Harvard business review最新文献

Senior leaders who write off the move toward big data as a lot of big talk are making, well, a big mistake. So argue McKinsey's Barton and Court, who worked with dozens of companies to figure out how to translate advanced analytics into nuts-and-bolts practices that affect daily operations on the front lines. The authors offer a useful guide for leaders and managers who want to take a deliberative approach to big data-but who also want to get started now. First, companies must identify the right data for their business, seek to acquire the information creatively from diverse sources, and secure the necessary IT support. Second, they need to build analytics models that are tightly focused on improving performance, making the models only as complex as business goals demand. Third, and most important, companies must transform their capabilities and culture so that the analytical results can be implemented from the C-suite to the front lines. That means developing simple tools that everyone in the organization can understand and teaching people why the data really matter. Embracing big data is as much about changing mind-sets as it is about crunching numbers. Executed with the right care and flexibility, this cultural shift could have payoffs that are, well, bigger than you expect.

Many of us are struggling to chart a path toward success in our careers and a sense of fulfillment in all aspects of our lives. But we can't excel simultaneously in every role. Instead, at various points in life we must choose what to emphasize and what to relinquish. The goal is to make that decision consciously instead of unwittingly Letting go of the most important item. The author presents a framework he designed with Howard Stevenson, a business professor who has played many roles throughout his life, to help ambitious executives understand their limits and make tough trade-offs. It starts with considering all the dimensions of your life, developing a vision of yourself for the present and for the future, and then evaluating how your options advance you toward your goals. where do your options fall on the needs-wants spectrum? Most things fall somewhere in the middle. Some wants are so strong that it's difficult to separate them from needs. What are the investment and opportunity costs? Most decisions involve both kinds of costs. The challenge is to understand if incurring them will help you achieve your goals. Are the potential benefits worth the costs? Does the benefit you'll receive warrant the investment you'll have to make? Can you make a trade? Many of us try to exchange something we have for something else that we want. But sometimes the two items can't be traded. Money, for instance, cannot buy health. Have you considered sequencing your most valued options? Consciously staggering your goals may enable you to be equally successful in many dimensions over time.

Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.

Back in the 1990s, computer engineer and Wall Street "quant" were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don't just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School's Davenport and Greylock's Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.

When leaders don't fire underperforming executives, they send a bad message to the whole organization. A case in point is the U.S. Army. "To study the change in the army across the two decades between World War II and Vietnam," Ricks writes, "is to learn how a culture of high standards and accountability can deteriorate." In this essay, adapted from his new book, The Generals: American Military Command from World War II to Today, Ricks illuminates the contrast between General George C. Marshall, an unlikely figure of quiet resolve who became a classic transformational Leader, and the disastrous generals of the Vietnam era. In Vietnam, he writes, the honesty and accountability of Marshall's system were replaced by deceit and command indiscipline. If inadequate leaders are allowed to remain in command of an enterprise, their superiors must look for other ways to accomplish its goals. In Vietnam commanders turned to micromanagement, hovering overhead in helicopters to direct (and interfere with) squad leaders and platoon leaders on the ground. This both undercut combat effectiveness and denied small-unit leaders the opportunity to grow by making decisions under extreme pressure. In Iraq and Afghanistan, Ricks writes, though U.S. troops fought their battles magnificently, their generals often seemed ill equipped for the tasks at hand-especially the difficult but essential job of turning victories on the ground into strategic progress. This brief but powerful history of the army since World War II holds stark lessons for business leaders.

Many managers feel doomed to trade off the futile rigor of ordinary strategic planning for the hit-or-miss creativity of the alternatives. In fact, the two can be reconciled to produce novel but realistic strategies. The key is to recognize that conventional strategic planning, for all its analysis, is not actually scientific-it lacks the careful generation and testing of hypotheses that are at the heart of the scientific method. The authors outline a strategy-making process that combines rigor and creativity. A team begins by formulating options, or possibilities, and asks what must be true for each to succeed. Once it has listed all the conditions, it assesses their likelihood and thereby identifies the barriers to each choice. The team then tests the key barrier conditions to see which hold true. From here, choosing a strategy is simple: The group need only review the test results and choose the possibility with the fewest serious barriers. This is the path P&G took in the late 1990s, when it was looking to become a major global player in skin care. After testing the barrier conditions for several possibilities, it opted for a bold strategy that might never have surfaced in the traditional process: reinventing Olay as a prestigelike product also sold to mass consumers. The new Olay succeeded beyond expectations-showing what can happen when teams shift from asking "What is the right answer" and focus instead on figuring out "What are the right questions?".

No sales force consists entirely of stars; sales staffs are usually made up mainly of solid perfomers, with smaller groups of laggards and rainmakers. Though most compensation plans approach these three groups as if they were the same, research shows that each is motivated by something different. By accounting for those differences in their incentive programs, companies can coax better performance from all their salespeople. As the largest cadre, core performers typically represent the greatest opportunity, but they're often ignored by incentive plans. Contests with prizes that vary in nature and value (and don't all go to stars) will inspire them to ramp up their efforts, and tiered targets will guide them up the performance curve. Laggards need quarterly bonuses to stay on track; when they have only annual bonuses, their revenues will drop 10%, studies show. This group is also motivated by social pressure-especially from new talent on the sales bench. Stars tend to get the most attention in comp plans, but companies often go astray by capping their commissions to control costs. If firms instead remove commission ceilings and pay extra for overachievement, they'll see the sales needle really jump. The key is to treat sales compensation not as an expense to rein in but as a portfolio of investments to manage. Companies that do this will be rewarded with much higher returns.