Can Your C-Suite Handle Big Data?
October 11, 2013 Editor 0
Over the past 30 years, most companies have added new C-level roles in response to changing business environments. The chief financial officer (CFO) role rose to prominence in the mid -1980’s as pressures for value management and more transparent investor relations gained traction. Adding a chief marketing officer (CMO) became crucial as new channels and media raised the complexity of brand building, while Chief strategy officers (CSOs) joined top teams to help grapple with complex and fast-changing global markets.
Today, as the power of data and analytics profoundly alters the business landscape, companies once again may need more top-management muscle. Capturing data-related opportunities to improve revenues, boost productivity, and create entirely new businesses puts new demands on companies — requiring not only new talent and investments in information infrastructure, but also significant changes in mind-sets and frontline training. It’s becoming apparent that it will take extra executive horsepower to navigate new organizational hazards, make tough trade-offs, and muster authority when decision rights conflict in the new environment.
Because the new data analytics horizons typically span a range of functions, including marketing, risk, and operations, the C-suite evolution may take a variety of paths. In some cases, the way forward will be to enhance the mandate of (and provide new forms of support for) the chief information, marketing, strategy, or risk officer. Other companies may need to add new roles, such as a chief data officer, chief technical officer, or chief analytics officer, to head up centers of analytics excellence.
Six top-team tasks
The transformative nature of these changes involves much more than just serving up data to an external provider to mine for hidden trends. Rather, it requires concerted action that falls into six categories. Leaders should take their full measure before assigning responsibilities or creating roles.
Establishing new mind-sets. Senior teams embarking on this journey need both to acquire a knowledge of data analytics so they can understand what’s rapidly becoming feasible, and then push durable behavioral changes through the organization with the question: “Where could data analytics deliver quantum leaps in performance?” This exercise should take place within each significant business unit or function and be led by a senior executive with the influence and authority to inspire action.
Defining a data-analytics strategy. Like any new business opportunity, data analytics will underdeliver on its potential without a clear strategy and well-articulated initiatives and benchmarks for success. Many companies falter in this area, either because no one on the top team is explicitly charged with drafting a plan or because there isn’t enough discussion or time devoted to getting alignment on priorities.
Determining what to build, purchase, borrow, or rent. The authority and experience of a senior leader are needed to guide the strategic tradeoffs involved when assembling data and building the advanced-analytics models for improved performance. The resource demands often are considerable, and with multitudes of external vendors now able to provide core data, models, and tools, top-management experience is needed to work through “build-versus-buy” tradeoffs.
Securing analytics expertise. The new environment also requires management skills to engage growing numbers of deep statistical experts who create the predictive or optimization models that will underwrite growth. The hunt for such talent is taking place in the world’s hottest market for advanced skills. Retaining these employees and then getting them to connect with business leaders to make a real difference, is a true top-management task.
Mobilizing resources. Companies often are surprised by the arduous management effort involved in mobilizing human and capital resources across many functions and businesses to create new decision-support tools and help frontline managers exploit analytics models. Success requires getting a diverse group of managers to coalesce around change—breaking down barriers across a wide phalanx of IT, business-lines, analytics, and training experts. The possibility of failure is high when companies don’t commit senior leadership.
Building frontline capabilities. The sophisticated analytics that data scientists devise must be embedded in tools that engage managers and frontline employees on a daily basis. The scale and scope of this adoption effort—which involves formal training, coaching, and metrics—shouldn’t be downplayed. In our experience, many companies spend 90 percent of their investment on building models and only 10 percent on frontline usage, when, in fact, closer to half of the analytics investment should go to the front lines. Here, again, we have seen plenty of cases where no one on the top team assumed responsibility for sustained ground-level change and efforts fizzled.
Putting leadership capacity where it’s needed
In sizing these challenges, most companies will find they need more executive capacity. That leaves important decisions about where the new roles will be located and how to draw new lines of authority. Our experience shows that companies can make a strong case for leading their data-analytics strategies centrally when there’s a strong company- wide set of data assets to exploit, or a potent functional group such as marketing or finance with strong talent that spearheads value creation. Sometimes a formal centralized data-analytics center of excellence may be needed to launch or accelerate a data analytics initiative. Importantly however, frontline activities (mobilizing resources, building capabilities) will need to take place at the business-unit or functional level since priorities for using data analytics to increase revenues and productivity will differ by business. And just as critically, companies will best catalyze frontline change when they connect it with core operations and management priorities and reinforce it with clear metrics and targets.
A starting point for thinking through issues like these is for top teams, and probably board members as well, to develop a better understanding of the scale of what’s needed to ensure data-analytics success. Then they must notch these responsibilities against their existing management capacity in a way that’s sensitive to the organization’s core sources of value, and that meshes with existing structures.
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