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This is an MIT Sloan Management Review article. Many people in the information technology world believe that "big data"will give companies new capabilities and value. But companies have been dealing with an exponentially increasing amount of data, and much of it in forms that are impossible to manage by traditional analytics. "Big data"includes information such as call center voice data, social media content and video entertainment, as well as clickstream data from the web. The authors posit that organizations that are learning to take advantage of big data are beginning to understand their business environments at a more granular level, are creating new products and services, and are responding more quickly to change as it occurs. These companies stand apart from those with traditional data analysis environments in three critical ways. First, rather than looking at data to assess what happened in the past, these organizations consider data in terms of flows and processes, and make decisions and take actions quickly. In addition, organizations already involved with big data are taking a lead on hiring -and training -data scientists and product and process developers as opposed to data analysts. And finally, advanced organizations are moving analytics from IT into their core business and operational functions. As big data evolves, a new information ecosystem is also evolving, a network that is continuously sharing information, optimizing decisions, communicating results and generating new insights for businesses.

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.

Leadership missteps are proliferating--perhaps because of the cognitive limitations of individuals. One antidote is multiple perspectives. Another is better information gathering. Gradually we are learning to tap the superior judgment of organizations.

Over the past decade companies have become more global and employee groups more diverse than ever before. Organizations are less hierarchical and more collaborative. And today's offices are full of once unimaginable technological distractions. We asked experts in cross-cultural communication, information networks, and the science of attention what skills executives should cultivate to tackle these new challenges. Molinsky thinks that managers must overcome psychological barriers in order to act in ways that other cultures find appropriate. Davenport and Iyer explain why the devolution of hierarchy has increased the value of building and wielding influence through digital networks, and offer tips for how to do it. And Davidson tells managers to get over their fears about distraction and embrace the brain's natural tendency to divide attention.

Shoppers once relied on familiar salespeople to help them find exactly what they wanted--and sometimes to suggest additional items they hadn't even thought of. But today's distracted consumers, bombarded with information and options, often struggle to find products or services that meet their needs. Advances in information technology, data gathering, and analytics are making it possible to deliver something like the personal advice of yesterday's sales staffs. Using increasingly granular customer data, businesses are starting to create highly customized offers that steer shoppers to the "right" merchandise--at the right moment, at the right price, and in the right channel. But few companies can do this well. The authors demonstrate how retailers can hone their "next best offer" (NBO) capability by breaking the problem down into four steps: defining objectives, gathering data (about your customers, your products, and the purchase context), analyzing and executing, and learning and evolving. Citing successful strategies in companies such as Tesco, Zappos, Microsoft, and Walmart, they provide a framework for nailing the NBO.

This is an MIT Sloan Management Review article. Almost all executives want more and faster information, and almost all companies are racing to provide it. What many of them are overlooking is that the real aim should not be faster information but faster decision making, and those aren't the same things. Executives tend to request more information than they can use, and oftentimes the cycle time of decision making, not the speed of information flow, is the real bottleneck. While it is critical to identify what information is needed, it is just as important to deliver it at the right time. The types of information needed most quickly are not static and can depend on several conditions, including whether the current state of the economy is growing or contracting. The right cycle time for particular types of information can vary widely by industry, but in general the highest requested frequency is for competitor news, sales and customer news. Based on responses to their surveys and interviews of senior executives and managers, the authors provide suggestions and guidelines to enable IT organizations to deliver information more quickly and flexibly. That requires changes to both human and technical capabilities, but those changes could make all the difference in having the correct information to avert a crisis.

HBR asked top management thinkers to share what they were resolved to accomplish in 2011. Here are their answers: Joseph E. Stiglitz will be crafting a new postcrisis paradigm for macroeconomics whereby rational individuals interact with imperfect and asymmetric information. Herminia Ibarra will be looking for hard evidence of how "soft" leadership creates value. Eric Schmidt will be planning to scale mobile technology by developing fast networks and providing low-cost smartphones in the poorest parts of the world. Michael Porter will be using modern cost accounting to uncover-and lower-the real costs of health care. Vijay Govindarajan will be trying to prototype a $300 house to replace the world's poorest slums, provide healthy living, and foster education. Dan Ariely will be investigating consumers' distaste for genetically modified salmon, synthetic pharmaceuticals, and other products that aren't "natural." Laura D. Tyson will be promoting the establishment of a national infrastructure investment bank. Esther Duflo will be striving to increase full immunization in poor areas of India. Clay Shirky will be studying how to design internet platforms that foster civility. Klaus Schwab will be undertaking to create a Risk Response Network through which decision makers around the world can pool knowledge about the risks they face. Jack Ma will be working to instill a strong set of values in his 19,000 young employees and to help clean up China's environment. Thomas H. Davenport will be researching big judgment calls that turned out well and how organizations arrived at them. A.G. Lafley will be proselytizing to make company boards take leadership succession seriously. Eleven additional contributors to the Agenda, along with special audio and video features, can be found at hbr.org/2011-agenda.

Do investments in your employees actually affect workforce performance? Who are your top performers? How can you empower and motivate other employees to excel? Leading edge companies such as Google, Best Buy, P&G, and Sysco use sophisticated data-collection technology and analysis to answer these questions, leveraging a range of analytics to improve the way they attract and retain talent, connect their employee data to business performance, differentiate themselves from competitors, and more. The authors present the six key ways in which companies track, analyze, and use data about their people-ranging from a simple baseline of metrics to monitor the organization's overall health to custom modeling for predicting future head count depending on various "what if" scenarios. They go on to show that companies that compete on talent analytics manage data and technology at an enterprise level, support what analytical leaders do, choose realistic targets for analysis, and hire analysts with strong interpersonal skills as well as broad expertise.

Knowledge management has been a high priority for Cognizant Technology Solutions since its inception since its global delivery model requires the global sharing of knowledge. Its first major tool was called the Knowledge Management Appliance but as Web 2.0 tools came into wider use, this evolved into what the company called "Cognizant 2.0" (C2) which was designed to ensure that the KM Appliance capabilities for storing documents and participative tools such as blogs and wikis were directed towards supporting business goals. This required the development of a set of structured work process guidelines and tasks for each major type of work performed internally and for clients. Increasing awareness amongst its clients about C2 has led the company into considering whether it should turn this into a client-facing service offering itself. As its clients become more interested in knowledge management within their own companies, the interest in a C2-based offering could grow.

Traditionally, decision making in organizations has rarely been the focus of systematic analysis. That may account for the astounding number of recent poor calls, such as decisions to invest in and securitize subprime mortgage loans or to hedge risk with credit default swaps. Business books are rich with insights about the decision process, but organizations have been slow to adopt their recommendations. It's time to focus on decision making, Davenport says, and he proposes four steps: (1) List and prioritize the decisions that must be made; (2) assess the factors that go into each, such as who plays what role, how often the decision must be made, and what information is available to support it; (3) design the roles, processes, systems, and behaviors your organization needs; and (4) institutionalize decision tools and assistance. The Educational Testing Service and The Stanley Works, among others, have succeeded in improving their decisions. ETS established a centralized deliberative body to make evidence-based decisions about new-product offerings, and Stanley has a Pricing Center of Excellence with internal consultants dedicated to its various business units. Leaders should bring multiple perspectives to their decision making, beware of analytical models that managers don't understand, be clear about their assumptions, practice "model management," and - because only people can revise decision criteria over time - cultivate human backups.

Managers regularly implement new ideas without evidence to back them up. They act on hunches and often learn very little along the way. That doesn't have to be the case. With the help of broadly available software and some basic investments in building capabilities, managers don't need a PhD in statistics to base consequential decisions on scientifically sound experiments. Some companies with rich consumer-transaction data - Toronto-Dominion, CKE Restaurants, eBay, and others - are routinely testing innovations well outside the realm of product R&D. As randomized testing becomes standard procedure in certain settings (website analysis, for instance), firms learn to apply it in other areas as well. Entire organizations that adopt a "test and learn" culture stand to realize the greatest benefits. That said, firms need to determine when formal testing makes sense. Generally, it's much more applicable to tactical decisions (such as choosing a new store format) than to strategic ones (such as figuring out whether to acquire a business). Tests are useful only if managers define and measure desired outcomes and formulate logical hypotheses about how proposed interventions will play out. To begin incorporating more scientific management into your business, acquaint managers at all levels with your organization's testing process. A shared understanding of what constitutes a valid test - and how it jibes with other processes - helps executives to set expectations and innovators to deliver on them. The process always begins with creating a testable hypothesis. Then the details of the test are designed, which means identifying sites or units to be tested, selecting control groups, and defining test and control situations. After the test is carried out for a specified period, managers analyze the data to determine results and appropriate actions. Results ideally go into a "learning library," so others can benefit from them.

Our annual survey of ideas and trends that will make an impact on business: Elizabeth Warren and Amelia Tyagi believe consumer credit should be made as safe as any other product. Paul Collier and Jean-Louis Warnholz reveal an increasingly investment-friendly climate in sub-Saharan Africa. Amy J.C. Cuddy asserts that warmth and competence are not mutually exclusive. John Sviokla predicts a surge of peer-to-peer lending in the wake of the financial crisis. Noah J. Goldstein explains the impact of social pressure on customers' behavior. Raymond Fisman urges the creation of a global forensic economics lab modeled on Interpol. Paul Saffo warns of a brain drain out of the U.S. Gurdeep Singh Pall and Rita Gunther McGrath contemplate the ramifications of immortalizing business meetings in searchable, high-quality digital video. Janine M. Benyus and Gunter A.M. Pauli illustrate the advantages of innovation copied from nature. Michael I. Norton observes that an investment of effort can lead to unduly glorifying its results. Peter Schwartz dispels the illusion that global temperatures are actually falling. Nicholas A. Christakis shows that personal influence wanes beyond three degrees of separation. Marcelo Suarez-Orozco sees the migrant millions as untapped brand emissaries to their relatives back home. Ian Bremmer and Juan Pujadas chart the growing influence of state capitalism in four industry sectors. Steve Jurvetson shares a fun way to stimulate the growth of new brain cells. Lew McCreary spotlights the interior designs of two adventurous architects who aim to counteract the degenerative effects of physical comfort. Tom Ilube explains the semantic web - a quiet revolution in technology that will radically change the internet. Alex Pentland weighs the benefits of combining two distinct kinds of social networking. Thomas H. Davenport and Bala Iyer look at the offshore outsourcing of decision making. R. Stanley Williams envisions a central nervous system for the earth.

This is an MIT Sloan Management Review article. When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options--some of which are well established, while others are in early stages of development. The authors provide an insider's guide to prediction and recommendation techniques and technologies. They cover prediction tools including attributized Bayesian analysis, biological responses analysis, cluster analysis, collaborative filtering, content-based filtering/decision trees, neural network analysis, prediction (or opinion) markets, regression analysis, social network-based recommendations and textual analytics. With each potential tool, they briefly describe the technique, who uses it and for what purpose, its strengths and weaknesses, and its future prospects as a prediction tool. Finally, the authors offer up an indication of the best time in the decision process to begin using the tool.

This is an MIT Sloan Management Review article. Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics -- data, statistics, predictive modeling -- to determine the likely success of their offerings. Instead, companies relied on the brilliance of tastemakers to predict and shape what people would buy. Creative judgment and expertise will always play a vital role in the creation, shaping and marketing of cultural products. But the balance between art and science is shifting. Today companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago. And with increased cost and risk associated with the creation of cultural products, it has never been more important to get these decisions right. In this article, the authors describe the results of a study of prediction and recommendation efforts for a variety of cultural products. They discuss different approaches used to make predictions, the contexts in which these predictions are applied and the barriers to more extensive use, including the problem of decision making pre-creation. They then discuss two aspects of the prediction market. First, the need for better prediction for distributors of cultural products, and second, the potential for business models around prediction techniques.

Even among internet companies, Google stands out as an enterprise designed with the explicit goal of succeeding at rapid, profuse innovation. Much of what the company does is rooted in its legendary IT infrastructure, but technology and strategy at Google are inseparable and mutually permeable - making it hard to say whether technology is the DNA of its strategy or the other way around. Whichever it is, Iyer and Davenport, of Babson College, believe Google may well be the internet-era heir to such companies as General Electric and IBM as an exemplar of management practice. Google has spent billions of dollars creating its internet-based operating platform and developing proprietary technology that allows the company to rapidly develop and roll out new services of its own or its partners' devising. As owner and operator of its innovation "ecosystem," Google can control the platform's evolution and claim a disproportionate percentage of the value created within it. Because every transaction is performed through the platform, the company has perfect, continuous awareness of, and access to, the by-product information and is the hub of all germinal revenue streams. In addition to technology explicitly designed and built for innovation, Google has a well-considered organizational and cultural strategy that helps the company attract the most talented people in the land - and keep them working hard. For instance, Google budgets innovation into job descriptions, eliminates friction from development processes, and cultivates a taste for failure and chaos. While some elements of Google's success as an innovator would be very hard and very costly to emulate, others can be profitably adopted by almost any business.

Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics--drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study in R0705A and R0705Z are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.

Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics--drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study in R0705A and R0705Z are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.

Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics--drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study in R0705A and R0705Z are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.

This is an MIT Sloan Management Review article. When employees leave an organization, they depart with more than what they know; they also leave with critical knowledge about who they know. Thus, the departure of key people can significantly affect the relationship structure and consequent functioning of an organization. In particular, companies should be aware of the unique knowledge held by three important types of employees: "central connectors" (those who are regularly asked for help, typically because they have a high level of expertise in one or more areas), "brokers" (those who act as bridges across subgroups) and "peripheral players" (those who reside on the boundaries of a network but could still possess valuable niche expertise and outside knowledge). Departure of an employee who filled any one of these roles presents knowledge-loss risks that need to be addressed. The departure of a handful of key brokers, for example, could fracture the social network of an organization into isolated subgroups. Thus companies need to take various measures to (1) identify key knowledge vulnerabilities by virtue of both what a person knows and how that individual's departure will affect a network and (2) address specific knowledge-loss issues based on the different roles that employees play in the network.

We all know the power of the killer app. It's not just a support tool; it's a strategic weapon. Companies questing for killer apps generally focus all their firepower on the one area that promises to create the greatest competitive advantage. But a new breed of organization has upped the stakes: Amazon, Harrah's, Capital One, and the Boston Red Sox have all dominated their fields by deploying industrial-strength analytics across a wide variety of activities. At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the few remaining points of differentiation-and analytics competitors wring every last drop of value from those processes. Employees hired for their expertise with numbers or trained to recognize their importance are armed with the best evidence and the best quantitative tools. As a result, they make the best decisions. In companies that compete on analytics, senior executives make it clear-from the top down-that analytics is central to strategy. Such organizations launch multiple initiatives involving complex data and statistical analysis, and quantitative activity is managed at the enterprise (not departmental) level. In this article, professor Thomas H. Davenport lays out the characteristics and practices of these statistical masters and describes some of the very substantial changes other companies must undergo in order to compete on quantitative turf. As one would expect, the transformation requires a significant investment in technology, the accumulation of massive stores of data, and the formulation of companywide strategies for managing the data. But, at least as important, it also requires executives' vocal, unswerving commitment and willingness to change the way employees think, work, and are treated.