To help their organizations capitalize on artificial intelligence and analytics, CAOs must do more than demonstrate their technical chops. They need to lead like a Catalyst.
The role of the chief analytics officer (CAO) is being thrust into the spotlight as artificial intelligence (AI) technology continues to improve—and prove its value. AI and other advanced analytics will unlock $9.5 trillion to $15.4 trillion annually, with recent AI advances such as deep learning alone making up nearly 40 percent of the total.
Given the enormity of the stakes, it’s no surprise that CEOs are asking their CAOs (or those assuming CAO duties under a different title) to deploy and scale AI and advanced analytics—stat. Yet while the opportunity is great, so too is the challenge. In McKinsey research earlier this year, only 8 percent of senior executives reported that their organization engages in practices identified as key enablers for AI and analytics at scale (Exhibit 1).
The reasons for low success rates to date are numerous, as CAOs face a barrage of headwinds—from data silos and rising data risks to leaders and front lines resistant to a new way of data-driven decision making—while experiencing some of the lowest tenures among their peers (about two to three years).1
One analytics leader told us, for example, that while his organization hired him to create a data and analytics function that could scale to drive growth, progress was constantly derailed, as his team was forced to spend outsized time generating basic reports for narrowly focused business leaders.
How can CAOs cut through the whirlwind of obstacles to help their organization capture a larger piece of the advanced analytics prize than their competitors?
Based on our extensive experience working with analytics leaders and a series of in-depth interviews with some who have been successful, we believe one key to success will be for CAOs to assume the role of Catalyst—a new persona that redefines leadership for deploying analytics and AI at scale.
Successful CAOs of times past
Historically, we’ve seen that successful CAOs have often been buoyed by an analytically minded CEO or a mission-critical situation. Their organizations fit into one of three types:
- Born digital, with data and analytics as their lifeblood, leading them to position their CAOs as core members of the C-suite.
- Led by an analytically driven CEO who aggressively made analytics the top priority and rallied all executives and business units behind the effort.
- In crisis, facing a significant threat to their business model and, sometimes, to their very existence. These organizations required analytics to compete and put CAOs squarely in charge of their business transformation.
But most companies faced a different reality: an organizational desire to move to an analytics-driven approach but without a forceful push from a visionary CEO or existential crisis.
Among these companies, analytics leaders made progress in line with the times. The 1990s were arguably ground zero for data and analytics, as the Internet had only just opened to the public and begun generating data. The title “CAO” didn’t even exist yet.
Simply establishing a data-science capability somewhere in the organization amounted to success, putting math-minded mavericks and statistical geniuses in the best position to thrive (Exhibit 2).
The early 2000s saw a massive increase in data generation, thanks in large part to broadband and the rise of Internet-based businesses and social-media platforms. Better data capture and analytics technologies emerged in response, raising the bar for success.
CEOs began placing higher expectations on analytics leaders, and the true role of CAO was born. In this environment, data evangelists who could seed data and analytics usage a bit more broadly—even if unevenly—throughout their organizations were heralded for their achievements.
However, the next decade brought a new level of urgency. Digital natives became increasingly successful, upping the intensity of competition. The number of data-generating smartphones surpassed the number of humans on the planet, making data-hungry machine learning techniques even more commercially viable.
Organizations needed a more aggressive CAO to embed analytics more consistently across the organization. While good at achieving this goal, the aggressive CAO’s strong push, against what was often significant organizational resistance, left many organizations soured, requiring a new CAO persona to facilitate further change.
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