Data are often prized for their indifference to intuition, stoic reflection of the facts, and ability to shatter our assumptions—veritable superpowers when put into the hands of decision makers. While data have always been an important business input, recent advances in artificial intelligence (AI) and other analytics are only increasing the number of organizational arenas in which decision makers can activate data’s superpowers, from hiring to product development to customer engagement.

Separately, design thinking has spread like wildfire across industries after some iconic brands and born-digital companies (think Apple and Google) demonstrated the revenues and customer satisfaction it could drive. Harnessing qualitative insights, creativity, and a relentless focus on end-user needs, the approach is typically aimed at product and service innovation.

Yet, while organizations are increasingly reaping the rewards of data and design, we find that many are missing out on the benefits they offer when used in combination. Simply hiring both designers and data professionals to perform their discrete functions (even when on the same project) isn’t enough. Organizations need to enable the two to effectively work in lockstep—so that the whole is greater than the sum of its parts. Getting this interplay right unleashes the ability not only to create killer, user-centric products and services but also, as we illustrate throughout this article, to improve business processes, an area in which the combination of data and design is greatly underutilized by many organizations today. In fact, we’ve seen, on average, a 10 to 30 percent performance improvement among companies that tightly interweave data and design capabilities to solve business problems far removed from product innovation but undeniably critical to the organization’s success.

But how do companies knit data and design together more effectively? Three key shifts are required: moving from silos to squads, from disconnected workflows to deep synchronicity of skills, and from product innovation only to operations-wide use.

In a study of the design practices of 300 companies, we found that the top financial performers had integrated design across the organization rather than creating design units within specific departments, such as marketing or customer service, or within discrete functions, such as customer experience management (exhibit).

In our experience, the most effective integration of design is through the assembly of “squads” that include both data experts (data scientists, data engineers, and so on) and designers (design researchers, visual designers, and others) who are seated side by side—preferably physically in the same workspace. Wherever data and analytics capabilities reside—in a center of excellence serving the enterprise, embedded within the business units themselves, or both—design capabilities should reside as well. While their “straight-line” manager may be within their own field (for example, design reports to a design lead and data experts report to an analytics lead), all squad members should have a “dotted-line” reporting relationship with the squad leader responsible for solving a particular business problem and take their direction on a daily basis from him or her. These squads act as the first responders when it comes to understanding a business problem, identifying and prioritizing potential solutions, and developing the business case and prototypes. Once an organization is ready to build and implement a solution, these squads can easily be integrated into an agile-development team to help bring the solution to life. One McKinsey research study found that 60 percent of companies successfully scaling analytics to solve problems across the organization used cross-functional teams.

Through these squads, teams can achieve a more iterative inquiry process and combine the best of both worlds: deep qualitative insights around motivations, attitudes, and business processes, and strong quantitative insights from data. A data expert might see a pattern emerging in the data and can ask his or her teammates in design to probe end users on the subject during their ethnographic interviews. Design teams might hear an interesting point of view during an interview with an end user and ask their data colleagues to explore whether the belief is unique to the participant or reflects a larger pattern that they need to explore.