What Are Analytics?

By analytics, we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions (see the box “Analytics Definitions” for some key terms). The analytics may be input for human decisions or may drive fully automated decisions.

As figure 1-2 shows, analytics may be descriptive, predictive, prescriptive, or autonomous. Each of these approaches addresses a range of questions about an organization’s business activities. The questions that analytics can answer represent the higher-value and more proactive end of this spectrum.

In principle, analytics could be performed using paper, pencil, and perhaps a slide rule, but any sane person using analytics today would employ a computer and software. The range of analytical software encompasses relatively simple statistical and optimization tools in spreadsheets (Excel being the primary example, of course), traditional statistical software packages (e.g., Minitab or Stata), complex data visualization and descriptive analytics suites (Qlik, Tableau, MicroStrategy, Oracle Hyperion, IBM Cognos), comprehensive descriptive, predictive and prescriptive analytics software (SAS, IBM), predictive industry applications (FICO), and the reporting and analytical modules of major enterprise systems (SAP BusinessObjects and Oracle). Open-source statistical programming capabilities (e.g., R, Python) are rapidly evolving to address both traditional statistical analysis and massive unstructured data. And as we’ll describe later in the book, good analytical capabilities also require good information management capabilities to acquire, transform, manage, analyze, and act on both external and internal data. Some people, then, would simply equate analytics with analytical information technology. But this would be a huge mistake—as we’ll argue throughout this book, it’s the human and organizational aspects of analytical competition that are truly differentiating.

Why Compete on Analytics?

At a time when companies in many industries offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation. Many of the previous bases for competition are no longer available. Unique geographical advantage doesn’t matter in global competition, and protective regulation is largely gone. Proprietary technologies are rapidly copied, and breakthrough innovation in products or services seems increasingly difficult to achieve. What’s left as a basis for competition is to execute your business with maximum efficiency and effectiveness, and to make the smartest business decisions possible. And analytical competitors wring every last drop of value from business processes and key decisions. Analytics are even increasingly being embedded into their products and services.

Analytics can support almost any business process. Yet organizations that want to be competitive must have some attribute at which they are better than anyone else in their industry—a distinctive capability.⁵ This usually involves some sort of business process or some type of decision, or perhaps a distinctive product offering. Maybe you strive to make money by being better at identifying profitable and loyal customers than your competition, and charging them the optimal price for your product or service. If so, analytics are probably the answer to being the best at it. Perhaps you sell commodity products and need to have the lowest possible level of inventory while preventing your customer from being unable to find your product on the shelf; if so, analytics are often the key to supply chain optimization. Maybe you have differentiated your products and services by incorporating some unique data and proprietary algorithms. Perhaps you compete in a people-intensive business and are seeking to hire, retain, and promote the best people in the industry. There too, analytics can be the key.

On the other hand, perhaps your operational business processes aren’t much different from anybody else’s, but you feel you compete on making the best decisions. Maybe you can choose the best locations for your stores—if so, you’re probably doing it analytically. You may build scale through mergers and acquisitions, and select only the best candidates for such combinations. Most don’t work out well, according to widely publicized research, but yours do. If so, you’re probably not making those decisions primarily on intuition. Good decisions usually have systematically assembled data and analysis behind them.

Analytical competitors, then, are organizations that have selected one or a few distinctive capabilities on which to base their strategies, and then have applied extensive data, statistical and quantitative analysis, and fact-based decision making to support the selected capabilities. Analytics themselves don’t constitute a strategy, but using them to optimize a distinctive business capability certainly constitutes a strategy. Whatever the capabilities emphasized in a strategy, analytics can propel them to a higher level. Capital One, for example, calls its approach to analytical competition “information-based strategy.” Caesars’ distinctive capabilities are customer loyalty and service, and it has certainly optimized them with its analytically driven strategy. GE is differentiating its industrial services processes by using sensor data to identify problems and maintenance needs before they cause unscheduled downtime.

Can any organization in any industry successfully compete on analytics? This is an interesting question that we’ve debated between ourselves. On the one hand, virtually any business would seem to have the potential for analytical competition. The cement business, for example, would seem to be as prosaic and non-analytical an industry as one could find. But the global cement giant CEMEX has successfully applied analytics to its distinctive capability of optimized supply chains and delivery times. We once believed that the fashion business might never be analytical, but then we found numerous examples of analytics-based predictions about what clothing styles and colors might sell out this season.

On the other hand, some industries are clearly more amenable to analytics than others. If your business generates lots of transaction data—such as in financial services, travel and transportation, or gaming—competing on analytics is a natural strategy (though many firms still don’t do it). Similarly, if you can draw on the wealth of data available on the internet or on social media to get a unique insight into your customers and markets, competing on analytics is a great way to differentiate yourself. If your business model is based on hard-to-measure factors like style (as in the fashion business) or human relationships (as in the executive search industry), it would take much more groundbreaking work to compete on analytics—although, as we suggested, it’s being done to some degree. Virtually every day we find examples of businesses that were previously intuitive but are now becoming analytical. The wine business, for example, was once (and in some quarters still is) highly intuitive and built on unpredictable consumer preferences. Today, however, it’s possible to quantitatively analyze and predict the appeal of any wine, and large winemakers such as E. & J. Gallo are competing on analytics in such domains as sales, agriculture, and understanding of consumer preferences.⁶

How Did We Get Here? The Origins of Analytical Competition

The planets are clearly aligned for the move to analytical competition by organizations. At the same time that executives have been looking for new sources of advantage and differentiation, they have more data about their businesses than ever before. Enterprise resource planning (ERP) systems, point-of-sale (POS) systems, and mobile devices, websites and e-commerce, among other sources, have created more and better data than in the history of humankind. A new generation of technically literate executives—the first to grow up with computers—is coming into organizations and looking for new ways to manage them with the help of technology. Finally, the ability to make sense of data through computers and software has finally come of age. Analytical software makers have dramatically expanded the functionality of their products over the past several years, and hardware providers have optimized their technologies for fast analysis and the management of massive databases.

The use of analytics began as a small, out-of-the-way activity performed in a few data-intensive business functions. As early as the late 1960s, practitioners and researchers began to experiment with the use of computer systems to analyze data and support decision making. Called decision support systems (DSS), these applications were used for analytical, repetitive, and somewhat narrow activities such as production planning, investment portfolio management, and transportation routing. Two DSS pioneers, Peter Keen and Charles Stabell, argue that the concept of decision support arose from studies of organizational decision making done at Carnegie Tech (now Carnegie Mellon) by researchers such as Herbert Simon during the late 1950s and early ’60s, and technical work on interactive computer systems, mainly carried out at MIT in the 1960s.⁷ Others would argue that their origins were closely connected to military applications in and following World War II, although computers as we know them were not yet available for those applications.

Statistical analysis on computers became a much more mainstream activity in the 1970s, as companies such as SAS Institute and SPSS (now part of IBM) introduced packaged computer applications that made statistics accessible to many researchers and businesspeople. Yet despite the greater availability of statistics, DSS did not prosper in the period and evolved into executive support systems.⁸ These applications involved direct use of computers and data by senior executives for monitoring and reporting of performance (with a lesser emphasis on decision making). This activity also never took off broadly, in part because of the reluctance of executives to engage in hands-on use.

Analytical technology became most frequently used for storing relatively small amounts of data and conducting ad hoc queries in support of decisions and performance monitoring. The focus on managing data became important because vast amounts of it were becoming available from transaction systems such as ERP and POS systems, and later from internet data. Versions of this data-oriented focus were referred to as OLAP (online analytical processing) and later business intelligence. The data management activities were known as data warehousing. Smaller data warehouses were called data marts.

Meanwhile, big data has its roots in a field originally known as “knowledge discovery and data mining” in 1989.⁹ Before this time, the practice of data exploration without a guiding hypothesis was deemed too unfocused to be valuable to businesses. Technically speaking, the term big data refers to data that is too big, volatile, and unstructured to be manipulated and analyzed using traditional technologies. As we describe in the introduction, about a decade ago, Silicon Valley firms such as Google and LinkedIn developed new ways to process and make sense of all the data they capture. Once they made those tools publically available, big data and machine learning began to infiltrate analytical enterprises in other industries, too.

Today, the entire field is referred to by a variety of names such as big data analytics or business intelligence and advanced analytics, which generally encompass the collection, management, and reporting of decision-oriented data as well as the analytical techniques and computing approaches that are performed on the data. Business intelligence and analytics platforms are a broad and popular field within the IT industry—in fact, Gartner’s 2016 survey of nearly three thousand chief information officers from eighty-four countries found that business intelligence and data analytics are the number-one technology priority for IT organizations for the fifth consecutive year.¹⁰ Two studies of large organizations using ERP systems that we did in 2002 and 2006 revealed that better decision making was the primary benefit sought, and (in 2006) analytics was the technology most sought to take advantage of the ERP data.

Despite the variation in terminology, these movements—each of which lasted about a decade—had several attributes in common. They were largely technically focused, addressing how computers could be used to store, analyze, and display data and results of analysis. They were focused on fairly narrow problems—with the exception of the executive and performance monitoring systems, which displayed only the condition of the business. They were also relegated to the back office of organizations—used by technicians and specialists, with little visibility to senior executives. With only a few exceptions, they could rarely be said to influence the nature of competition.

Today, most large organizations have some sort of analytical applications in place and some analytics tools installed. But they are too often marginal to the success of the business and are managed at the departmental level. An insurance company, for example, may have some analytical tools and approaches in the actuarial department, where pricing for policies is determined. A manufacturing company may use such tools for quality management. Marketing may have some capabilities for lifetime value analysis for customers. However valuable these activities are, they are invisible to senior executives, customers, and shareholders—and they can’t be said to drive the company’s competitive strategy. They are important to individual functions but insignificant to competition overall.

Our focus in this book, however, is on companies that have elevated data management, statistical and quantitative analysis, predictive modeling, and fact-based decision making to a high art. These organizations have analytical activities that are hardly invisible; they are touted to every stakeholder and interested party by CEOs. Rather than being in the back room, analytics in these companies are found in the boardroom, the annual report, and in the press clippings. These organizations have taken a resource that is ostensibly available to all, and refined it to such a degree that their strategies (and increasingly, their products) are built around it.

When Are Analytical Decisions Appropriate?

There is considerable evidence that decisions based on analytics are more likely to be correct than those based on intuition.¹¹ It’s better to know—at least within the limits of data and analysis—than to believe or think or feel, and most companies can benefit from more analytical decision making. Of course, there are some circumstances in which decisions can’t or shouldn’t be based on analytics. Some of these circumstances are described in Malcolm Gladwell’s popular book Blink, which is a paean to intuitive decision making.¹² It’s ironic that a book praising intuition became popular just when many organizations are relying heavily on analytics, but then perhaps that was part of its romantic appeal. The book is fun and has great stories, but it doesn’t make clear that intuition is only appropriate under certain circumstances.

It’s also clear that decision makers have to use intuition when they have no data and must make a very rapid decision—as in Gladwell’s example of police officers deciding whether to shoot a suspect. Gary Klein, a consultant on decision making, makes similar arguments about firefighters making decisions about burning buildings.¹³ Even firms that are generally quite analytical must sometimes resort to intuition when they have no data. For example, Jeff Bezos, CEO of Amazon, greatly prefers to perform limited tests of new features on Amazon, rigorously quantifying user reaction before rolling them out. But the company’s “search inside the book” offering was impossible to test without applying it to a critical mass of books (Amazon started with 120,000). It was also expensive to develop, increasing the risk. In that case, Bezos trusted his instincts and took a flier. And the feature did prove popular when introduced.¹⁴

Of course, any quantitative analysis relies on a series of assumptions. When the conditions behind the assumptions no longer apply, the analyses should no longer be employed. For example, Capital One and other credit card companies make analytical predictions about customers’ willingness to repay their balances under conditions of general economic prosperity. If the economy took a sharp downturn, the predictions would no longer apply, and it would be dangerous to continue using them. This is not just a hypothetical example; many banks that issued mortgage loans found that their assumptions about repayment were no longer valid in the 2008–2009 financial crisis, and those that didn’t change their models quickly no longer exist.

The key message is that the frontier of decisions that can be treated analytically is always moving forward. Areas of decision making that were once well suited for intuition accumulate data and analytical rigor over time, and intuition becomes suboptimal. Today, for example, some executives still consider it feasible to make major decisions about mergers and acquisitions from their gut. However, the best firms are already using detailed analytics to explore such decisions. Procter & Gamble, for example, used a variety of analytical techniques before its acquisition of Gillette, including those for logistics and supply chains, drivers of stock market value, and human resources. In a few years, firms that do not employ extensive analytics in making a major acquisition will be considered irresponsible. Already, IBM is using algorithms to evaluate merger and acquisition candidates. Its M&A Pro tool both speeds deals and eliminates what the company views as the greatest source of problems in M&A work—human error.¹⁵

Indeed, trends point to a more analytical future for virtually every firm. The amount of data available will only continue to increase. Radio frequency identification (RFID) sensors will be put on virtually every pallet or carton that moves through the supply chain, generating vast amounts of new data for companies to collect and analyze. Every industrial machine and every vehicle will produce a vast amount of sensor data. Every mobile phone has a wealth of data about its user and her behavior. In retail, every shopping cart will be intelligent enough to gather data on “pickstreams,” or a record of which products are taken off the shelves in what order (Amazon has already opened a grocery store where sensors permit it to know what is in your cart and automatically debit your account when you leave the store).¹⁶ In oil exploration and mining, the amount of data—already massive—will expand geometrically. In advertising, more businesses are rapidly shifting to media such as the internet and cable television that can monitor which ads are seen by whom—again creating a huge new stream of data. And the decisions about what ad to run on what website are made by automated algorithms.

Analytical software will become more broadly available and will be in reach of every organization. Statistically oriented software firms such as SAS and IBM have made increasingly sophisticated analyses available to average companies and users for over forty years, and they will continue to do so. Enterprise systems vendors such as SAP, Oracle, and Salesforce.com are incorporating descriptive, predictive, and prescriptive analytics into their products, enabling managers to analyze their systems’ data in real time and monitor the performance of the business. New industry applications targeting different business capabilities will become available from vendors such as FICO Corporation and MMIS, Inc. Open-source analytical tools (like R and RapidMiner) and computing frameworks (like Apache Hadoop and Spark), which originated in Silicon Valley, are rapidly evolving and proliferating across the corporate world. And Microsoft is incorporating increasing amounts of analytical capability into basic office productivity software. In the future, software availability will not be an issue in analytical competition, although the ability to use analytical software well won’t ever be a commodity.

It’s also safe to assume that hardware won’t be a problem. Today, laptops and tablets that can do extensive quantitative analysis on large data sets are already readily available. Specialized computers and cloud platforms from providers such as Amazon, Microsoft, Teradata, Oracle, and IBM can easily manage petabytes or even exabytes of data. The cloud offers an infinitely expandable processing capability for data storage and analysis. No doubt even the smartphone of the near future will be able to perform serious analyses. The bigger issue will be how organizations control their data and analysis, and ensure that individual users make decisions on correct analyses and assumptions.

To remain an analytical competitor, however, means staying on the leading edge. Analytical competition will be something of an arms race, requiring continual development of new measures, new algorithms, new data sources, new data manipulation techniques, and new decision-making approaches. Firms embracing it will systematically eliminate guesswork from their processes and business models. Analytical competitors will have to conduct experiments in many aspects of their businesses and learn from each one of them. In order for quantitative decisions to be implemented effectively, analysis will have to be a broad capability of employees, rather than the province of a few “rocket scientists” with quantitative expertise.

We’ve developed a road map describing the primary steps needed to build an effective analytical competitor. It involves key prerequisites, such as having at least a moderate amount and quality of data about the domain of business that analytics will support, and having the right types of hardware and software on hand. The key variables are human, however. One prerequisite is that some manager must have enough commitment to analytics to develop the idea further. But the pivotal factor in how fast and how well an organization proceeds along the analytical path is sponsorship. Firms such as Netflix, Caesars, Capital One, and UPS have CEO-level sponsorship and even passion for analytical competition that lets them proceed on a “full steam ahead” path.

Other organizations that lack passionate executive sponsorship must first go through a “prove-it” path to demonstrate the value of analytical competition. This path is slower, and even those who take the prove-it path have to eventually arrive at strong executive sponsorship if they are to become true analytical competitors. We will discuss this road map—and the steps on each of the two paths—in detail in the second part of this book (chapter 6 in particular). For now, we simply want to emphasize that although analytics seem to be dispassionate and computer based, the most important factors leading to success involve passionate people.

Analytics in Professional Sports—and Their Implications for Business

We can perhaps best understand the progression of analytical competition across an industry by focusing on professional sports. While sports differ, of course, they have in common large amounts of data and talented but expensive human resources (the athletes). Sports also differ from businesses, but both domains of activity have in common the need to optimize critical resources and of course the need to win.

Perhaps the most analytical professional sport is baseball, which has long been the province of quantitative and statistical analysis. The use of statistics and new measures in baseball received considerable visibility with the publication of Moneyball, by Michael Lewis.¹⁷ The book (and 2011 movie starring Brad Pitt) describes the analytical orientation of the Oakland A’s, a professional team that had a record of consistently making the playoffs despite a low overall payroll (including the 2014 playoffs—although even the best analytical competitor doesn’t win all the time, as in 2016). Lewis described the conversion of Oakland’s general manager (GM), Billy Beane, to analytics for player selection when he realized that he himself had possessed all the traditional characteristics of a great player, according to major league scouts. Yet Beane had not been a great player, so he began to focus more on actual player performance as revealed in statistics than on the conventional wisdom of the potential to be great. Beane and the A’s also began to make use of relatively new measures of player performance, eschewing the traditional “runs batted in,” or RBIs, and focusing on “on-base percentage” and “on-base plus slugging percentage.” Like analytical competitors in business, they invented new metrics that assessed and stretched their performance.

Yet Beane was not actually the first Oakland general manager to take a statistical orientation.¹⁸ In the early 1980s, Sandy Alderson, then the GM (now CEO of the San Diego Padres, another 2006 playoff contender), adopted a more statistical approach for two reasons. First, Oakland had performed badly for a number of years before the decision and was on the brink of going out of business. Second, Alderson was offered an early version of a PC-based (actually, Apple II–based) statistical database and analysis package. Baseball statistics are widely available from firms such as STATS, LLC, and the Elias Sports Bureau, although the statistics were available to teams well before they started taking advantage of them. These reasons are typical of why businesses often adopt analytical competition: a combination of pressing business need, the availability of data, and IT that can crunch all the numbers.

The analytical approach to baseball has broadened dramatically over the last few years. Another team that has adopted the moneyball approach is the Boston Red Sox—a team with both analytical capabilities and the money to invest in expensive players. The Red Sox also had a business need, having failed to win the World Series for eighty-six years by the 2004 season. The Sox also exemplify another reason why organizations adopt analytical competition: new leadership. The team’s two new principal owners in 2002 were John Henry, a quantitative hedge fund manager, and Tom Werner, a television producer who had previously owned the San Diego Padres. The appeal of analytics to Henry was obvious, but Werner had also realized with the Padres that the traditional baseball establishment didn’t know as much about what led to championships as it purported to. The high level of executive sponsorship at the Sox let the team take the full-steam-ahead (discussed in chapter 6) approach to analytical competition.

The owners knew they needed a management team that shared their vision of using data analytics to outperform the competition. Werner brought Yale-educated Theo Epstein from the Padres and made him the youngest GM in baseball history. Epstein was joined by assistant GM Jed Hoyer. Epstein and Hoyer share a deep passion for baseball and a thirst for winning. But what made these two hires so crucial is that they also shared a deep commitment to ignoring conventional baseball beliefs in favor of detailed data analysis for decision making. Next, like other organizations committed to analytical strategies, the Red Sox quickly hired as a consultant the best analytical talent: Bill James, who was widely regarded as the world’s foremost practitioner of sabermetrics, or baseball statistics (James even invented the term himself). The fact that no other team had seen fit to hire such an underemployed analytical genius suggests that analytical competition in baseball was not yet widespread. The analytical approach—along with some new and expensive talent—paid off for the Sox quickly, and they made the American League Championship Series (ALCS) against their perennial rivals, the New York Yankees, in 2003.

Yet one game in that series illustrates a key difficulty of analytical competition: it has to spread everywhere within an organization if analytical decisions are to be implemented. In the seventh and deciding game of the series, Red Sox ace Pedro Martínez was pitching. Sox analysts had demonstrated conclusively that Martínez became much easier for opposing batters to hit after about seven innings or 105 pitches (that year, the opposing team’s batting average against Martínez for pitches 91–105 was .231; for pitches 106–120 it was .370). They had warned manager Grady Little that by no means should Martínez be left in the game after that point. Yet when Martínez predictably began to falter late in the seventh, Little let him keep pitching into the eighth (even against the advice of his pitching coach), and the Yankees shelled Martínez. The Yanks won the ALCS, and Little lost his job. It’s a powerful story of what can happen if frontline managers and employees don’t go along with the analytical program. Fortunately for long-suffering Red Sox fans (including one of the authors of this volume), the combination of numbers and money proved insurmountable in the 2004 season, and the Sox broke the eighty-six-year World Series championship drought. The Red Sox won it all again in 2007, and once more in 2013. One author of this book hopes they win again soon.

Still, some baseball pundits argued that the Red Sox’s use of data analytics to build a better team and win games was not a reliable strategy. But the data does not support this belief. Nate Silver’s data journalism website fivethirtyeight.com found that there was a considerable first mover advantage for teams that embraced analytics. It reported that “Teams with at least one analyst in 2009 outperformed their expected winning percentage by 44 percentage points over the 2012–14 period.”¹⁹ So it’s no surprise that analytical talent has a much higher profile in MLB than it did ten years ago.

There is convincing evidence that all organizations, even those that are late adopters, can compete and win with analytics. The Chicago Cubs had been one of the teams that was slow to embrace data analytics. The team had not won the World Series in over a century—the longest drought in baseball history. But in 2009. the team was bought by the Ricketts family, who were determined to bring a championship to their hometown. Tom Ricketts, chairman of the Chicago Cubs, is a highly quantitative executive who has experienced firsthand the power of analytics to get superior results. Ricketts knew that he needed to bring in analytically minded leadership to transform the Cubs. So in 2011, he hired Red Sox alums Theo Epstein as president of Baseball Operations and Jed Hoyer as GM in 2011. He offered them the greatest challenge in professional baseball: to break the Cubs’ “Curse of the Billy Goat.”²⁰

Ricketts gave Epstein a mandate to do whatever was needed to finally break the Curse. Epstein and Hoyer knew that data-driven insights would not be enough, since most major league teams had adopted them to some degree by 2011. But Ricketts promised time and resources to completely rebuild the entire team.

The management team began by creating “The Cubs Way”—a document that details the organization’s philosophy and a detailed summation of everything the team had learned about winning. For example, it describes how teaching techniques and procedures must be consistent across the farm teams and the big leagues. The document gets quite detailed about the optimal way to perform specific movements. For example, it describes “. . . which foot hits the bag when players make a turn on the bases.”²¹

Constructing a team from the ground up was a monumental task. Fortunately, while the salary cap restricted owners’ ability to pay for on-field talent, there’s no salary cap in the front office. The Cubs R&D group (led by Chris Moore, a PhD in psychology and neuroscience), was established to analyze every aspect of the game and the organization.

The team lost over a hundred games in the first year. But by 2016, they had the best record in professional baseball. And after 108 years, to the great joy of Cubs fans everywhere (including the other author of this volume), the Chicago Cubs won the 2016 World Series.

Using advanced data analytics has spread to every professional sport, including golf, hockey, and tennis. At the most analytical teams, baseball analysts are now being scouted and recruited just like players. Analyst hires and assessments of a team’s capabilities are covered in ESPN magazine’s annual “Analytics” issue, Nate Silver’s fivethirtyeight.com website, and the mainstream media. And the MIT Sloan Sports Analytics Conference routinely draws from professional teams in every sport imaginable, and is attended by thousands.

One early adopter was in football. The New England Patriots, for example, have been particularly successful, winning five Super Bowls in the last fifteen years, most recently in 2017. The team uses data and analytical models extensively, both on and off the field. In-depth analytics help the team select its players and stay below the salary cap. The team selects players without using the scouting services that other teams employ, and it rates potential draft choices on such nontraditional factors as intelligence and willingness to subsume personal ego for the benefit of the team.

The Patriots also make extensive use of analytics for on-the-field decisions. They employ statistics, for example, to decide whether to punt or “go for it” on fourth down, whether to try for one point or two after a touchdown, and whether to challenge a referee’s ruling. Both its coaches and players are renowned for their extensive study of game films and statistics, and head coach Bill Belichick has been known to peruse articles by academic economists on statistical probabilities of football outcomes. Off the field, the team uses detailed analytics to assess and improve the “total fan experience.” At every home game, for example, twenty to twenty-five people have specific assignments to make quantitative measurements of the stadium food, parking, personnel, bathroom cleanliness, and other factors. External vendors of services are monitored for contract renewal and have incentives to improve their performance.²²

Other NFL teams that make extensive usage of statistical analysis include the Atlanta Falcons, Baltimore Ravens, Dallas Cowboys, and Kansas City Chiefs. Other teams use analytics too, but more sparingly. For example, the Green Bay Packers analyzed game films of one running back with a fumbling problem, and determined that the fumbles only happened when the player’s elbow wasn’t horizontal to the ground when he was hit.²³ Despite the success of the Patriots and these other teams, some teams in the NFL have yet to grasp the nature and value of analytical competition.

In contrast, professional basketball was historically less quantitatively oriented than baseball, but the numeric approach is now revolutionizing the sport. Several teams, including the high-performing San Antonio Spurs and the Golden State Warriors, have hired statistical consultants or statistically oriented executives. In 2007, the Houston Rockets chose a young, quantitatively oriented executive who previously managed information systems and analytics for the Boston Celtics to be their GM. Daryl Morey, an MIT MBA and cofounder of the MIT Sloan Sports Analytics Conference, considers baseball sabermetrician Bill James to be his role model, and argues that analytics in basketball are similar to those for moneyball in baseball. “It’s the same principle. Generate wins for fewer dollars.”²⁴ As in baseball and football, teams and their analysts are pursuing new measures, such as a player’s value to the team when on the court versus when off of it (called the Roland Rating after amateur statistician Roland Beech).

Analytical competition is advancing rapidly in international sports. Soccer (or football, as it is known outside the United States) teams employ similar techniques. AC Milan, one of the most storied teams in Europe, uses predictive models to prevent player injuries by analyzing physiological, orthopedic, and psychological data from a variety of sources. Its Milan Lab identifies the risk factors that are most likely to be associated with an injury for each player. The lab also assesses potential players to add to the team. Several members of the 2006 FIFA World Cup–winning Italy national team trained at Milan Lab.

The level of analytical competition in professional soccer has accelerated dramatically over the past few years. Sports writer Graham Ruthven describes the dramatic change that took place over the past few years this way: “Every Premier League club now employs a team of video and data analysts . . . graphs and pie-charts are now as much a part of the sport as the dressing room blackboard.”²⁵

Oliver Bierhoff, manager of the 2014 FIFA World Cup Champion German national football team, explains the big data challenge of a fluid game like soccer: “In just 10 minutes, 10 players with three balls can produce over seven million data points.”²⁶ The team relies on an array of sensors and on-field cameras along with a system called Match Insights to make sense of the data, allowing it to tailor its training and preparation for each match. Match Insights is used by both coaches and players, almost like a video game, to assess individual opponent’s capabilities and devise game strategies.

Why all this activity in professional sports, and what difference does it make for other types of organizations? There are many themes that could cut across sports and business. Perhaps the most important lesson from professional sports analytics is its focus on the human resource—on choosing, appropriately compensating, and keeping the best players. This is not a widespread practice in the business “talent management” domain, but it is growing rapidly. As executive and individual contributor salaries continue to rise, it may be time to begin to analyze and gather data on which people perform well under what circumstances, and to ensure that the right players are on the team. A few firms like Google have already adopted a more analytical approach to human resource management, but sports teams are still far ahead of most other organizations.

Analytical competition in sports also illustrates the point that analytics arises first when sufficient data is present to analyze. If there is a business area in which large amounts of data are available for the first time, it will probably soon become a playing field for analytical competition. Analytical innovators in professional sports also often create new measures, and businesspeople should do the same.

Finally, technology enables more opportunities for sports (and other businesses) to create entirely new types of data. This new data creates additional opportunities for competitive advantage. As Mark Cuban, owner of the Dallas Mavericks told ESPN.com, “. . . all teams now use all the data available to them to make personnel decisions, so the market has become more efficient. We have strived to introduce new and exclusive sources of data so that we can improve performance of our players.”²⁷ Daryl Morey at the Houston Rockets was the earliest adopter of video data in the NBA for the same reason. Analytical teams have harnessed technologies that allow them to make more informed, data-driven, real time decisions during actual games. In the same way, analytical businesses are using cutting edge technological innovations to become more agile in changing market conditions.

While analytics is a somewhat abstract and complex subject, its adoption in professional sports illustrates the human nature of the process. When a team embraces analytical competition, it’s because a leader makes a decision to do so. That decision can often be traced to the leader’s own background and experiences. Analytical competition—whether in sports or business—is almost always a story involving people and leadership.

It’s also no accident that the sports teams that have embraced analytical competition have generally done well. They won’t win championships every year, of course, but analytical competitors have been successful in every sport in which they have arisen. However, as analytical competition spreads—and it spreads quickly—teams will have to continue innovating and building their analytical capabilities if they wish to stay in front. Whatever the approach to competition, no team (or firm) can afford to rest on its laurels.