Teamwork as a Science: A Data-Backed Approach to Workplace Collaboration

This article was written by McKinsey & Company's Wendy Miller, Chief People Officer for North America and Bethany Rolan, Manager, People Strategy & Operations. For more insights, listen to the People + Strategy podcast episode featuring Miller.

Over the past five years, we’ve really seen a revolution in how we work, thanks to digital tools, advanced analytics, and AI. These technologies have made our workspaces more flexible and collaborative, allowing us to adopt agile methods and use hybrid technologies that enhance our ability to work together across the globe.  

Leaders often assume teams will harness new technologies and advancements in productive ways, but unfortunately, without real intention and thoughtfulness, new ways to collaborate do not always translate to teams doing so effectively. Luckily, there are proven ways to cultivate teamwork by collecting data and converting it into actionable insights. Today, cultivating high-performing teams is not merely about collaboration; it is a disciplined science.  

We’ve witnessed firsthand the transformative power data-driven insights can have to cultivate high-performing teams. At McKinsey, we created a continuous learning system using data science that taps into insights from the more than 4,000 teams we have deployed at any one time around the world. By analyzing how teams operate throughout a project and then surveying them post-project, we can evaluate what did—and didn’t—work, helping us continually improve our team rituals and tools.  

It is this linkage of metrics to outcomes, both for individuals and teams, that is transforming the way we work. 

The Foundation: Aligning Data Collection with Business Objectives 

Collecting data on a consistent basis is key for accurately measuring employee performance. At McKinsey, our goals are employee retention and exceptional client service ratings.  

To track these metrics, we rely on the following: 

• Annual pulse surveys that gauge employee sentiment and spotlight emerging issues. 
• Biweekly team surveys for ongoing insights into team dynamics and immediate feedback from client service teams. 
• The Organizational Health Index to assess long-term health and forecast performance trends. 
• Impact surveys we send to our clients to measure impact and experience.   

Tip: Provide your team leaders real-time access to the data collected so they can determine what is and isn’t working, adjust, and efficiently enact change within their teams.   
 

Real-Time Example 

As we navigated post-pandemic realities at McKinsey, our organization, like many, faced decisions about return-to-work policies. Instead of enforcing a blanket return to the office, we leaned on data and analytics to guide us. We conducted a comprehensive analysis involving thousands of team surveys and interviews. Then we selected specific teams to participate in experiments where we assigned them to various in person working schedules, such as alternating weekly between in person and remote work, two days in person, or four days in person. We then evaluated the performance of these teams based on critical outcomes including team effectiveness, client impact, and team satisfaction. We ultimately found that for our teams, there is a sweet spot for hybrid work: spending roughly 50% of the time working in person at a client site or in an office.   

This data-driven approach to making organizational changes has yielded better outcomes for both our clients and teams. It’s led to better productivity and engagement, supported workforce sustainability, and boosted client impact. For example, our analysis showed that: 

• Colleagues who co-locate no more than 50% of the time are two times as likely to report having a more sustainable work-life balance. However, when teams co-locate more than 50% of the time, individuals’ satisfaction with their work-life balance starts to decline. 
• Colleagues are 10 times more likely to feel that they are working well together when co-locating at least 50% of the time. 
• Quality of mentorship increased by 25% for teams who spent some of their time in-person. 
• Team, client, and individual outcomes increased significantly when colleagues spent at least 20% of their time in-person with their clients.  

Had we simply relied on pre-pandemic intuition, we might have reverted to a mandated four-day (80%) in-office week. Instead, our data-driven strategy allowed us to optimize our work model, enhancing our ability to deliver top-notch client service while attracting and retaining the best talent.    

People Analytics: Turning Data into an Operating System 

Data collection is just the start. The real impact comes from the partnership between our people analytics team and our business leaders. Our analytics team sifts through multiple datasets, applies advanced analytics methods, and grasps the organizational context to extract meaningful insights. They then partner with our leadership team to determine actionable steps and identify how to effectively implement them throughout the organization.  

Real-Time Example  

To foster a strong partnership between people analytics and our business leaders, we introduced an enterprisewide operating system known as the Way We Work (WWW). This system is built on several core principles and practices that foster effective collaboration and distinctive impact for both clients and internal teams. These rituals, used by all McKinsey teams, were developed in close partnership with our people analytics team. Through testing and learning, we’ve ensured these practices enhance team performance and experience across the board. 

Today, all managers and new hires are trained on the WWW guidelines, and middle managers track which teams actively use them. Through this process, the four WWW core best practices are:  

1. Strong Kickoff Meetings: Establish clear roles, responsibilities, and deliverables. Discuss the purpose and significance of the work. 
2. Recurring 1:1s: Provide consistent feedback and coaching to foster personal connections within the team. 
3. Regular Retrospectives: Allocate dedicated time for teams to discuss what’s working and what isn’t so they can create actionable plans for improvement. 
4. High-Impact Handovers: Ensure smooth transitions when team members change roles or leave the team. 

Implementing these practices led to significant improvements in both team and client satisfaction metrics, demonstrating the science of teamwork. 

The formula involves adding the intuition that comes from experience to the insights deeply rooted in data. Remember: Even the most sophisticated data analytics won’t yield results alone. It is the employees who use the information to support their peers’ day-to-day workplace practices that create impact. Teamwork as a science is not just numbers; it’s a powerful tool that, when used correctly, transforms how teams operate and thrive.