Fueling growth through data monetization

Fueling growth through data monetization

A new survey finds that many companies are launching data-focused businesses. But few have achieved significant financial impact, which requires the right combination of strategy, culture, and organization.

Results from the newest McKinsey Global Survey on data and analytics indicate that an increasing share of companies is using data and analytics to generate growth.1 Data monetization, as a means of such growth, is still in its early days—though the results suggest that the fastest-growing companies (our high performers) are already ahead of their peers. Respondents at these companies say they are thinking more critically than others about monetizing their data, as well as using data in a greater number of ways to create value for customers and the business.2 They are adding new services to existing offerings, developing new business models, and even directly selling data-based products or utilities.

Moreover, responses from the organizations that are seeing the most impact from their data-and-analytics programs offer lessons to others striving to make the most of their data. Those companies have, according to respondents, established a strong foundation for analytics in a few ways: clear data-and-analytics strategies, better organizational design and talent-management practices, and a greater emphasis on turning new data-related insights into action.

Data and analytics are changing the way business is done

Overall, respondents say that the use of data and analytics has brought important changes to their companies’ core business functions. For example, nearly half of all respondents say data and analytics have significantly or fundamentally changed business practices in their sales and marketing functions, and more than one-third say the same about R&D. Across industries, respondents in high tech and in basic materials and energy report the greatest number of functions being transformed by analytics (Exhibit 1).

Data and analytics affect business practices most in the sales and marketing function and the energy and high-tech industries.

Data and analytics are also changing the nature of industry competition. Seventy percent of all executives report that data and analytics have caused at least moderate changes in their industries’ competitive landscapes in recent years (Exhibit 2). The most common change, cited by half of respondents, is entrants launching new data-focused businesses that undermine traditional business models. Across industries, respondents report the most significant changes in high tech, media and telecom, and consumer and retail.

New data-and-analytics-related businesses and the application of data insights are changing the nature of competition.

Data monetization is becoming a differentiator

Across industries, most respondents agree that the primary objective of their data-and-analytics activities is to generate new revenue. We asked about data monetization as one such way to create revenue, and the results suggest that these efforts are fairly new. Of the 41 percent of respondents whose companies have begun to monetize data, a majority say they began doing so just in the past two years.

Though nascent, monetization is already more prevalent in certain industries: more than half of the respondents in basic materials and energy, financial services, and high tech say their companies have begun monetizing data. What’s more, these efforts are also proving to be a source of differentiation. Most notably, data monetization seems to correlate with industry-leading performance. Respondents at the high-performing companies in our survey are more likely than others to say they are already monetizing data and to report that they are doing so in more ways, including adding new services to existing offerings, developing entirely new business models, and partnering with other companies in related industries to create pools of shared data (Exhibit 3). Perhaps unsurprisingly, respondents at high performers also see a top-line benefit: they are three times more likely than others to say their monetization efforts contribute more than 20 percent to company revenues.

Compared with their peers, high performers report a greater variety of actions to monetize data—with greater revenue impact.

The high performers’ focus on data monetization may stem from a better ability—and greater need—to adapt to change. Compared with their peers, high-performing respondents report that data-and-analytics activities are prompting more significant changes in their core business functions. For example, respondents at high performers are at least one-third more likely to report significant or fundamental changes to business practices in areas such as supply chain, research and development, capital-asset management, and workforce management. Additionally, they are more likely to report changes in competitive pressure, whether from new entrants launching new data-related businesses, traditional rivals gaining an edge through data and analytics, or companies forming data-related partnerships along the value chain.

Get the foundations right first

Before companies can make meaningful strides with data monetization, they must first set up the fundamental building blocks of a successful data-and-analytics program. We took a close look at a group of companies in which respondents report seeing the greatest business impact from analytics. The results reveal that these “analytics leaders”3 offer important lessons as to where, and how, companies can strengthen their foundations, particularly in areas beyond the technical aspects of building data-and-analytics solutions (Exhibit 4).

Analytics leaders differ from other companies in their data-and-analytics strategy, structure, and executive attention.

Strategy. Many respondents report a lack of a data-and-analytics strategy at their companies, even when the need for one becomes compelling. For example, 61 percent of respondents who recognize that data and analytics have affected their core business practices say their companies either have not responded to these changes or have taken only ad hoc actions rather than develop a comprehensive, long-term strategy for analytics. In contrast, analytics leaders are nearly twice as likely as others to report enacting a long-term strategy to respond to changes in core business practices.

Organization and talent. While either a decentralized or centralized organizational model for data-and-analytics activities can work, the results suggest that a hybrid model incorporating elements of both is much more common among the analytics leaders.4 At the leader companies, respondents are more than three times as likely as those whose companies are struggling to see an impact from data and analytics—the laggards5 —to say they are using a hybrid model led by a center of excellence, one of two hybrid models the survey asked about.

For all respondents—and regardless of the organizational model their companies use—attracting and retaining talent appears to be even more difficult than it was in our previous survey on the subject.6 Nearly 60 percent of respondents now say it is harder to source talent for data-and-analytics roles than for other positions, compared with 48 percent in our previous survey. This challenge is acute even for the analytics leaders, which have a harder time than others do in finding people with both technical and domain expertise—sometimes called translators. At leading companies, 24 percent of respondents identify the translator role as their organizations’ most pressing need for talent.

Leadership and culture. Successful data-and-analytics programs also require real commitment from business leaders, along with a consistent message from senior leaders on the importance and priority of these efforts. Overall, respondents report that senior-management involvement in data-and-analytics activities is the number-one contributor to reaching their objectives. At the analytics leaders, senior-management practices prove the point further. Respondents at these organizations are five times more likely than those at analytics laggards to say their executive teams spend more than 20 percent of their time at high-level meetings discussing their data-and-analytics activities.

Overall, though, the survey indicates that senior-leader alignment on data-and-analytics initiatives is still not optimal at many companies. At some firms, CEOs differ from other senior leaders in their perceptions of analytics program management, organizational structure, and keys to success—a situation that creates the potential for mixed messages. For example, CEOs are much likelier than other senior executives (53 percent, compared with just 10 percent of others) to identify themselves as the leaders of their organizations’ data-and-analytics agenda (Exhibit 5). CEO respondents are also more likely than others to report effectiveness at reaching data-and-analytics objectives and are less likely to view data scientists and engineers as a pressing talent need. Finally, the CEOs differ from other executives in their reasons for why their organizations have not responded to competitive or core business changes in their industries. While the others overwhelmingly cite a lack of senior-leadership commitment, CEOs are more likely to cite a lack of financial resources and uncertainty about which actions to take.

CEOs often see themselves as heads of the data-and-analytics agenda and cite different reasons for not pursuing analytics activities.

Looking ahead

Getting data monetization right requires significant effort, but it’s becoming critical for staying ahead of traditional competitors and new disruptors. Based on the survey results, here are some steps executives can take to start their data-monetization efforts on the right foot:

  • Focus on yourself first. It is nearly impossible for a company to succeed at creating externally focused data-based businesses while still struggling to get clean, consistent data that are shared internally across the organization. Before companies start down the path of monetization, they should take the time to shore up their data foundations—strategy, design, and architecture—which will help them build the business case and technical platform they need to monetize data effectively. Putting their data to work for internal use cases, such as improving decision making or optimizing operations, can also serve as a testing ground for their data foundations as well as for the data-monetization models of new data-based businesses.
  • Look outside for innovation. Once companies’ data-and-analytics foundations are in place, they may still find that the most innovative solutions can best be sourced externally, by partnering with others in the data ecosystem. Such partners include analytics companies that can supplement the organization’s existing capabilities, platform providers that host tools or solutions, and data providers that can help the organization gain access to unique data sets. Companies can even work with suppliers, customers, or their industry peers to augment and enrich existing data; they can then offer those data as unique add-ons to existing products or services, or sell the data as part of an entirely new business.
  • Commit to an end-to-end transformation, and get the business involved. Even as data monetization gains steam, many companies are still struggling to drive major business impact. In our experience, this happens for two reasons: failure to make the wholesale changes required to enter new markets, and a lack of partnership between the business and IT. For a transformation, such changes could involve the reconfiguration of operating models and core business functions (from product development to marketing), worker-reskilling programs, and change-management programs aimed at shifting organizational culture, mind-sets, and behaviors. These sorts of substantial efforts require full commitment from the C-suite, who must communicate to senior managers—in both business units and technology centers—the priority of a given initiative or program and the need to dedicate adequate time, human capital, and financial resources to make it succeed. Many companies also struggle with data monetization—and, in particular, finding the right strategy—when they delegate all data-and-analytics efforts to IT. In reality, efforts to monetize data are more effective when they are business led and focused on the most valuable use cases.

About the author(s)

The contributors to the development and analysis of this survey include Josh Gottlieb, a specialist in McKinsey’s Atlanta office, and Khaled Rifai, a partner in the New York office.
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