ANALYTICS SPECIAL .....Rebooting analytics leadership: Time to move beyond the math PART I

Rebooting analytics leadership: Time to move beyond the math

PART  I

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.

Exhibit 1 PLEASE SEE THE ORIGINAL ARTICLE

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:

1. Born digital, with data and analytics as their lifeblood, leading them to position their CAOs as core members of the C-suite.

2. Led by an analytically driven CEO who aggressively made analytics the top priority and rallied all executives and business units behind the effort.

3. 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).

Exhibit 2 PLEASE SEE THE ORIGINAL ARTICLE

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.

A demanding environment for today’s CAOs

This brings us to today, when organizations are looking to CAOs to capture a share of the huge AI opportunity, which necessitates a degree of scale not previously required—and in a far more demanding environment.

Digital natives are ratcheting up the competition for wallet share to new levels as they push into an increasing number of sectors, from grocery to financial services.

The use of AI and analytics has become table stakes in delivering consumers the personalized attention and experiences they demand as the universe of analytics tools expands and becomes more widely available thanks to the cloud. In fact, nearly a third of the value these technologies are expected to drive in the near term is projected to come from marketing and sales use cases.

At the same time, risks have grown exponentially, as organizations balance concerns around data privacy and information security. “Nearly every analytics project that we’re working on right now has run into a delay as we have encountered new data-security requirements,” said one analytics leader at a large insurer.

Against this backdrop, CAOs also face plenty of organizational challenges as they strive to stand up a scalable analytics function. They must navigate long-standing processes, stitch together data silos, and challenge legacy power structures that keep analytics in the back seat to business.

CAOs often find themselves doing this heavy lifting with a limited sphere of influence. They typically do not have the profit-and-loss or revenue accountability that would grant them due power in the organization. Moreover, like chief marketing officers a decade ago, CAOs need—but typically lack—a true seat at the C-suite table, placing them at a disadvantage when trying to obtain adequate funding or resources to power the analytics agenda.

Enter the Catalyst

Arguably, none of the previous CAO personas could succeed in today’s landscape. We’ve entered an era that requires a new CAO persona—the Catalyst—who embraces a style of leadership geared toward addressing the current demands, roadblocks, and scrutiny most companies face today when it comes to deploying AI and advanced analytics at scale. Catalysts approach their role very differently than did past CAO personas, in ways that those with more scientific and technical career backgrounds might not have ever done before. They both facilitate and lead the charge in five key areas. (To see if you approach the CAO role like a Catalyst, see Exhibit 3.)

1. Convening a coalition of equals

Catalysts build a tri-headed “coalition of equals” made up of the CAO, the business, and IT. Creating this coalition might be the most critical driver for bringing analytics initiatives to scale and success—and is often the most difficult to achieve due to long-standing hierarchies and the CAO’s position as the “new C-suiter on the block.”

As a group, this coalition of equals understands the company’s need to compete with integrated capabilities in this hypercompetitive era and that silos—of data, business functions, or culture—will prevent them from doing so. It recognizes that integrating digital and analytics elements into nearly every existing product and service creates opportunities to offer entirely new products and services. The group knows success in these efforts is predicated on joint efforts, close collaboration, and shared ownership. The coalition shares equally in decision making, and coalition members rally around a common goal: gaining a disproportionate share of value from advanced analytics and AI compared with their competitors.

The former analytics leader at a leading financial-services company told us how developing such a coalition of equals enabled his company to build enterprise-wide capabilities, scaling from about 30 to 200 profitable, revenue-generating use cases in two years.

An analytics leader at an online retailer also found the coalition of equals instrumental in maintaining momentum when the innovation process is in its infancy. “Moving to AI-driven automation is a challenging process, and often it’s the second and third iterations that work,” he said. “But you have to have faith and momentum to get to those second and third iterations. The partnerships I had with other leaders allowed me to drive organization-wide changes and adoption.”

Catalysts actively facilitate and take a leadership position in building the coalition, while bringing along other stakeholders at multiple levels. They do so in the following ways:

·         Recognizing that while a technical background is critical, there’s much more to driving success. 

“While it’s critical to get the math right, just getting the math right doesn’t drive the change,” said one insurance analytics leader. “You need the technical background to establish credibility, but the job is less about the technical aspect and more about employee development, resource allocation, vision setting, and leadership to manage the pace of change.”

·         Putting business value front and center. 

The same insurance analytics leader stated: “It’s gaining the trust of business-unit leaders by helping them to understand how the models influence business impact and, ultimately, drive profit and loss. That’s what they’re interested in.”

·         Spending an outsized proportion of time on communicating with the CEO and other C-suite leaders to maintain their support. 

For example, a financial-services analytics leader set up monthly operational committee meetings with his COO, CIO, CFO, and business-unit heads to set a shared vision and review priorities, progress, new value-creation opportunities, and investment needs. Throughout the month, he met separately with committee members, along with other company leaders, to maintain alignment and buy-in. He estimated that more than one-third of his time was spent on executive alignment and strategy.

Another analytics leader emphasized the need to communicate—and to do so in a way that’s in line with the C-suite’s level of knowledge and past experience. For example, in presenting new pricing methods to business leaders, he highlighted that the notions of understanding underlying costs wouldn’t change; the team would simply bring in more data about consumer behavior to drive a more precise understanding of pricing.

·         Spending considerable time codeveloping strategies with business leaders to align analytics opportunities and innovation with the business unit’s vision and priorities.

 Joint ownership is essential, one analytics leader emphasized. He worked with business leaders to create a shared scorecard so that both analytics and the business were measured on the same outcomes.

Another leader found that starting from a perspective of joint learning and sharing helped him overcome skepticism and “earn the right” to advise on organizational implications and influence business strategy.

·         Viewing IT as a strategic partner rather than simply as an execution arm. 

This can include bringing IT into meetings to develop and formalize new business strategies, soliciting input from engineering teams, and ensuring IT receives recognition for its role in transformation initiatives. Ultimately, this type of partnership approach not only helps build a motivated IT organization but also attracts the best IT talent.

CONTINUES IN PART II

By Brian McCarthy, Chris McShea, and Marcus Roth

https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/rebooting-analytics-leadership-time-to-move-beyond-the-math?cid=other-eml-alt-mip-mck-oth-1811&hlkid=f5c82c5620034dbeb6216c7ac7e7d1fd&hctky=1627601&hdpid=b3b5de99-d83f-4794-8650-4068aa0b1084