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Choosing the right chief data officer3/17/2017 Ryan Bulkoski and Joshua M. Clarke
Few roles are evolving more rapidly than that of today’s data and analytics leader. Across most industries, data and analytics are becoming central to operations, strategy, and competition. Increasingly, the role is located in the C-suite—45% of the 3,000 global companies studied by Forrester in 2015 had appointed chief data officers (CDOs), and 16% were planning to do so in the ensuing 12 months.1 (The study also found a high correlation between high-performing companies and those with CDOs.)
Research recently conducted by Heidrick & Struggles to examine the leadership profiles of senior data and analytics leaders, based primarily in North America and the United Kingdom, provides additional evidence of change. Of the 82 data and analytics executives we surveyed, 9 in 10 report that their role has changed more than once in scope or complexity over the past five years.
Broadly speaking, data and analytics evolve in organizations through three stages of maturity that we call Enable, Support, and Transform (Figure 1). Each stage calls for a distinct set of technical competencies, organizational capabilities, and leaders with relevant experience. As one CDO recently told us, “It’s the difference between creating analytics, delivering analytics, and leading analytics.”
Evolution through these three stages is neither inevitable nor always desirable. In some cases, the Enable or Support stage may be appropriate and sufficient for an organization for the foreseeable future; for other companies, advancing to the Transform stage could be the difference between winning in their industry or falling far behind.
What is less well understood is what leadership style—distinct from competencies and experience—is likely to be more appropriate at each of these stages. In assessing hundreds of data and analytics leaders, we have often observed a distinct career pattern emerge as their companies developed these capabilities. In the initial stages of investment, companies seek the brightest technical minds. These executives are subsequently recognized, rewarded, and promoted in large part based on their expertise. While this approach has served many organizations well in the Enable stage, these executives may be less well positioned for success as their organizations evolve to the Support and Transform stages. As a result, a gap opens up between the incredibly competent technical managers that companies have and the type of progressive, commercially oriented leaders that companies increasingly need.
While the mastery of specialized skills is an important credential, the disproportionate focus on technical expertise has crowded out some critical questions about leadership: What are the advantages and drawbacks of specific leadership styles for data and analytics executives? What leadership styles are most common among those executives today and most likely to be in demand tomorrow? And how can understanding differing leadership styles help improve the recruitment, retention, and development of leaders for the company’s current and anticipated stages of data and analytics maturity?
To begin answering these questions, we applied a proprietary assessment tool to determine how eight distinct styles of leadership are distributed among current senior data and analytics leaders in a variety of industries, including financial services, consumer goods, technology, and healthcare/life sciences. (Those eight styles emerged from prior research conducted with more than 1,000 executives at the director level or above.2)
The results offer insights into the “fit” of different types of leaders with the evolutionary stages of the analytics role. Among the 82 data and analytics leaders we assessed, three-fourths fall primarily into one of three leadership styles. Some 34% gravitate toward a style that is arguably best suited for the analytics role in the Enable and Support stages of an organization’s data maturity—a style we identify as “Forecaster.” Another 22% of respondents fall into a style we find increasingly in demand as data and analytics continue to grow in strategic importance—a style we call “Pilot.” Another 20% fall into a category we call “Collaborator.” The remaining 24% of respondents were unevenly distributed among the other five leadership styles (Figure 2).
It’s important to note that while every style has strengths and weaknesses, no particular leadership style is “right” or “wrong,” and all styles can be equally effective. Indeed, individuals tend to have some degree of access to all the styles, and self-aware or well-coached executives can learn to flex to additional styles when appropriate. Nonetheless, our experience and research suggest that leaders tend to gravitate to a much smaller set of default styles they find comfortable or familiar.
The challenge arises when leaders continue to resort to a style less suitable under changed conditions. For example, data and analytics leaders who have made their way to the top during the most detail-oriented stages of maturity (Enable and Support) may have leadership strengths less well suited to the Transform stage, when boldness and innovation are called for. Conversely, another mismatch may arise when a leader whose style is better suited for the Transform stage is brought in to lead a data function that has just begun to enable a data-driven organization or when supporting the business is paramount. Even when no apparent mismatch exists, CDOs and their CEOs alike should be aware that no style is without weaknesses that need to be addressed and strengths that need to be put fully to work.
The Forecaster: Delivering data and ideas
It’s not surprising that Forecasters represent the most prevalent leadership style, at 34% of our sample. Their deep subject-matter expertise is especially well suited to the Enable and Support stages of data maturity, and, as we have noted, they have often been promoted based on that expertise, even when it has become less relevant to their roles.
Forecasters relish the chance to expand their knowledge and enhance their subject-matter expertise. They like to have time to think deeply, gather data, reflect on what they’ve observed, and only then make a decision or propose a course of action. In principle, this information-driven style should serve them well as they go about meeting the daily demands of the function. And they tend to thrive in situations where people like to be led by leaders with substantial intellectual capital.
Consider how a major retailer approached its transition from the Enable stage to the Support stage. The organization’s existing IT capabilities met rudimentary data needs, but the company wanted to do more with its in-store and online consumer data. The eventual choice was to hire a Forecaster type who had experience creating new, scalable data-architecture capabilities that could deliver more sophisticated insights. His strong technical capabilities resonated with the company’s IT organization, which enthusiastically got behind him. At the same time, the new leader has been able to cultivate relationships with key leaders on the commercial side of the organization to help ensure that the technology delivers actionable analytics solutions to the business.
Of course, Forecasters face challenges too. For example, they are often adept at harnessing their formidable skills to identify trends and formulate insights about the future and forecast their impact on the business. Yet Forecasters often tend to be overcautious in moving forward—there is always more data to be gathered and additional analysis to be undertaken—so they may fail to capitalize in a timely manner on their strategic foresight. That caution can be crippling in the Transform stage, when speed to market and first-mover advantage are critical. Forecasters may also expect the sheer force of their ideas to carry the day, underestimating the need for influencing skills to win buy-in from the C-suite and boardroom and to inspire action among their people.
This isn’t to say that executives with a Forecaster style cannot thrive in these environments, but their success will be influenced by their ability to flex out of their comfort zones. Several steps can help Forecasters overcome these weaknesses and unleash the full value of their intellectual depth and strategic foresight. When seeking buy-in from key stakeholders, for example, they can consciously focus on emotional, as well as analytic, persuasion—though it might be a struggle for them at first. When they feel inadequately informed but a timely decision is critical, they can be challenged, or they can challenge themselves, to act more decisively and stretch their tolerance for risk—for example, by asking whether more information would really improve the quality of the decision. And when they become overly wedded to their own thinking, preventing them from accepting a new direction, they can put their intellectual firepower to work understanding why the alternative was chosen. And even if they still don’t understand the choice, they can be challenged to bring that intellect to bear on the new direction.
The Pilot: Delivering commercial value
The second-most prevalent leadership style in our sample, at 22%, is the Pilot, reflecting a reality we increasingly see in the talent market as data and analytics grow more central to commercial success.
Whereas Forecasters are more comfortable leading with technical content, Pilots are proficient at transforming large-scale concepts into action and results. Pilots particularly enjoy environments that are ambiguous, complex, and characterized by significant change—such as fast-moving, intensely competitive markets or start-ups, where the ability to commercialize ideas and grow rapidly is essential for survival.
One of the world’s leading real estate and development companies, for example, realized that its operations generated a continuous stream of potentially valuable data about real estate, retailers, and retail customers. But with only a rudimentary data and analytics capability, the CEO and CFO knew they needed a leader who could simultaneously transform the function and develop a business model for monetizing the company’s data. Working from a profile that included leadership attributes of the Pilot type, they recruited a new CDO. In a little over a year in the company’s newly created CDO role, this executive has helped develop and implement a valuable business model built around the company’s retail partners and their customers, and the company is well on its way to generating substantial new insights.
Pilots have clear opinions—they don’t suffer from “analysis paralysis”—and they relish new challenges. But they are also open to input from other people, particularly those who have established credibility. All of this tends to make Pilots comfortable—and effective—in creating and working in teams. For example, the real estate company’s new CDO, charged with building out the company’s data and analytics function, applied the Leadership Signature framework in quickly hiring three direct reports with capabilities to complement his. They included a head of data management, a data scientist to spearhead advanced analytics, and a director of B2B and B2C insights. These roles covered, respectively, the Enable, Support, and Transform stages of data maturity the CDO had to orchestrate in order to ensure the function was built on a strong foundation and designed to commercialize valuable data.
At times, however, Pilots’ combination of strong drive, dynamic orientation, and a “here and now” mentality can spur them to push for changes faster and harder than their colleagues are ready for. As a result, they may be perceived as unreflective, hard to satisfy, and constantly disappointed in the performance of others. Or they may find that their dynamism and bias for action are ill-suited to an organization still in the early stages of data maturity.
Consider, for example, the experience of a leading bank that brought in a CDO from a highly sophisticated Internet company. A classic Pilot type, he was put in charge of an organization firmly entrenched in the Support stage of data maturity, heavily focused on financial and regulatory reporting and performance tracking. Instead of putting him to work bringing the organization to the next stage, the bank wanted him, in effect, to maintain and refine the current operation. He had little interest in making incremental improvements or in carrying out the unglamorous work of painstakingly bringing along the 3,000 employees in the function. Frustrated and bored, he left after just three years in the job.
Even in organizations in the Transform stage of data maturity, Pilots may need to be coached to temper some of their natural tendencies. They may push forward on their big ideas before they have fully addressed—or even identified—the ramifications of those ideas. Pilots should also be aware that their propensity to seek out new challenges can sometimes come at the expense of not learning from the past (unlike the Forecaster). They can guard against this danger by conducting structured “after-action reviews” at the completion of projects. Pilots’ strongly held viewpoints can also leave little space for others to share their thoughts freely. Pilots should therefore be mindful that they may be unintentionally encouraging people to defer to their perspectives. Similarly, their natural inclination to boldly lead projects and initiatives they have created offers little opportunity for others to develop leadership capabilities. To compensate, Pilots can carve out meaningful leadership roles for others to help spur their growth and development—and consciously work to make space for others’ ideas to flourish.
Collaborators, “hyphenates,” and “arranged marriages”
Though Forecasters and Pilots are the most prevalent types of data and analytics leaders, a number of other leadership possibilities exist for moving the organization to another stage of data maturity. Consider Collaborators, almost as numerous (20%) in our sample as Pilots. Humble and perceptive about others’ needs, Collaborators take a team-first approach to leadership. In building teams, they focus on supporting and developing colleagues by placing them in positions where they can excel, and Collaborators share credit for team success with all members. As a result, they’re good at attracting talent and encouraging collaboration.
In an organization in the Support stage of data maturity with aspirations of moving to the Transform stage, a Collaborator could assemble a high-performing team consisting of Forecaster and Pilot types who together harness the power of information-driven insights and market-focused actions.
Nonetheless, the Collaborator style, like all leadership styles, can suffer from the “defects of its virtues.” For example, Collaborators’ team-first orientation may be so strong that it prevents them from developing their own convincing identities as leaders. The focus on people and relationships may at times come at the expense of strategic vision and planning, which are essential for moving an organization to the Transform stage and making sure it thrives there. Collaborators may also struggle with bold direction and the engaging personal presence required to influence key stakeholders, such as board members leery of big, rapid shifts in strategy. But in settings where the primary leader is a Pilot with an aggressive, impersonal, or demanding style, a Collaborator might make a good “number two,” providing a more collaborative, relational, and supportive approach—the glue that holds the organization together through the turbulence a Pilot tends to naturally seek.
“Hyphenates” are leaders whose default style is complemented by a dominant secondary style in the context of data maturity. Indeed, our 8 Leadership Signatures yield 56 possible primary–secondary combinations. In our global research, now encompassing more than 10,000 executives, the top 4 most frequently found combinations are Forecaster–Collaborator, Pilot–Forecaster, Forecaster–Pilot, and Collaborator–Forecaster. (Interestingly, the combinations Collaborator–Pilot and Pilot–Collaborator rank only 9th and 12th, respectively, in frequency.)
Because 94% of our CDO survey respondents report that their role has changed more than once in scope or complexity over the past five years, we might infer that the technical demands and increasingly commercial demands these leaders face are producing a fully hybrid leadership style—one that incorporates both the technical mastery of a Forecaster and the high-level, strategic mind-set of a Pilot, instances of which we have seen in our work placing senior data and analytics leaders. The fact that these two vastly different styles are the most dominant and one of the most often found in combination reinforces what we see in the market: the rapid transformation of the CDO role as the demand for a more comprehensive skill set increases. The prevalence of this hybrid style is good news for organizations that want to make an orderly but decisive strategic transition from the Support to the Transform stage, and it indicates that the choice of an appropriate data and analytics leader doesn’t have to be an either–or proposition.
Another possibility for getting the best of two worlds lies in an “arranged marriage”: the deliberate pairing of two executives with different styles so that one can help compensate for the other’s blind spots. Consider, for instance, the case of a major commercial bank. With traditional businesses struggling, margins dwindling, and new digital-only entrants offering stiff competition, the industry is currently involved in an arms race to put data and analytics to work in new business models and compelling customer experiences (for more, see “Seizing banking’s uncertain future”). To make the leap from the Support to the Transform stage, the bank installed a full-bore Pilot from outside the industry as CDO. But to ensure that bread-and-butter execution wasn’t neglected and the function’s thousands of employees weren’t left behind, the CDO was paired with the bank’s COO, whose formidable operational skills could be counted on to keep the Pilot’s feet on the ground even as his head was rightly in the clouds. Although the CDO reports to the COO, they are in essence full partners in the transformation of the data and analytics function.
As we’ve noted, there are no definitively good or definitively bad leadership styles. Each one has its distinctive strengths and weaknesses, and each can be highly effective. Further, leadership styles constitute only one of several dimensions along which leaders should be assessed. Experience, education, past performance, potential, career trajectory—all these dimensions are important. But a full understanding of an individual’s Leadership Signature offers a degree of specificity—whether in the case of a function such as data and analytics; a business situation such as a start-up, a turnaround, a merger, growth, or new competitive pressures; or individual skills such as influencing, strategizing, or executing—that more general leadership frameworks lack. It makes leaders’ particular attributes, in effect, more legible. They leave their distinctive mark in their Leadership Signature, and, when they are put in the right situation with the right support, they leave that mark on the organization.
We surveyed 82 big data and analytics leaders: the vast majority were 40 to 59 years of age, male, and based in the United States and the United Kingdom. More than three-quarters work for organizations with gross revenues greater than $1 billion. Over half describe their main functional role as technical, engineering, security, or based in data management or analytics. Roughly 18% see their main function as general management, while only 2% describe their role as operational.
About the authors
Ryan Bulkoski (firstname.lastname@example.org) is a partner in Heidrick & Struggles’ Big Data & Analytics Practice; he is based in the San Francisco office.
Joshua Clarke (email@example.com) is a global co-managing partner in the Big Data & Analytics Practice; he is based in the Boston office.