How CFOs can own analytics

CFO Insights

Leading analytics to support operational decision-making demands a high-level commitment to shift the organization from a historical perspective to a forward-looking perspective. It also requires a willingness to invest in a small group of talented people who can help you determine and implement the analytic capabilities you’re going to need. And importantly, it needs a CFO who can drive analytics for both strategic and operational decision-making.

Much has been made about the unprecedented quantities of data companies collect these days, from their own operations, supply chains, production processes, and customer interactions. And even more has been made of the promise of business analytics to analyze data for competitive advantage in new ways, providing real-time insights across the value chain, from inventory management to pricing.

Who should own analytics remains an open question, however. Findings from The Analytics Advantage*, Deloitte Touche Tohmatsu Limited’s first annual survey on the state of analytics readiness at leading corporations, reveal wide variations in analytics oversight, with the most frequent leader of analytics—named by 23 percent of respondents—being the “business unit or division head,” who also typically has significant budgetary responsibility.

Not far behind, however, is the CFO, at 18 percent (20 percent said there was no single overseer). Finance itself is cited as the area most often found to invest in analytics, at 79 percent (see sidebar, “Organization and oversight: Analytics by the numbers”).

Given that many CFOs and their finance teams traditionally have led and used data-driven analytics for strategic aspects of managing the business, a case can be made for ceding ownership of analytics to the finance chief. Moreover, it only makes sense that by taking the lead to apply analytics to operational decisions as well, CFOs can strengthen ties throughout the business and expand influence outside core finance functions. In this issue of CFO Insights, we will examine where the ownership of analytics currently lies, and offer steps to driving analytics throughout the business.

*About the survey
The Analytics Advantage was commissioned by DTTL to better understand the state of analytics readiness at leading corporations today, and what the future may hold. It was conducted using a mix of online questionnaires to which 100 individuals representing companies in financial services, technology, communications, entertainment, health care, consumer products/retail, and other sectors responded, and “deep dive” interviews with senior executives at 35 companies in North America, China, and the United Kingdom.

Interviews were overseen or conducted by analytics thought leader and author Thomas H. Davenport, President’s Distinguished Professor of Information Technology and Management at Babson College and director of research at the International Institute for Analytics.

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From managing—to running—the business

Finance has long been data-driven, but the availability of big data and the growth of data analytics capabilities have further heightened its importance. But to truly own analytics, CFOs should bridge the gap between strategic and operational decision-making with analytics. That’s a fundamental change in roles: it’s the difference between “managing the business”—the big, upper-level strategic decisions, such as planning, budgeting, and forecasting, that are the CFO’s traditional responsibilities—and “running the business,” the day-to-day, or even minute-to-minute, operational decision-making that typically resides outside the finance chief’s purview (for instance, in the business units, or sales and marketing).

Armed with analytics, however, CFOs can exercise more-centralized control of operational business decision-making, answering questions such as, What price point should be used for this customer on this day? or What inventory products should be pulled forward or out of the supply chain?

Moreover, advanced analytics can allow companies to hedge against volatility and to respond faster, and with greater insight, to changes in the marketplace, to such a degree that the predictive power that analytics offers—in pricing, the supply chain, and other areas—can have an increased impact on how business is done.

While some CFOs may hesitate to lead analytics in operational areas because it’s not necessarily about “big picture” issues, the insights into the customer that analytics can provide go right to the bottom line. A lot of profit can fall between the operational cracks, and analytics can be a game changer in the way it leads to improved operational discipline.

Steps to taking the analytics lead

The following steps can help CFOs and finance organizations take a lead in driving analytics throughout the business.

1. Define the analytics advantage.

CFOs can show how finance can lead analytics by identifying business areas where those analytics can bring value and competitive advantage (for example, drive operational insights). In many cases, finance doesn’t own the data on the operational side of the business that is gained from customer-, pricing-, supply chain-, or asset-tracking. But through finance-supported analytics, the CFO can drive value outside finance’s core functions throughout the business.

At a $1 billion food manufacturer, for example, finance led a spend-analytics initiative that created supply-chain efficiency and reduced costs by improving the management of supply-chain sourcing. Finance developed analytic models that looked at SKU-level profitability, and brought those insights forward to help the operation better understand the types of decisions it could make to reduce costs. Finance crossed over into decision-making traditionally outside its function by using an analytics solution to evaluate sourcing and suppliers, and then deployed the data to buyers on a monthly basis by individual commodity.

Other areas in which finance-supported analytics can drive value outside the finance function include procurement (spend analysis and vendor management), business units (margin-erosion analysis, pricing analytics, and service level and customer profitability alignment), sales and marketing (price-point, revenue-leakage, revenue-driver, demand/price-elasticity, customer-retention, and churn-analyses), supply chain (sales- and finance-linked forecasting, new product introduction profitability, and dollarization effect) and information technology (technology investment planning and prioritization).

2. Deliver operational value with analytics.

Still, CFOs who want to lead analytics across the organization have to show they can deliver on the operational side. Then, once they make the case for leading analytics in operational decision-making and identify the value of doing so, it comes down to delivering that value.

To start, identify an analytics-focused initiative that cuts across areas to make a major impact at the operational level. Demonstrate the value that analytics can bring through inventory management—a better customer experience, or whatever component you need to be thinking about. And don’t be limited by traditional constraints. Ask yourself: “What business decision or goal would drive margin or growth through analytics outside finance?” For example, the business need may lie in marketing or sales, which is why some consumer-products companies are looking at price points, margin yields, and other analytics that can help influence profitability.

Define, articulate, deploy

Delivering analytics insights to operational decision-making is a matter of articulating and capturing the required data, performing the analysis, and, finally, working with business teams to deploy the analytics-based model.

CFOs can drive the benefits of analytics throughout the enterprise either directly, from finance’s analytics team, or by establishing an Analytics Center of Expertise led by finance and reporting directly to the finance chief.

As an example of how CFOs can lead analytics in areas outside finance’s traditional role, consider a telecom whose CFO led an initiative to target the company’s most profitable customers and tailor service—and pricing—levels. He did so by taking the following steps:

  • Defined the operational decision-making challenge: For this telecom, the objective was to gain a larger share of prime customers’ wallets with tiered service.
  • Articulated the analytics solution: The telecom’s CFO directed finance to calculate the lifetime value of individual customers and develop scenario models for discounts, marketing, and rewards to guide interactions with the customer.
  • Deployed: Working cross-functionally to execute the plan and capture value, the telecom’s CFO brought the analytics solution to front-line employees and marketers to derive individual customer profitability from each transaction, in real time.

Leading analytics to support operational decision-making demands a high-level commitment to shift the organization from a historical perspective to a forward-looking perspective. It also requires a willingness to invest in a small group of talented people who can help you determine and implement the analytic capabilities you’re going to need. And importantly, it needs a CFO who can drive analytics for both strategic and operational decision-making.

Organization and oversight: Analytics by the numbers

How should analytics be structured within an organization? Should it be centralized or diffused? And who, ultimately, should “own” it?

Findings from The Analytics Advantage, DTTL’s first annual survey on the state of analytics readiness at leading corporations, reveal wide variations in oversight, with the most common analytics champion being a business unit or division head (23 percent). Five C-suite categories (CEO, CFO, CIO, CMO, Chief Analytics Officer, plus “Other”) combine to account for 56 percent of those responsible for analytics, with about 18 percent of corporations surveyed reporting that the CFO is primarily responsible for analytics, making the CFO the third most common analytics overseer.

In addition, another 20 percent of respondents were unable to identify one single executive with that responsibility. This suggests some businesses may be experiencing an ongoing analytics-ownership power struggle, as executives become more cognizant of the potential benefits and complexities of a robust analytics capability within their enterprises. More than half of the 100 survey respondents were C-suite executives (33 percent) or heads of business units or divisions (22 percent), which may point to the growing importance of analytics at the highest levels of the enterprise.

How analytics is organized is also an evolving issue. Some 42 percent of respondents report some level of centralization within their organization—a finding that may be somewhat surprising given that modern-era analytics and big data are still relatively new on the corporate agenda. The remaining 58 percent either have uncoordinated pockets of analytical activity (20 percent) or geography—or business unit-based analytical capabilities that are in early phases of collaboration as they begin to share tools, data, talent, and leading practices.

Moreover, there is ongoing debate about whether analytics should be a more centralized function or embedded within various corporate work streams and geographies. The opinions are as varied as the companies and industries involved. As one leader described it, moving from a decentralized to a more centralized approach to analytics—which is in the early stages at her organization—is desirable.

“I would prefer to go to a centralized team, because it decreases the likelihood of confusion within the organization,” she explained. “Otherwise, you have two or more departments investing and building their own support models. That can create riffs and gray areas that none of the analytics groups can support or overcome.”

Conversely, some respondents were adamant that decentralization of analytics capabilities is more effective, with one indicating that “by serving individual leaders, we are more embedded in the business and better able to serve their needs.”

The “centralize versus decentralize” debate will likely continue, as organizations grapple with the volume, velocity, and variety of data at their disposal, the resources required to make sense of this data, and the effects data-driven projects have on revenue, expenses.

Advantage, analytics

The following are other key findings from The Analytics Advantage:

  • Analytics as a competitive resource. Analytics is already an important competitive resource for many companies, with less than 20 percent of survey respondents stating that analytics does not yet support their corporate strategies.
  • Growing importance of analytics. Some 96 percent of respondents feel that analytics will become more important to their organizations in the next three years. Two reasons why there is plenty of room to grow: a great deal of data is still not used for decision-making, and many organizations have only rudimentary analytical technology.
  • Better decision-making. Nearly half of respondents (49 percent) assert that the greatest benefit of using analytics is that it is a key factor in better decision-making capabilities. Another 16 percent believe that its greatest benefit is better enabling key strategic initiatives. Nearly two-thirds of respondents say that analytics plays an important role in driving business strategy.
  • Marketing and customers. Surprisingly, only 1 percent of respondents believe that the greatest benefit of using data analytics is identifying and creating new product and service revenue streams. But its marketing influence is rising, as 55 percent of respondents said their marketing and sales groups invest in analytics second only to finance operations.
  • Structure is a challenge. Analytics is managed by a variety of executive roles within companies, and a wide range of functions benefit from analytics. More structure around coordination and alignment is needed to realize the impact and benefits of a company’s data throughout the organization.
  • Key barriers to overcome. Organizations will likely be slow to fully capitalize on the potential of analytics unless they are able to overcome several key barriers, of which data management and access to talent are often the most problematic.

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