Here is a brief excerpt from an article written by Nicolaus Henke, Ari Libarikian, and Bill Wiseman for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.
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The revolution isn’t coming—it’s already under way.
In the science of management, the revolution in big data analytics is starting to transform how companies organize, operate, manage talent, and create value. Changes of this magnitude require leadership from the top, and CEOs who embrace this opportunity will increase their companies’ odds of long-term success. Those who ignore or underestimate the eventual impact of this radical shift—and fail to prepare their organizations for the transition—do so at their peril.
It’s easy to see how analytics could get delegated or deprioritized: CEOs are on the hook for performance, and for all of the potential associated with analytics, many leaders operating in the here and now are reporting underwhelming results. In fact, when we surveyed a group of leaders from companies that are committed to big data–analytics initiatives, three-quarters of them reported that their revenue or cost improvements were less than 1 percent. Some of the disconnect between promise and payoff may be attributed to undercounting—the sum of the parts is not always immediately apparent. Ironically, the results of “big data” analytics are often thousands—or more—of incrementally small improvements realized system-wide. Individually, any one of these gains may appear insignificant, but when considered in the aggregate they can pack a major punch.
The shortfalls, however, are more than just a matter of perception, and the pitfalls are real. Critically, an analytics-enabled transformation is as much about a cultural change as it is about parsing the data and putting in place advanced tools. “This is something I got wrong,” admits Jeff Immelt, the CEO of GE. “I thought it was all about technology. I thought if we hired a couple thousand technology people, if we upgraded our software, things like that, that was it. I was wrong. Product managers have to be different; salespeople have to be different; on-site support has to be different.”
CEOs who are committed to a shift of this order, yet wonder how far the organization has truly advanced in its data-analytics journey to date, should start by stimulating a frank discussion with their top team. That includes a clear-eyed assessment of the fundamentals, including your company’s key value drivers, your organization’s existing analytics capabilities, and, perhaps most important, your purpose for committing to analytics in the first place. (See “Making data analytics work for you—instead of the other way around.”) This article poses questions—but not shortcuts—to help a company’s senior leaders determine where they are and what needs to change for their organization to deliver on the promise of advanced analytics.
Two scenes from the front lines of the revolution
Transforming analytics from a “science-fair project” to the core of a business model starts with leadership from the top. Here are five questions CEOs should be asking their executive teams.
Jeff Immelt reached his conclusions from witnessing — and, in many respects, leading — the revolution. GE’s CEO is keenly aware that so far in the 21st century, the digitization of commerce and media has allowed a handful of US Internet stalwarts to capture almost all the market value created in the consumer sector. To avoid a similar disruption as the industrial world goes online over the coming decade, Immelt is driving a radical shift in the culture and business model of his 124-year-old company. GE is spending $1 billion this year alone to analyze data from sensors on gas turbines, jet engines, oil pipelines, and other machines and aims to triple sales of software products by 2020 to roughly $15 billion. To make sense of those new streams of data, the company is also building a cloud-based platform called Predix, which combines its own information flows with customer data and submits them to analytics software that can lower costs and increase uptime through vastly improved predictive maintenance. Getting this right will require hiring several thousand new software engineers and data scientists, retraining tens of thousands of salespeople and support staff, and fundamentally shifting GE’s business model from product sales coupled with service licenses to outcomes-based subscription pricing. “We want to treat analytics like it’s as core to the company over the next 20 years as material science has been over the past 50 years,” says Immelt.
To understand further the growing power of advanced analytics, consider as well how a consumer-electronics OEM is picking up more speed in an inherently slow-growth market. The company started with a Herculean effort to pull together information on more than 1,000 variables previously collected in silos across millions of devices and sources—product sales and usage data, channel data, online transactions, and service tickets, plus external consumer data from third-party suppliers such as Acxiom. Mining this integrated big data set allowed the company to home in on a dozen or so unrealized opportunities where a shift in investment patterns or processes would really pay off. Armed with a host of new, fine-grained insights on which moves offered the greatest odds to increase sales, reduce churn, and improve product features, the company went on to realize $400 million in incremental revenue increases in year one. As success builds, the leadership has begun to fundamentally rethink how it goes about new-business development and what future capabilities its top managers will require.
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Here is a direct link to the complete article.
Nicolaus Henke is a senior partner in McKinsey’s London office, Ari Libarikian is a senior partner in the New York office, and Bill Wiseman is a senior partner in the Taipei office.