Workplace Data in Action: Insights from Tiffany English, Qualcomm
At the CoreNet Global Summit North America 2025, Tiffany English, Senior Director of Architecture at Qualcomm, shared how workplace and occupancy data is being used to support portfolio and operational decisions across the organization. Her remarks focused on understanding real behavior in a flex environment and using that understanding to act more deliberately.
“We’re not working just in the real estate and facilities bubble. It’s about taking all of that data and creating information the company can actually use.”
Tiffany English, Qualcomm
Using workplace data to evaluate the portfolio
Qualcomm operates under a five-day flex return-to-office mandate. To understand how that model translates into actual space use, the company relies on occupancy data to evaluate its portfolio over time.
At the Cambridge campus, this approach revealed how teams were utilizing four buildings in practice. Based on those observed patterns, Qualcomm was able to optimize the footprint and downsize from four buildings to two, with the goal of making the remaining space more efficient and productive.
This decision was grounded in longitudinal use, not assumptions about how space should be used.
Understanding behavior, not just presence
As Tiffany explains, workplace behavior varies significantly by region, shaped by local culture and working patterns. Because of that, a single global assumption rarely reflects reality on the ground.
What Qualcomm is now focused on understanding is how people work in a space with flex hours on site and how they make that time intentional and purposeful when they are there.
“What we’re really trying to understand from the data now is how people are working in a space with flex hours on site — and how they’re making that time intentional and purposeful when they’re there.”
Behavior data, including sensor and occupancy signals, helps reveal how people move through the office, where they connect, and how space supports collaboration. These insights feed directly into workplace experience and design decisions, enabling teams to work with a clearer narrative rather than isolated data points.
Extending data beyond the workplace into operations
A key theme in Tiffany’s remarks is that workplace data does not stop at real estate planning. Once patterns are visible, the same data can be applied to facilities services.
She points to several concrete areas:
Janitorial services, where understanding presence helps reduce unnecessary costs
HVAC operations, noting that there is no need to run air conditioning after certain hours if offices are largely empty
Food services, particularly in regions where meals are subsidized and attendance patterns directly affect demand
Turning data into usable information with AI
As data volumes grow, Tiffany notes a practical constraint: many teams are understaffed and stretched thin. The challenge is no longer collecting data, but making it usable.
Her focus is on bringing data together and using AI to help sift through signals, draw conclusions, and support forecasting. The objective is not to add more dashboards, but to reduce data fatigue and turn workplace data into information the broader organization can act on.
In this framing, AI supports decision-making by helping teams focus on what matters — and by enabling a view that extends beyond real estate and facilities alone.
Looking ahead
Tiffany frames this approach as part of a broader shift in how organizations think about workplace data. By combining portfolio insights, behavioral signals, and operational context, teams can move away from siloed decision-making and toward a more connected understanding of how work actually happens.
The emphasis is practical and grounded: use data to support better decisions, without overwhelming the people responsible for making them.
