Here is an excerpt from an article written by Jon Wettersten and Dean Malmgren for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.

Illustration Credit: Crosailes /Getty Images

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Rise Science came to IDEO with a challenge. The young startup had built a robust data platform for college and professional athletes to track their sleep and adjust their behavior so that they played at peak performance. But for the players, the experience was challenging. Rise expected athletes to look at data-driven charts and graphs to determine what decisions to make next, but players struggled to find those insights. Rise was convinced they just needed easier-to-read charts and graphs.

As IDEO designers and Rise’s data scientists spent time with players and coaches, they discovered that Rise didn’t have a data visualization problem, they had a user experience problem. Charts and graphs were far less important than knowing when to go to bed each night and when to wake up the next morning. Within a few weeks, the charts and graphs moved into the background of their app and an alarm clock and a chat tool took center stage.

In the 18 months since relaunching their service, Rise Science has signed up over 15 of the most elite pro and collegiate sports teams, as well as ​several companies who hope to improve employee performance and wellbeing through better sleep habits.

This example shows how human-centered data science can result from interdisciplinary teams incorporating design thinking into their approach. Instead of a version of data science that is narrowly focused on researching new statistical models or building better data visualizations, a design-thinking approach recognizes data scientists as creative problem solvers. We’re not suggesting that the disciplines of data science and design merge, but rather that if practitioners work together and learn each other’s art they will produce better outcomes.

Many of the techniques we use in our human-centered design process at IDEO—user research, analogous inspiration, sketching and prototyping — work well with data-driven products, services, and experiences.

Data by themselves are inert — dumb, raw material. Making things smart will mean designing with data in a way that reflects and responds to the functional, social, and emotional behavior of users. If you start with the needs and insights of people rather than leading with data, you can gain insights through the combination of qualitative design research and exploratory data analysis. This hybrid approach can radically change the user experience for the better and be a true differentiator.

For example, Rise and IDEO visited college athletes in dorm rooms and training facilities to develop a deep understanding of their day-to-day needs — a common design-thinking practice known as user research. We observed that nearly every component of the player’s life is scheduled, measured, and optimized. Giving them more data to digest was simply too much to ask. We also learned that the onboarding experience and service touchpoints like in-the-moment sleep coaching were just as important to the athletes’ success as the data visuals tied to their sleep.

Data scientists see the world in unique ways, but they can only leverage that point of view when they have a chance to go out into the world and spend time with human beings. Engaging data scientists in design research produces powerful insights and, more importantly, unlocks empathy for the people who will be touched by the data engines they develop.

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Here is a direct link to the complete article.

Jon Wettersten is a design director at IDEO, where he focuses on software as a creative discipline.

Dean Malmgren is an executive portfolio director at IDEO, an engineer, and an entrepreneur. Last year, Dean joined IDEO through the acquisition of Datascope, a data science consulting firm he co-founded in 2009.