticated analytic techniques.
Back to topThe public sector is the most fertile terrain for change. Governments collect huge amounts of data, transact business with millions of citizens, and, more often than not, suffer from highly variable performance. While potential benefits are large, governments face steep barriers to making use of this trove: few managers are pushed to exploit the data they have, and government departments often keep data in siloes.
Fragmented industry structures complicate the value creation potential of sectors such as health care, manufacturing, and retailing. The average company in them is relatively small and can access only limited amounts of data. Larger players, however, usually swim in bigger pools of data, which they can more readily use to create value.
The US health care sector, for example, is dotted by many small companies and individual physicians’ practices. Large hospital chains, national insurers, and drug manufacturers, by contrast, stand to gain substantially through the pooling and more effective analysis of data. We expect this trend to intensify with changing regulatory and market conditions. In manufacturing, too, larger companies with access to much internal and market data will be able to mine new reservoirs of value. Smaller players are likely to benefit only if they discover innovative ways to share data or grow through industry consolidation. The same goes for retailing, where—despite a healthy strata of data-rich chains and big-box stores on the cutting edge of big data—most players are smaller, local businesses with a limited ability to gather and analyze information.
A final note: this analysis is a snapshot in time for one large country. As companies and organizations sharpen their data skills, even low-ranking sectors (by our gauges of value potential and data capture), such as construction and education, could see their fortunes change.
Notes1 The big data value potential index takes into account a sector’s competitive conditions, such as market turbulence and performance variability; structural factors, such as transaction intensity and the number of potential customers and business partners; and the quantity of data available. The ease-of-capture index takes stock of the number of employees with deep analytical talent in an industry, baseline investments in IT, the accessibility of data sources, and the degree to which managers make data-driven decisions.
Back to topIn addition, we’d suggest that executives look to history for clues about what’s coming next. Earlier waves of technology adoption, for example, show that productivity surges not only because companies adopt new technologies but also, more critically, because they can adapt their management practices and change their organizations to maximize the potential. We examined the possible impact of big data across a number of industries and found that while it will be important in every sector and function, some industries will realize benefits sooner because they are more ready to capitalize on data or have strong market incentives to do so (see sidebar, “Parsing the benefits: Not all industries are created equal”).
The era of big data also could yield new management principles. In the early days of professionalized corporate management, leaders discovered that minimum efficient scale was a key determinant of competitive success. Likewise, future competitive benefits may accrue to companies that can not only capture more and better data but also use that data effectively at scale. We hope that by reflecting on such issues and the five questions that follow, executives will be better able to recognize how big data could upend assumptions behind their strategies, as well as the speed and scope of the change that’s now under way.
1. What happens in a world of radical transparency, with data widely available?
As information becomes more readily accessible across sectors, it can threaten companies that have relied on proprietary data as a competitive asset. The real-estate industry, for example, trades on information asymmetries such as privileged access to transaction data and tightly held knowledge of the bid and ask behavior of buyers. Both require significant expense and effort to acquire. In recent years, however, online specialists in real-estate data and analytics have started to bypass agents, permitting buyers and sellers to exchange perspectives on the value of properties and creating parallel sources for real-estate data.
Beyond real estate, cost and pricing data are becoming more accessible across a spectrum of industries. Another swipe at proprietary information is the assembly by some companies of readily available satellite imagery that, when processed and analyzed, contains clues about competitors’ physical facilities. These satellite sleuths glean insights into expansion plans or business constraints as revealed by facility capacity, shipping movements, and the like.
One big challenge is the fact that the mountains of data many companies are amassing often lurk in departmental “silos,” such as R&D, engineering, manufacturing, or service operations—impeding timely exploitation. Information hoarding within business units also can be a problem: many financial institutions, for example, suffer from their own failure to share data among diverse lines of business, such as financial markets, money management, and lending. Often, that prevents these companies from forming a coherent view of individual customers or understanding links among financial markets.
Some manufacturers are attempting to pry open these departmental enclaves: they are integrating data from multiple systems, inviting collaboration among formerly walled-off functional units, and even seeking information from external suppliers and customers to cocreate products. In advanced-manufacturing sectors such as automotive, for example, suppliers from around the world make thousands of components. More integrated data platforms now allow companies and their supply chain partners to collaborate during the design phase—a crucial determinant of final manufacturing costs.
2. If you could test all of your decisions, how would that change the way you compete?
Big data ushers in the possibility of a fundamentally different type of decision making. Using controlled experiments, companies can test hypotheses and analyze results to guide investment decisions and operational changes. In effect, experimentation can help managers distinguish causation from mere correlation, thus reducing the variability of outcomes while improving financial and product performance.
Robust experimentation can take many forms. Leading online companies, for example, are continuous testers. In some cases, they allocate a set portion of their Web page views to conduct experiments that reveal what factors drive higher user engagement or promote sales. Companies selling physical goods also use experiments to aid decisions, but big data can push this approach to a new level. McDonald’s, for example, has equipped some stores with devices that gather operational data as they track customer interactions, traffic in stores, and ordering patterns. Researchers can model the impact of variations in menus, restaurant designs, and training, among other things, on productivity and sales.
Where such controlled experiments aren’t feasible, companies can use “natural” experiments to identify the sources of variability in performance. One government organization, for instance, collected data on multiple groups of employees doing similar work at different sites. Simply making the data available spurred lagging workers to improve their performance.
Leading retailers, meanwhile, are monitoring the in-store movements of customers, as well as how they interact with products. These retailers combine such rich data feeds with transaction records and conduct experiments to guide choices about which products to carry, where to place them, and how and when to adjust prices. Methods such as these helped one leading retailer to reduce the number of items it stocked by 17 percent, while raising the mix of higher-margin private-label goods—with no loss of market share.
3. How would your business change if you used big data for widespread, real-time customization?
Customer-facing companies have long used data to segment and target customers. Big data permits a major step beyond what until recently was considered state of the art, by making real-time personalization possible. A next-generation retailer will be able to track the behavior of individual customers from Internet click streams, update their preferences, and model their likely behavior in real time. They will then be able to recognize when customers are nearing a purchase decision and nudge the transaction to completion by bundling preferred products, offered with reward program savings. This real-time targeting, which would also leverage data from the retailer’s multitier membership rewards program, will increase purchases of higher-margin products by its most valuable customers.
Retailing is an obvious place for data-driven customization because the volume and quality of data available from Internet purchases, social-network conversations, and, more recently, location-specific smartphone interactions have mushroomed. But other sectors, too, can benefit from new applications of data, along with the growing sophistication of analytical tools for dividing customers into more revealing microsegments.
One personal-line insurer, for example, tailors insurance policies for each customer, using fine-grained, constantly updated profiles of customer risk, changes in wealth, home asset value, and other data inputs. Utilities that harvest and analyze data on customer segments can markedly change patterns of power usage. Finally, HR departments that more finely segment employees by task and performance are beginning to change work conditions and implement incentives that improve both satisfaction and productivity.4
4. How can big data augment or even replace management?
Big data expands the operational space for algorithms and machine-mediated analysis. At some manufacturers, for example, algorithms analyze sensor data from production lines, creating self-regulating processes that cut waste, avoid costly (and sometimes dangerous) human interventions, and ultimately lift output. In advanced, “digital” oil fields, instruments constantly read data on wellhead conditions, pipelines, and mechanical systems. That information is analyzed by clusters of computers, which feed their results to real-time operations centers that adjust oil flows to optimize production and minimize downtimes. One major oil company has cut operating and staffing costs by 10 to 25 percent while increasing production by 5 percent.
Products ranging from copiers to jet engines can now generate data streams that track their usage. Manufacturers can analyze the incoming data and, in some cases, automatically remedy software glitches or dispatch service representatives for repairs. Some enterprise computer hardware vendors are gathering and analyzing such data to schedule preemptive repairs before failures disrupt customers’ operations. The data can also be used to implement product changes that prevent future problems or to provide customer use inputs that inform next-generation offerings.
Some retailers are also at the forefront of using automated big data analysis: they use “sentiment analysis” techniques to mine the huge streams of data now generated by consumers using various types of social media, gauge responses to new marketing campaigns in real time, and adjust strategies accordingly. Sometimes these methods cut weeks from the normal feedback and modification cycle.
But retailers aren’t alone. One global beverage company integrates daily weather forecast data from an outside partner into its demand and inventory-planning processes. By analyzing three data points—temperatures, rainfall levels, and the number of hours of sunshine on a given day—the company cut its inventory levels while improving its forecasting accuracy by about 5 percent in a key European market.
The bottom line is improved performance, better risk management, and the ability to unearth insights that would otherwise remain hidden. As the price of sensors, communications devices, and analytic software continues to fall, more and more companies will be joining this managerial revolution.
5. Could you create a new business model based on data?
Big data is spawning new categories of companies that embrace information-driven business models. Many of these businesses play intermediary roles in value chains where they find themselves generating valuable “exhaust data” produced by business transactions. One transport company, for example, recognized that in the course of doing business, it was collecting vast amounts of information on global product shipments. Sensing opportunity, it created a unit that sells the data to supplement business and economic forecasts.
Another global company learned so much from analyzing its own data as part of a manufacturing turnaround that it decided to create a business to do similar work for other firms. Now the company aggregates shop floor and supply chain data for a number of manufacturing customers and sells software tools to improve their performance. This service business now outperforms the company’s manufacturing one.
Big data also is turbocharging the ranks of data aggregators, which combine and analyze information from multiple sources to generate insights for clients. In health care, for example, a number of new entrants are integrating clinical, payment, public-health, and behavioral data to develop more robust illness profiles that help clients manage costs and improve treatments.
And with pricing data proliferating on the Web and elsewhere, entrepreneurs are offering price comparison services that automatically compile information across millions of products. Such comparisons can be a disruptive force from a retailer’s perspective but have created substantial value for consumers. Studies show that those who use the services save an average of 10 percent—a sizable shift in value.
Confronting complications
Up to this point, we have emphasized the strategic opportunities big data presents, but leaders must also consider a set of complications. Talent is one of them. In the United States alone, our research shows, the demand for people with the deep analytical skills in big data (including machine learning and advanced statistical analysis) could outstrip current projections of supply by 50 to 60 percent. By 2018, as many as 140,000 to 190,000 additional specialists may be required. Also needed: an additional 1.5 million managers and analysts with a sharp understanding of how big data can be applied. Companies must step up their recruitment and retention programs, while making substantial investments in the education and training of key data personnel.
The greater access to personal information that big data often demands will place a spotlight on another tension, between privacy and convenience. Our research, for example, shows that consumers capture a large part of the economic surplus that big data generates: lower prices, a better alignment of products with consumer needs, and lifestyle improvements that range from better health to more fluid social interactions.5 As a larger amount of data on the buying preferences, health, and finances of individuals is collected, however, privacy concerns will grow.
That’s true for data security as well. The trends we’ve described often go hand in hand with more open access to information, new devices for gathering it, and cloud computing to support big data’s weighty storage and analytical needs. The implication is that IT architectures will become more integrated and outward facing and will pose greater risks to data security and intellectual property. For some ideas on how leaders should respond, see “Meeting the cybersecurity challenge.”
Although corporate leaders will focus most of their attention on big data’s implications for their own organizations, the mosaic of company-level opportunities we have surveyed also has broader economic implications. In health care, government services, retailing, and manufacturing, our research suggests, big data could improve productivity by 0.5 to 1 percent annually. In these sectors globally, it could produce hundreds of billions of dollars and euros in new value.
In fact, big data may ultimately be a key factor in how nations, not just companies, compete and prosper. Certainly, these techniques offer glimmers of hope to a global economy struggling to find a path toward more rapid growth. Through investments and forward-looking policies, company leaders and their counterparts in government can capitalize on big data instead of being blindsided by it.
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