In 1997, a computer named Deep Blue defeated Garry Kasparov, the world chess champion at the time. In 2011, another computer, Watson, competed and won against former champions of Jeopardy!, the popular U.S. television quiz show. Both events changed perceptions about what computers could do. Deep Blue demonstrated the power of new parallel processing technology, and Watson showed that computers can understand ordinary language to meet the challenges of the “real world.”
In computer science terms, Jeopardy! is much harder than chess. Whereas Deep Blue used specialized computer chips to calculate outcomes of possible chess moves, Watson answered unpredictable questions put forward in peculiarly human speech patterns. Today, almost any computer can scan a database to match structured queries with answers. In contrast, Watson was able to “read” through a massive body of human knowledge in the form of encyclopedias, reports, newspapers, books and more. It evaluated evidence analytically, hypothesized responses and calculated confidence levels for each possibility. It offered up, in a matter of seconds, the one response with the highest probability of being correct. And it did all that faster and more accurately than its world-class human opponents.
New analytical tools for making decisions, such as Watson, are bringing about entirely new opportunities. With the digitization of world commerce, the emergence of big data and the advance of analytical technologies, organizations have extraordinary opportunities to differentiate themselves through analytics. The majority of organizations have seized these opportunities, according to this study, “Analytics: The Widening Divide,” by the MIT Sloan Management Review and the IBM Institute for Business Value. Fifty-eight percent of organizations now apply analytics to create a competitive advantage within their markets or industries, up from 37% just one year ago (see Figure 1).1 Significantly, these same organizations are more than twice as likely to substantially outperform their peers. To understand how organizations are using analytics today, we surveyed more than 4,500 executives, managers and analysts from more than 120 countries.
Our initial joint study in 2010 identified three progressive levels of analytical sophistication: Aspirational, Experienced and Transformed (see Figure 2).2 Year-to-year comparisons of these groups reveal that Experienced and Transformed organizations are expanding their capabilities and raising their expectations of what analytics can do, while the Aspirational organizations are falling behind. This growing gap has major implications for businesses seeking to make the best possible decisions based on a flood of insight arising from the interconnected world.
We closely examined what the Transformed organizations, the most sophisticated users of analytics, are doing well and found three key competencies: (1) information management, (2) analytics skills and tools, and (3) data-oriented culture. Mastering these competencies enables organizations to gain full benefit from analytics.
We also found, however, that organizations take one of two different paths to achieving analytics sophistication. Each path is comprised of a different mix of competencies, so organizations choose the best route to follow based on their strengths and circumstances. The chosen path influences their overall approach to analytics, the kinds of projects they pursue — and the steps they will need to take to achieve full analytical prowess.
The Gap is Widening
The growing gap between Transformed and Experienced groups, on the one hand, and the Aspirational group, on the other, is evident on two fronts: using analytics to create competitive advantage, and integrating analytics into strategy and operations. Among all respondents, the number of companies using analytics to create a competitive advantage has surged by 57% in the past year. Yet all of the gains in competitive advantage have been made by the Transformed and Experienced groups, which grew by 23% and 66%, respectively, from 2010 to 2011. The Aspirational segment, by contrast, fell 5% behind during the same period (see Figure 3).
The widening divide between organizations is also evident in the use of analytics to inform core business strategy and day-to-day operations. Fully 70% of the Transformed and 55% of the Experienced groups say they have increased their use of information and analytics in their business strategy and operations in the past 12 months. Only 34% of the Aspirational group has done so (see Figure 4).
Transformed Organizations Use Analytics More Widely
Financial and operational activities have historically been data-driven, and are typically the first areas where analytics is adopted.3 A majority of organizations affirmed they rely on data and analytics to manage financial forecasting, annual budget allocations, supply chain optimization and streamlining operations. Among Aspirational and Transformed organizations alike, these were the four areas where leaders rely on analytics to make decisions.
By comparison, analytics is less frequently relied upon for decisions involving customers, business strategy and human resources. On average, fewer than one-quarter of Aspirational organizations said they rely primarily on data and analytics to make key decisions in these areas, compared to one-half of Transformed organizations (see Figure 5).
Transformed Organizations Leave Others Behind
Today’s business environment is characterized by increasing uncertainty and competition. At the same time, customer loyalty is eroding. All of this, and more, makes it very difficult for organizations to gain lasting benefits unless analytics is applied broadly. A piecemeal approach to analytics adoption misses the opportunity to link supply chains to customer channels, for example, or financial forecasts to more precise resource planning.
Most organizations are expanding their use of analytics beyond finance and operations. However, the Transformed group has set the pace and has already distinguished itself in the marketplace. Overall, organizations that used analytics for competitive advantage were 2.2 times more likely to substantially outperform their industry peers. Transformed organizations in that group were 3.4 times more likely to do so.
While Transformed organizations use analytics broadly across the organization, their business objectives are highly focused. Using an analytical technique called binning, we found that Transformed organizations are concentrating on three critical areas that span the enterprise: speed of decision making, managing enterprise risk and understanding customers.
Moving Faster with Analytics
Big data, and the fast pace and complexity of today’s marketplace, require that leaders make decisions faster than ever before. Nearly 7 out of 10 CEOs interviewed for the IBM Global CEO Study 2010 told us that they already face unprecedented uncertainty and volatility — and are expecting more ahead.4 We found that Transformed organizations keenly appreciate the value of more precise and near-real-time decisions, and are more than three times more likely than Aspirational organizations to focus intensely on the speed of decision making (see Figure 6).
While proven instincts and experience were once a leader’s best guides, decision makers are now in a position to use an extraordinary amount of data to inform their choices. Decisions based on large amounts of data, however, can’t come at the price of speed. The digital transformation of business has put pressure on organizations to become more effective in their reactions to market shifts and to shorten the time to market for new products and services.
Organizations focused on the speed of decision making are using analytics to manage operations and improve output levels based on real-time supply and demand management. They automate their inventory replenishment processes and optimize production by doing things such as embedding triggers that signal maintenance needs before equipment breaks down.
We found that two-thirds of Transformed organizations are relying on analytics to manage day-to-day operations, more than four times the percentage of Aspirational organizations. In some ways, using analytics for these immediate operational needs can be more difficult than crafting long-term strategies. Whereas future strategies are typically iterated over time, operational decisions require precise and accurate insights to be available much more quickly: hence, the need for analytics speed.
The speed at which some organizations operate today outpaces the processing capacity of the human brain. McKesson, for example (see case study), processes more than 2 million orders per day. To operate at this speed, McKesson has embedded algorithms into the intake process to manage orders, issue stockroom holds and process inventory replenishments without human intervention.
When a pharmacist re-orders at the end of the day, the product arrives by 10 a.m. the next day. “That’s what we do,” said Robert Gooby, vice president of process redesign. “We need to be outstanding in our execution, and lower costs,” he said, explaining the manpower to manually keep up with that level of demand is cost prohibitive. In a $112 billion company, he noted, even a 99.9% degree of accuracy in execution can lead to the loss of more than $100 million. “We need to reduce our write-offs to the millions, not hundreds of millions. And when you’re talking about that level of accuracy, you have to rely on data and analytics.”
Analytics confers greater agility, acuity and certainty in today’s fast-changing business environment. It allows leaders to isolate the components of complex activities and ecosystems, as well as to see and understand the dynamic interrelationships of their businesses and the markets they operate in. Detecting and analyzing trends and patterns, they can predict what is most likely to occur next. Using modeling techniques and what-if scenarios, they can even prescribe the next best action.
Managing Risk for Strategic Advantage
Propelled by the digital transformation of entire industries and the globalization of business operations, leading organizations continuously re-evaluate and re-define the strategic decisions that underpin their success. Almost 3 out of 4 Transformed organizations use analytics to guide their future strategies compared to fewer than 1 in 7 Aspirationals. These new business and operating tactics promise competitive differentiation. But they are not without risk.
A report from the Corporate Executive Board found that strategic risks, rather than financial risks, were responsible for 68% of severe market capitalization declines between 1998 and 2009. These strategic risks include decline in demand and competitor infringements on core products, destructive price wars and margin pressure, and failure to expand new revenue sources.5 Yet a 2011 American Productivity and Quality Center (APQC) study found that 56% of the respondents admitted they were least prepared to manage these kinds of risks.6 Managing strategic risk calls for a better line of sight into the organization and its markets, and an ability to anticipate and act ahead of events that might derail progress.
Transformed organizations understand that in the face of growing volatility and uncertainty, they must improve their abilities to anticipate and predict. We found that 86% of Transformed organizations were highly focused on understanding the full range of organizational risks that can impact their businesses. None of the Aspirational organizations had the same level of focus (see Figure 7).
By using analytics across the enterprise to monitor, detect and anticipate events, organizations are learning to avoid unnecessary risk. Armed with real-time information, they are monitoring supply levels to help minimize disruptions. They are automating tasks — moving inventory from one location to another when a trigger is set off, for example — and using predictive analytics to anticipate needs based on dynamic variables like weather or political upheavals. The most adept are forging bold strategies, such as taking a risk-based pricing approach to introduce services and products that once would have been deemed too risky to develop. Others are anticipating regulations before they are enacted in their markets, proactively adjusting their products to get ahead of regulatory constraints.
Chevron Corp., a global energy company, understands the link between risk and performance. Each drilling miss can cost the company upward of $100 million. But the seismic surveys it uses to evaluate potential drilling sites — each up to 50 terabytes of data — take an enormous amount of time and computing power to analyze.7 Chevron’s geologists always knew they wanted to do more, but were restrained by one of the biggest challenges organizations face in using analytics: a lack of bandwidth to focus on analytics.
In the summer of 2010, the U.S. federal government temporarily suspended all deep water drilling permits in the Gulf of Mexico, regulation that essentially shut down all oil exploration in the region for nine months. Rather than sit idle, geologists at Chevron seized the opportunity. Using recent advances in computing power and data storage capabilities, as well as refinements to their already advanced computer models, geologists were able to improve the odds of drilling a successful well at certain of its deep-water prospects to nearly 1 in 3, up from odds of 1 in 5 or worse. The intensive review led the company to change the next year’s drilling schedule to explore several higher-probability wells first.8