Analytics Culture: The Secret to SuccessIt’s not just about data, technology, and quants

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Given the buzz around analytics, why are so few organizations getting it done? According to a recent Accenture survey of 600 executives, 8 out of 10 companies have not achieved their goals in analytics. And only 1 out of 12 respondents expressed satisfaction with the return on their investments.1

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A quick scan of how high-performing organizations use analytics answers that question. At Harrah’s, now Caesars Entertainment, service delivery metrics, such as the average time it takes to greet a customer or to deliver a drink, are reported to management on Sunday. The very next day, the property’s general manager can expect a call if the revenues are down compared to the same week the previous year.2

After an analysis of its loyalty program data showed that 7% of customers were responsible for 43% of its sales, Best Buy redesigned its store layout to meet the needs of those loyal customers. Further, this giant retailer quantified the value of employee engagement to customers’ in-store experience — a 0.1% increase in engagement is worth more than $100,000 in a store’s annual income — to inform its investments in the workforce.3

Quick action is a distinguishing feature of examples such as these. High performers do not simply gather and analyze data; they use the resulting insights to make smarter decisions faster. Their leaders are in synch on how to employ analytics in the service of their strategy, and that vision percolates down through the organization. As a result, middle managers measure the right metrics, make decisions based on the best data available, and understand the significance of immediate action on the basis of those decisions.

No technological solution, simply layered on top of existing processes and culture, can achieve these results. Further, existing analytical talent in organizations rapidly grows frustrated with the added complexity. As one market researcher said, “We buy tons of data on the consumer products market. We analyze the hell out of it. The problem is, we don’t change anything as a result of it.”4

Access to robust data is essential to progress toward analytical maturity. The value of the data is unlocked, however, when companies develop the capabilities to analyze what they gather. Do your employees know how to use scenario-based or workflow analysis tools? Can they overcome organizational barriers and build momentum behind their positions? Capabilities such as these are necessary to drive transformative change fueled by analytics.

As talent is hired and capabilities expand, pockets of analytical excellence develop in companies. Typically, however, these pockets produce little of strategic value because their scope is bounded by their unit or department. Opportunities that require multifunctional involvement are missed.

Contrast that scenario to one of a high-performing organization with a culture that understands and celebrates the capabilities required to win with analytics. In a culture such as this, respect for data coupled to a pervasive curiosity results in people asking questions such as, “Do we think this is true, or do we know?”5 This is the key to success — the “secret sauce” — of analytics competitors. Due to the hard work required to build and maintain such an analytics culture, succeeding in this endeavor raises the table stakes in the market. Accenture uses the term “organizational effectiveness” to structure the multifaceted endeavors required to foster an analytics culture.

One facet of organizational effectiveness, for example, is promoting and reinforcing top leaders with analytical vision, passion, and the ability to nurture leaders at all levels. Another is developing, engaging, and organizing talent with the right skill sets. The design of the operating model and the processes used to problem solve and execute at a strategic level are covered by this term, as are the processes and software required to embed analytical tools, methods, and behaviors. The magic occurs when the interpersonal and process strengths of an innovative, results-focused culture are combined with the technical and data-mining skills required to deliver high performance.

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Leading from the top and the middleThe single most important step you can take is to promote leaders with a passion for data analysis at every level. Leaders in the C-suite need to model appropriate behavior, but they do not own analytics in the organization. Every manager and leader in the middle ranks has to take responsibility for creating a more fact-based culture because through ownership comes commitment.

It is also important for executives to take a hard, honest look at how in touch they are with the existing culture before they attempt to drive transformative change. On a recent Accenture survey, leaders in 400 organizations responded favorably to statements such as “This organization places a high value on collecting objective data to improve the quality of decision-making” and “In this organization, you get ahead based on merit and objectively demonstrated performance rather than political behavior.” In all but 2 of these 400 organizations, employees answered

these questions in a very different way from their leaders.6 This is no less true for changes driven by analytic transformations.

Having an accurate understanding of their organization’s readiness allows senior leadership to assess gaps and define a path forward to creating an analytics culture. And this effort, in turn, helps them to get in synch with each other regarding how analytics will be used to support their strategic vision — the value they want to gain. By translating that consensus down through the middle ranks, leaders can confer ownership of analytics to the appropriate people and thereby avoid what we call “the frozen middle.”

Often an effective approach to achieving analytics goals is to recognize how factors play together. As Tom Anderson, CEO of Integrity Interactive, has said, “The beauty of analytics is that you find lots of things that can be incrementally improved. If it’s a multiplicate business, [like] medical finance, and you can improve each factor—the number of doctors times the number of patients

times the percentage that seek financing—by 10 percent, it’s huge.”7

Harrah’s took a similar approach by recognizing the role of a number of service delivery factors in a customer’s satisfaction. Tracking each of these factors, such as the time required to greet a customer or deliver a drink, allows them to be targeted separately if revenues slip.

Breaking down silosLike unengaged leaders, organizational silos are quick killers of an analytics culture. Silos naturally develop as organizations grow. Analytics in the service of the enterprise, however, requires cross-functional collaboration — what one UK-based healthcare company calls “boundaryless” collaboration. To address this tension, organizations need processes to facilitate people working together from all parts of the organization. For example, they need to decide who will lead these cross-functional analytics efforts. And ask, “How do we help our people balance the interests of their unit versus the interest of the enterprise?”

Fostering a High-Performing Analytics Culture

How do you get it done? What steps will help you progress along the path to analytic maturity? Three areas — leadership, breaking down silos, and developing and keeping talent — are fundamental to fostering an analytics culture.

Monthlybusiness review

Quarterlyhealth check

Weeklyforecast adjustment

Performancemonitoring

Value realization Insight validation

Insight generation

Execution

Questions on key metrics

Marketing Supply chain ManufacturingHR Finance

Technology Enablers

Core analytics

Functional analytics

Cross functional analytics

Analytics center of excellence

Bland packaged workbench Root cause analysis tools Statistical models optimization tools

Figure 1. Closed loop decision making process

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The products of collaboration can then be applied in different parts of the organization. Procter & Gamble is an example of an analytics high performer that has established a central team to contribute to the bottom line in a variety of disciplines. This team, called Global Analytics, tackles challenges such as manufacturing site location, inventory management, supply chain design, and strategic decision making.8

Creating a single repeatable process for integrating analytics into everyday work would be a powerful way to counteract the rigidity of “silo-think.” A closed loop decision making process such as that shown in Figure 1 defines how data is leveraged to test hypotheses and support decisions anywhere in the enterprise. Recent Accenture research shows that only one in five companies currently has such a repeatable process in place.9

Breaking down silos also facilitates the collaboration required to stay ahead of the competition. For example, as a strategic partner rather than an order taker, IT can help business units access

the quality data needed to forecast more accurately, price more appropriately, and tailor offerings to customers or citizens more effectively. Just as important is evaluating how other companies, even in different industries, are using analytics. Agile organizations know they don’t have to do all the analytics heavy lifting themselves — there is a global analytics community brimming with experts, peer discussions, and case studies.

Selecting, motivating, and retaining talentA smattering of quants around an organization is not an analytics culture. Upskilling the workforce in analytic capabilities is quickly becoming essential just to keep pace with market.

As Tom Anderson has said, “You have to become a teacher. Some people already have the problem-solving capabilities, and you have to teach them the math. Others know the math, but don’t know how to apply it to business problems.”10 Training workers in IT skills has

consumed the organization’s training departments in the past; the next 20 years will be about integrating analytics into everyday work.

The bar is also rising for new hires. One financial services company, for example, requires all potential employees, including senior executives, to take a series of tests to determine analytical and financial aptitude. One successful hire joked that he might have been “the only HR guy who could pass their math test.”11

Analyzing the talent in the organization is as important as hiring talent with a passion for analytics. This application of analytics can provide a clear advantage to companies that use customer satisfaction as their differentiator. Harrah’s, for example, analyzes the effects of its health and wellness programs on employee engagement. In this way, the company showed that a rise in preventative care visits to its on-site clinics resulted in an annual decrease (by millions of dollars) in urgent-care costs.12

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One reason Harrah’s chose to capture wellness metrics is because its leadership team understands that happy, healthy employees provide better customer service. Gathering this data provides insights on revenue growth as well as on health insurance and sick days. It is an example of the value of selecting the right metrics, however seemingly unrelated, to help make decisions that support the corporate strategy.

Another use of talent analytics is to retain high-performing employees. Google has placed sufficient strategic importance to talent retention that its people analytics function has a staff of 30 researchers, analysts, and consultants. Among the tools that this function has provided Google is a list of eight managerial behaviors that help them inspire and develop their employees.

As Laszlo Bock, Google’s VP of people operations, says, “It’s not the company-provided lunch that keeps people here. Googlers tell us that there are three reasons they stay: the mission, the quality of the people, and the chance to build the skill set of a better leader or entrepreneur. And all our analytics are built around these reasons.”13

An analytics culture is necessary to drive transformative change, and deliver on the full potential of your analytics investments. A prerequisite for high performance is being organizationally ready to make faster, smarter decisions and to drive cross-departmental ownership of the implementation of those decisions. While there are multiple facets involved in organizational effectiveness, getting the right leaders in place, breaking down silos, fostering the necessary culture and developing your talent are good places to start.

References

1 Dave Rich, Brian McCarthy, and Jeanne Harris, “Getting Serious About Analytics: Better Insights, Better Outcomes.” Accenture, 2010.

2 Walter E. Shill and Robert J. Thomas, “Exploring the Mindset of the High Performer,” Outlook Journal, October 2005.

3 Jeanne Harris, “How to Turn Data into a Strategic Asset,” Outlook Journal, 2010.

4 Thomas H. Davenport, Jeanne G. Harris, and Robert Morison, Analytics at Work: Smarter Decision, Better Results. Harvard Business School Publishing 2010.

5 CEO Gary Loveman, Harrah’s Entertainment, a casino and hotel empire, quoted in Dave Rich, Brian McCarthy, and Jeanne Harris, “Getting Serious About Analytics: Better Insights, Better Outcomes.” Accenture, 2010.

6 Accenture’s High Performance Culture Research.

7 Quoted in Thomas H. Davenport, Jeanne G. Harris, and Robert Morison, Analytics at Work: Smarter Decisions, Better Results. Harvard Business School Publishing 2010.

8 Jeanne G. Harris, “Research Note: How Consumer Goods Companies Compete on Analytics to Achieve High Performance,” Accenture Institute for High Performance Business, 2007.

9 Dave Rich, Brian McCarthy, Jeanne Harris, “Getting Serious About Analytics: Better Insights, Better Outcomes,” Accenture, 2010.

10 Quoted in Thomas H. Davenport, Jeanne G. Harris, and Robert Morison, Analytics at Work: Smarter Decisions, Better Results.” Harvard Business School Publishing 2010.

11 Thomas H. Davenport and Jeanne g. Harris, Competing on Analytics: The New Science of Winning. Boston, MA: Harvard Business School Press, 2007.

12 Thomas H. Davenport, Jeanne Harris, and Jeremy Shapiro, “Competing on Talent Analytics.” Harvard Business School Publishing, 2010.

13 Quoted in Davenport, Harris, and Shapiro, “Competing on Talent Analytics.”

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