Column: Can a Culture of Innovation Co-Exist with a Data-Driven World?

Data and creativity may seem like competing forces, but by cultivating them simultaneously, companies can achieve their highest levels of success

Google often comes to mind as an exemplar of innovativeness. The company is heralded for certain attributes including its playful, whimsical physical environment, its willingness to let employees spend a certain percentage of their time on creative activities and its nontraditional interview process that focuses on how people think and problem-solve rather than the answers they generate.

But Google is also known as having a data-driven culture, relying on data, technology and engineers to inform decision-making to achieve competitive advantage. The company is proof that a highly creative culture might co-exist with a data-driven culture.

Data and creativity may seem like competing forces, but Google’s success and ability to lead on both fronts suggest it’s an idea worth exploring: What are the organizational values that characterize a culture of innovation (also known as a culture of innovativeness) or those that characterize a data-driven culture? I’ve found that both exhibit a striking degree of similarity in underlying traits. But these cultural values, while compatible, must be cultivated simultaneously for a company to achieve its highest levels of success.

Culture of Innovativeness

Research on innovative cultures indicates that they start at the top. Managers establish bold, audacious goals and an ambitious vision that inspire the team to engage in breakthrough thinking. Somewhat paradoxically, managers who are highly motivated by fears of obsolescence use such fears to catalyze novel thinking; such managers who are “paranoid” about being out-competed by new types of competition—sometimes referred to as being “Amazoned”—demonstrate a willingness to cannibalize their own legacy products in order to lead the market with breakthrough innovations.

Moreover, companies with a culture of innovativeness encourage disciplined risk-taking and exhibit a tolerance for failure based on the understanding that not all innovative ideas will pan out and that learning from failure can inspire the next innovative idea that may succeed.

In addition, companies with a culture of innovativeness exhibit an agile mindset (adopted from agile software development) when it comes to learning. They plan fast, low-risk experiments to gauge market response to possible innovations prior to making major investments. Their teams comprise diverse members, based on the fact that diverse teams exhibit higher levels of innovativeness. Each member’s contributions are valued and they do not let ego, hierarchy and authority get in the way of divergent thinking. Product champions, sometimes referred to as renegades, leverage their informal networks to cultivate a coalition in support of their radical ideas; these individuals play a key role in challenging status-quo thinking and firms with a culture of innovativeness reward their champions with resources to better think innovatively. Otherwise, as research shows, they may become disillusioned and opt to work for a competitor.

Innovative cultures embed these types of attributes into the DNA of the company. Rather than isolating creative endeavors in skunkworks or new venture groups removed from the organizational hierarchy, creative cultures infuse values and beliefs regarding innovative thinking throughout the company’s people and processes. For example, rewards for innovative thinking are embedded into Google’s innovation process. If an individual’s initiatives all succeed, it’s an indicator of playing it safe rather than taking risks.

Finally, innovative cultures are known for using metrics to gauge how well the company is doing, whether it’s the percentage of revenue derived from new product releases, how their new products have redefined industry boundaries or created new industries, or tracking whether competitors are doing a better job of achieving those outcomes.

Data-Driven Culture

Companies with data-driven cultures expect to see data used to support evaluation of strategic alternatives. For example, one story told about Google is that managers cannot rely on opinions or “hippos” (the highest-paid person in the office) to make decisions or to resolve strategic alternatives. Instead, managers and employees present data to evaluate alternatives. Data-driven cultures take great care in ensuring that the right question is being asked, and that the data marshaled to inform possible answers are appropriate for the question at hand.

According to an article in Harvard Business Review, “technology isn’t the biggest challenge” in establishing a data-powered organization—but culture is. Similar to a culture of innovation, data-driven cultures require leadership to establish the vision for how data and analytics can create new opportunities. In support of challenging status-quo thinking, these leaders view the need for data analytics capabilities as an “existential imperative”: the risk of not relying on data is falling behind in the important analytics capabilities that will drive future success. These managers model the ways in which organizational decision-making will rely on data by asking questions such as, “What does the data say to inform this decision?”

In the HBR article, Tim Fountaine and his colleagues at McKinsey demonstrate that data-driven cultures move from a rigid, risk-averse mindset to one that is agile and adaptable. Using a “test and learn” mentality, managers in these companies reframe mistakes as a source of discovery and reduce fear of failure.

Companies that harness the power of data have interdisciplinary teams, based on the notion that technology alone doesn’t create a data-driven culture: It requires that the users of data (e.g., the business units) must be integrally involved in the applications. The value of diversity in perspectives avoids myopia. And because data-driven cultures may require changes to processes and workflows, involving broad coalitions of users ensures greater acceptance. These initiatives may be led by key individuals, sometimes known as data translators (or “culture catalysts”) who have the unique ability to speak both the technical language of data science and the language of the non-technical business unit managers. As data evangelists, they can identify roadblocks in diffusing data-driven thinking and build bridges between the technical team and the users of the data.

Data-driven cultures embed the technology and capabilities across the organization—sometimes referred to as “the democratization of information.” Fontaine and his colleagues note that “90% of companies who successfully deployed AI spent more than half of their budget on deployment activities, such as workflow redesign, communication and training. This trait includes understanding what areas of the company might be highly resistant to data-driven thinking, such as a company that sees interpersonal connections in customer relationships as the sacred domain of key account managers or one that is based on “executive experience and wisdom.” In contrast, bringing the power of data to inform these areas can perhaps unleash new value.

Finally, data-driven companies use metrics to score project performance, including the pace and degree of adoption of technical tools (e.g., AI platforms), as well as business outcomes.

Are These Two Types of Organizational Cultures Compatible?

Both of these organizational cultures have much in common, given that they require leadership and vision from the top, are motivated by fears of being out-competed, they encourage appropriate risk-taking, they value diverse perspectives and encourage out-of-the-box thinking, they reward the efforts of key champions to drive change, they embed these values across the organization and they rely on metrics to evaluate performance of the initiatives.

But this does not mean that they are synonymous. Rather, a company could exhibit variation in both its culture of innovativeness (e.g., higher or lower) as well as its culture of relying on data-driven decision-making (more or less). Based on this thinking, I would suspect that companies that lack both a culture of innovation and a data-driven culture will struggle to thrive. Companies that are primarily data-driven and lack a culture of innovation are likely to experience only incremental changes in their business models and processes. Those that are primarily oriented around innovation but lack data-driven processes are likely to experience missed opportunities or market failures that might have been avoided.

Companies that manifest both a culture of innovation and a data-driven culture will unleash the best that both have to offer and see synergies in doing so.

Illustration by Bill Murphy.