Executives have high hopes for their data and analytics programs. Large majorities of respondents to a recent McKinsey Global Survey on the topic expect their analytics activities to have a positive impact on company revenues, margins, and organizational efficiency in the coming years. To date, though, respondents report mixed success in meeting their analytics objectives. For those lagging behind, a lack of strategy or tools isn’t necessarily to blame. Rather, the results suggest that the biggest hurdles to an effective analytics program are a lack of leadership support and communication, ill-fitting organizational structures, and troubles finding (and retaining) the right people for the job.
Leadership and organization matter
Respondents say their organizations pursue data and analytics activities for a range of reasons, most often to build competitive advantage or improve the customer experience. Whatever the motivation, companies have found mixed success: 86 percent of executives say their organizations have been at best only somewhat effective at meeting the primary objective of their data and analytics programs, including more than one-quarter who say they’ve been ineffective.
However, a select group of executives report greater effectiveness and more developed analytics capabilities relative to their peers. Compared with their lower-performing peers, these high performers say their analytics activities have had a greater impact on company revenue in the past three years. And some of the biggest qualitative differences between high- and low-performing companies, according to respondents, relate to the leadership and organization of analytics activities. High-performer executives most often rank senior-management involvement as the factor that has contributed the most to their analytics success; the low-performer executives say their biggest challenge is designing the right organizational structure to support analytics (Exhibit 1).
On the whole, responses suggest that company leaders are less involved in analytics efforts than they are in digital activities. In McKinsey’s latest survey on digitization, 38 percent of respondents said their CEOs were leading the digital agenda for their companies; in this survey, just one-quarter say their CEOs lead the data and analytics agenda. But even when analytics are top of mind for company leaders, many of them don’t seem to be communicating a clear vision throughout their organizations. Thirty-eight percent of CEOs say they lead their companies’ analytics agendas, but only 9 percent of all other C-suite executives agree (Exhibit 2). These respondents are much more likely to cite chief information officers or business-unit heads as leaders of the analytics agenda.
Sponsorship is another area where company leaders can do more, and where the high and low performers differ notably. Respondents at high performers in analytics are nearly three times likelier than their low-performer peers to say their CEOs directly sponsor their analytics initiatives (Exhibit 3).
Just as companies pursue varied objectives with their analytics activities, they also differ in how they organize around this work. There is no consensus on a single structure—centralized, decentralized, or a hybrid model—that most companies use. But the executives who report using a hybrid structure—a central analytics organization that coordinates with employees who are embedded in individual business units—say analytics has a greater impact on both cost and revenue than other respondents do. Relative to others, these executives also report a broader range of analytics capabilities (including more sophisticated tools and advanced modeling techniques) and a greater number of business functions pursuing analytics activities.
For many companies—especially the low performers—the results indicate that attracting and retaining talent are more difficult for data and analytics than for other parts of the business. In particular, executives say it’s challenging both to find and to retain business users with analytical skills, even more than data scientists and engineers (Exhibit 4). Within the C-suite, the CEOs’ direct reports are more likely than CEOs themselves to cite difficulty attracting executive leaders for analytics—roles that are critical, given the correlation between leadership involvement and overall analytics success.
The most significant talent challenges that companies face, according to respondents, are a lack of structured career paths (especially at larger companies) and the inability to compete effectively on salary and benefits. These two challenges are even more acute for companies where analytics work is decentralized (that is, when analytics employees are embedded in individual business units and act independently), reflecting the difficulty of creating a distinct analytics culture without a central team and leader. And while executives at low-performing companies report the same challenges as others, they overwhelmingly cite a lack of leadership support as the primary challenge to both attracting and retaining talent—once again underscoring the importance of leaders’ involvement in advancing the goals of data and analytics efforts.
Compounding the talent challenge is that traditional recruiting methods seem to be falling short. Only 16 percent of respondents say their organizations have successfully found data and analytics talent through recruitment agencies and search firms. Other approaches, though, have worked better. Respondents most often cite retraining current employees as an effective method, which eliminates the need to find new hires and attract them away from other opportunities. At high-performing companies, respondents have also found success by developing a unique recruiting team for analytics employees.
How companies are getting it right
Beyond better talent practices and more active CEO involvement, executives at high-performing companies report other practices that differentiate their analytics activities. Most executives—including three-quarters of those at low-performing companies—say their organizations have established some analytics capabilities. However, the high performers report significantly more advanced capabilities across the board (Exhibit 5). They are, for example, nearly five times likelier than their low-performing peers to say they have tools and expertise to work with unstructured and real-time data. And they are nearly twice as likely to say they make data accessible across their organizations.
High performers are also more diligent than others when it comes to measuring results, and more likely than their peers to track most of the nine analytics-related metrics we asked about. Fifty-four percent of high performers, for example, say their companies track the impact of their analytics activities on top-line revenues. By contrast, only 19 percent of respondents at low performers say they measure the impact on revenue.
Finally, high performers extend their data and analytics activities more broadly across the organization. Fifty-nine percent of these executives say their R&D functions use analytics, compared with just one-fifth of low-performing respondents. What’s more, the high performers are more likely than low performers to say their primary purpose with analytics is building competitive advantage—and less likely to say they are simply trying to cut costs.
- Communicate from the top. The results indicate that even if CEOs are aware of their analytics activities—and their importance to the business—many are failing to communicate their vision and strategy across the organization. This lack of communication can confuse the groups responsible for implementing analytics and can hinder collaboration among functional teams. Senior-leader involvement also goes a long way toward creating a culture that values this work, a critical factor in an organization’s ability to recruit data and analytics talent and to capture value from their efforts. Company leaders must continually articulate the importance of analytics by hosting town-hall meetings, monitoring results on company dashboards, and incentivizing senior managers to focus on these initiatives.
Organize for success. While there is still debate over which operating model works best for analytics, the survey results suggest that companies using a hybrid approach often see greater impact from analytics than companies with either a strictly centralized or decentralized model. However they decide to organize, though, companies must ensure that they have the right balance of technical and domain expertise, that resources are being used efficiently, and that all analytics resources align closely with the goals and targets of the business units they support.
- Find new ways to attract talent. Most respondents say it’s difficult to attract and retain the best data and analytics talent through traditional recruiting means. To attract good people, companies will need to develop a distinct culture, career paths, and recruiting strategy for data and analytics talent; ensure that analytics employees have a close connection with company leaders; and articulate the unique contributions that data and analytics talent can make. They must also identify and tap into new or alternative sources of talent—retraining existing employees, for example, or forming innovative external partnerships.