Here is a brief excerpt from an article written by Jacques Bughin 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 benefits match those of earlier technology cycles, but companies must scale up their data-analytics skills to reap the gains.

Over the past several years, many companies have avidly pursued the promised benefits of big data and advanced analytics. In a recent McKinsey survey of executives in this field, nearly all of them said that their organizations had made significant investments, from data warehouses to analytics programs. But practitioners have raised questions about the magnitude and timing of the returns on such investments. In 2014, for example, we conducted a poll of senior executives and found that they had seen only modest revenue and cost improvements from them in the previous year.

Our latest research investigated the returns on big data investments for a random sample of 714 companies around the world, encompassing a mix of industries and company sizes typical of most advanced economies. Our findings paint a more nuanced picture of data analytics. When we evaluated its profitability and value-added productivity benefits, we found that they appear to be substantial—similar, in fact, to those experienced during earlier periods of intense IT investment. Our results indicated that to produce these significant returns, companies need to invest substantially in data-analytics talent and in big data IT capabilities.

Yet we also found that while data-analytics investments significantly increased value-added or operating profits, the simple revenue impact for consumer companies was considerably lower. This finding, mirrored among B2B companies on the cost side, appears to confirm the intuition of executives struggling to uncover simple performance correlations. The time frame of the analysis also seems to be important, since broader performance improvements from large-scale investments in data-analytics talent often don’t appear right away.

Analyzing data analytics

The research avoided overweighting technology companies, since many denizens of the C-suite say that “we know that digital natives capture big returns, but does their experience apply to those of us who live in a hard-wired universe of factories and distribution channels?” Operating profit was used to measure returns, since it captures the impact of big data both through value-added productivity and pricing power (often resulting from better customer targeting). The data also allowed us to understand other aspects of the returns on these investments—for example, the advantages of being the first data-analytics mover in a given market.

We took care to measure the returns from technologies specifically linked to big data and therefore considered only analytics investments tied to data architecture (such as servers and data-management tools) that can handle really big data. Looking beyond capital spending, we assessed complementary investments in big data talent across eight key roles, such as data scientists, analysts, and architects. Finally, we examined whether improvements were radiating throughout organizations or captured only in narrower functions or individual businesses.

Gauging performance

Our research looked at the results of big data spending across three major business domains—operations, customer-facing functions, and strategic and business intelligence. These were our key findings:

Big data’s returns resemble those of earlier IT-investment cycles.

History tells us that it takes time for new technologies to gather force and diffuse throughout an economy, ultimately producing tangible benefits for companies. Big data analytics—the most recent major technology wave—appears to be following that pattern. The average initial increase in profits from big data investments was 6 percent for the companies we studied. That increased to 9 percent for investments spanning five years, since the companies that made them presumably benefited from the greater diffusion of data analytics over that period. Looked at from another vantage point, big data investments amounted to 0.6 percent of corporate revenues and returned a multiple of 1.4 times that level of investment, increasing to 2.0 times over five years. That’s not only in the range of the 1.1 to 1.9 multiples observed in the computer-investment cycle of the ’80s but also exceeds the multiples others have identified for R&D and marketing expenditures.

Investments are profitable across key business domains.

Companies, we found, benefit broadly from big data investments. With minor variations, spending on analytics to gain competitive intelligence on future market conditions, to target customers more successfully, and to optimize operations and supply chains generated operating-profit increases in the 6 percent range. Although companies struggle to roll out big data initiatives across the whole organization, these results suggest that efforts to democratize usage—getting analytics tools in the hands of as many different kinds of frontline employees as possible—will yield broad performance improvements.

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Jacques Bughin is a director in McKinsey’s Brussels office.