What does it mean to compete in analytics? It means that an intensive use of analytics, which is key to the strategies and business models of these firms, helps them to compete. In other words, “top management had announced that analytics was key to their strategies; they had multiple initiatives underway involving complex data and statistical analysis; and they managed analytical activity at the enterprise level” (Harvard Business Review, Cambridge: 2006).
In researching more than 50 firms that are trying to make extensive use of analytics (see “The Rise of Competition in Analytics” below), I found that several different C-level executive roles come into play. One, of course, is the CEO, who must set the cultural and strategic tone for analytics competition. Most important to the day-to-day execution of analytical strategies, however, are the chief financial officer (CFO) and chief information officer (CIO).
The Rise of Competition in Analytics
Most of the viable competitive strategies that organizations use today involve optimizing key business processes. Instead of serving all customers, companies want to serve those with the highest level of profitability and lifetime value. Instead of receiving goods and services whenever they happen to arrive, organizations try to optimize supply chains, minimizing disruptions and in-process inventory. Instead of looking backward at their business performance and making ex post facto adjustments, companies try to understand how non-financial performance drives optimum financial performance and to make accurate forecasts of future performance. Instead of throwing money at business problems, businesses want to optimize their use of capital.
But strategies involving optimization require something different from those based on taking business as it comes. They need extensive data on the state of the business environment, the company’s place within it, and extensive analysis of the data to model that environment, predict the consequences of alternative future actions, and guide executive decision-making. Moreover, these strategies require analysts and decision-makers who both understand the value of analytics and how best to apply it to drive enhanced performance. Companies that strive to optimize their business performance using this data-intensive approach are said to compete in analytics and analytical capabilities.
Many companies attempt to optimize their business processes, but most fail to develop the analytical capabilities necessary for them to succeed. Today, most large organizations have some sort of analytics applications in place and some business intelligence tools installed. However, they are typically marginal to the success of the business and are managed at the departmental or individual level. No matter how valuable these activities may be, they are invisible to senior executives, customers, and shareholders, and they can’t be said to drive the company’s competitive strategy.
Of course, a few organizations – particularly financial investment and trading businesses – have competed in analytics for decades. What is new now is how the competition in analytics is spreading to a variety of other industries, from consumer finance to retailing to travel and entertainment to consumer goods, and within a company, from individual business units to an enterprise-wide perspective. For these organizations, analytics is becoming a primary basis for competition. They use analytics tools either to change the game or to perform substantially better in the existing game.
These analytics competitors have several key attributes that distinguish them as such:
- They make extensive use of sophisticated analytics – predictive models, simulation, and optimization – employing these tools first deeply within a particular business domain, and then broadly across the organization.
- They apply analytics to a clear, distinctive capability that they are attempting to optimize. It may involve customer loyalty or marketing, supply-chain management, risk and asset management, or even human- resource management.
- They take an enterprise-wide approach to managing information and analytics.
- They have senior executive teams who are fully committed to analytical strategies and capabilities.
Having a strong analytics orientation seems to be a function of data and computation, and indeed those resources are important. Most of the analytics competitors have an SAP or a third-party ERP system installed, and they take advantage of the data in those systems to manage their businesses.
But having such systems and data is common in large organizations. What is not widely available is the human dimension of competition in analytics: leadership, disciplined management, and deep expertise. These human attributes truly differentiate the successful competitors in analytics.
The Chief Financial Officer
In most organizations the CFO is responsible for financial processes and information. Therefore, analytics efforts in these domains are also the CFO’s concern. Since most analytics projects involve some sort of financial information or returns, the CFO is at least a partial player in virtually all of them.
In some companies the CFO leads the analytical charge. To fill that role effectively, however, a CFO must focus on analytical domains as well as finance and accounting. For example, at State Farm Mutual Automobile Insurance Company, the largest automobile insurance firm in the United States, CFO Michael Tipsord has led analytics efforts in cost management; he has also monitored and championed analytical initiatives in the company’s claims, actuarial, and marketing areas. Tipsord made it his responsibility to try to establish an appropriate overall balance between intuitive and analytical thinking among the company’s employees.
Jim Muehlbauer, senior VP of finance at Best Buy Co., has made analytics his primary focus, and not just those involving finance. The company had a strong focus on customers and customer-orientation, and he played an active role in developing measures, systems, and processes to advance that capability. The company already had good information and analytics on some of its business drivers such as labor, space allocation, advertising, and product assortment. Muehlbauer’s goal was to add customer-relationship and customer-segment information to those factors. Since his role also incorporated working with the external financial community (Wall Street analysts, for example), he tried to make the company’s analytics story well known to the outside world. Muehlbauer viewed his role as including the advocacy of a strong analytics orientation in a culture that didn’t always emphasize it. He notes, “I’m not the only advocate of analytics in the company – I have a number of allies – but I’m trying to ensure that we tell our story, both internally and externally, with numbers and analytics.”
CFOs may also be the drivers of an analytics orientation when the functions they manage are key to their companies’ performance. At Bank of America Corp., for example, CFO Al de Molina views himself as a major instigator of analytical activity. The bank had tried – and largely failed with – a big data warehouse in the early 1990s, so managers were understandably wary of gathering together to integrate the data. But in his previous job as head of the Treasury function, de Molina had felt that to accurately assess the bank’s risks, the bank needed to consolidate information about assets and rates. Since the bank was growing rapidly and assimilating several acquisitions, integrating the information wasn’t easy but de Molina pushed it anyway. The CFO has also taken responsibility for analytics around U.S. macroeconomic performance.
Since it has a wealth of data on the spending habits of American consumers, Bank of America can predict the monthly fluctuations in macroeconomic indicators that drive the capital markets. This has obvious beneficial implications for the bank’s investors. Both the interest-rate risk and macroeconomic-analytics domains are obvious ones for a CFO’s focus. De Molina largely defers to other executives where, for example, the bank’s extensive marketing analytics are concerned.
Several CFOs at companies that compete in analytics support their organizations’ analytics orientations, even when they are not leading the charge. At Partners HealthCare System, for example, which is a large healthcare provider in New England with several leading academic medical centers, the primary analytics orientation involves “evidence-based medicine” and “clinical decision support.” These approaches involve the use of scientific and clinical research data to alter treatment processes. It would not be logical for Partners HealthCare’s CFO, Peter Markell, to oversee analytics in medicine.
However, the CFO plays a critical role. His focus is to “help the business side catch up with the clinical side” of Partners HealthCare. This includes analyzing health-insurance denials, helping to create and employ measures of quality and patient safety, and analyzing costs and revenues with respect to different payers, service lines, and procedures. Markell is also involved in decisions about financing clinical decision-support initiatives such as intelligent physician order entry, electronic medical-records enhancement, and personalized medicine based on genomic data. Markell sees himself as a cheerleader for a more consistent orientation toward analytics throughout the business. Advocacy is necessary because while the physicians accept the analytics vision in principle, they don’t always welcome the change and disruption it entails.
The Chief Information Officer
The CIO has an extremely important role to play in any analytics competition. The most traditional approach to analytics for a CIO is through technology. Overseeing analytical technology is necessary for the CIO at an analytics competitor, although perhaps not sufficient. The leader of an organization’s analytical technology initiatives has several possible roles, including inspiring leader, architect, and financial analyst. The CIO may not have to play those roles, but those who play them should report to the CIO.
The technology environment at an analytics competitor has certain attributes, which could make up a substantial research report themselves. Some of them are:
- A robust, integrated transaction-software environment. It’s impossible to do good analytical work without high-quality transaction data that is consistent across the enterprise. Transaction data can come from a variety of sources including point-of-sale systems and e-commerce or Web transactions, but it is typically the result of an integrated, comprehensive ERP system such as the one from SAP.
- An accessible, high-quality data environment. You must capture transaction data and make it available in data marts or warehouses. CIOs at companies that compete in analytics, such as Capital One Services, report that their IT organizations devote extraordinary efforts to integrating, cleaning, and storing data. At Capital One, 25% of the IT employees work in data management.
- High-quality analytics software. Firms with a strong analytics orientation need software to extract and load data, query, create reports, and make statistical and quantitative analyses. Some centralized control over analyses that use these tools may be necessary. Given the strategically critical nature of analysis in these firms, it’s unlikely to be entrusted to user-managed spreadsheets.
- Analytics-focused hardware. Large data warehouses and huge statistical analyses can place unusual demands on computer hardware. Predictive modeling often requires real-time or rapid analysis. Computation-intensive hardware, including 64-bit processors, may be necessary to process large amounts of data rapidly.
All of these resources must be planned and mapped through an analytics architecture that includes:
- Data architecture, which includes data sources as well as data quantity, quality, relevance, management, and context
- Population architecture, which describes how the data is extracted, cleaned, transmitted, and loaded to “populate” databases
- Repository architecture, which completes the transformation of the data into management information and stores it for use
- Application architecture, which describes the analytical tools and applications
CIOs who want to play an even more valuable analytical role than simply overseeing the technology should focus on the “I” in their titles – information. Competition in analytics, of course, is all about information. Do we have the right information? Is it truly reflective of our performance? How do we get people to make decisions based on information? These issues – particularly the last one, which involves the organization’s “information culture” – are more complex and multi-faceted than just buying and managing the right technology, but organizations that wish to compete in analytics need to master them.
The CEO has the primary responsibility for changing the culture and the analytical behaviors of employees. But CIOs can help in this regard, too. They can work with their executive peers to decide what behaviors are necessary and how to elicit them. At least two CIOs I studied are clearly focused on changing the analytics culture of their organizations. Irving Tyler, formerly CIO of Quaker Chemical Corp. (now CIO at IMS Health), provided the results of data analysis and reporting through email alerts to Quaker Chemical employees for several years. He believed that the more information users receive, the more it shapes their ability to solve problems and make decisions based on information rather than intuition. Tyler also worked with other executives on how Quaker Chemical made key decisions and solved business problems.
At the telecommunications firm Verizon, the CIO’s objective is to create a change in the analytics culture similar to the one at Quaker Chemical. Verizon has long been analytics-oriented, but decisions have typically been made slowly and pushed up the organizational hierarchy. Verizon CIO Shaygan Kheradpir is trying to change this culture through continual exposure to information. He created a form of continuous scorecard in which hundreds of performance metrics of various types are broadcast to PCs around the company, each occupying the screen for 15 seconds. The idea is to get everyone – not just senior executives – focused on information and what it means, and to encourage employees at all levels to address any issues that appear in the data. Kheradpir feels that he is beginning to see signs of cultural change from using the scorecard.
The CIO may also provide a home and a reporting relationship for specialized analytics experts. Such analysts make extensive use of IT and online data, and they are similar in temperament to other IT people. Some analytics competitors in which the analytical groups report to the CIO include Procter & Gamble, Schneider National, and Marriott International. Procter & Gamble, for example, has recently consolidated its analytics organizations for operations, supply chain, marketing, and other functions. This consolidation allows P&G to deploy a critical mass of analytics expertise to address the company’s most critical business issues. The P&G Global Analytics group, which includes about 120 analysts, reports to CIO Filippo Passerini, and is part of an overall emphasis within the IT function on information and decision-making. In fact, the IT function has been renamed “Information and Decision Solutions” at P&G.
Other Attributes of Analytics Executives
Senior executives in a highly analytics-oriented organization have several generic attributes. For example, they should be passionate believers in analytics and fact-based decision-making. You can’t inspire others to change their behavior in a more analytical direction if you’re not passionate about the goal. They should also have some appreciation of analytics tools and methods. The senior executives of competitors in analytics don’t necessarily have to be experts in analytics, but they do need to have an awareness of what kinds of tools make sense for particular business problems and what the limitations of those tools are.
Analytics executives should be willing to act on the results of analyses. There is little point in commissioning detailed analytics if the outcome won’t change anything. They should also be willing to manage over a meritocracy. With widespread use of analytics in a company, it usually becomes very apparent who is performing and who isn’t. Those who perform well should be rewarded for their performance; those who don’t shouldn’t be strung along for a long time.
Although the roles of CIOs and CFOs are critical, no competitor in analytics can rely solely on leaders in one function or process. The management teams of such organizations have to work as teams to identify and implement analytical strategies. Functional leaders in fields such as marketing and supply chain must also work closely with leaders of support functions such as IT and finance, and these leaders must coordinate their analytics activities over cross-functional processes. Initiatives in various business domains must be closely coordinated. In short, competition in analytics is clearly a team sport. As with any sport, the best wins typically come to those with the best players and leaders.
|Thomas H. Davenport holds the president's chair in the IT management division at Babson College in Wellesley, Massachusetts, and is director of research for the School of Executive Education at Babson. He has written, co-authored, or edited 10 books, including the first books on business-process reengineering, knowledge management, and the business use of enterprise systems. He is the co-author of the new book, Competing on Analytics: The New Science of Winning (HBS Press: 2007). You can reach him at firstname.lastname@example.org.