Commercial office is an undeniably data-rich industry. A multitude of technologies are deployed within buildings and each of these systems contains a wealth of potentially powerful customer data. However, we face a paradox: despite this abundance of data points, we struggle to extract meaningful insights. Data remains unstructured and trapped in disparate and disconnected silos, hindering a holistic view of customer behaviors and preferences.
Our industry's approach to extracting value from data has evolved over the last few decades. Early PropTech solutions focused on the building as a financial object, with systems of record built around transactions, leasing, and property management. In this first generation, performance was measured by revenue-based metrics like rent rolls and lease duration.
The next generation introduced digital twins and a focus on digitizing real estate operations. While a crucial step, the focus remained on the building—rather than the behaviors of the people using the building. More recently, there have been efforts to leverage 3rd party data sources and benchmarks to gauge asset performance. However, these sources, while directionally useful, lack the granularity needed for customer-level insights.
We believe the industry now stands at a chasm, recognizing that for today's generation, a new definition of success is emerging—one that balances traditional property metrics with a deeper understanding of customer behaviors. To truly harness data's power, we recommend mastering these core data management competencies.
Across myriad operational tools, there are rich datasets to harvest. However, inconsistencies in data management policies and structures can create more headaches than answers.
We’re past the AI hype cycle—what matters now is grounded application. Using LLM or Agentic Models directly on unstructured data requires significant investment of time and resources. Whereas, structured data provides a consistent representation of the underlying data from disparate operational systems. This can form the foundation for reliable AI applications and efficient data analysis.
Unstructured Data:
Structured Data:
Data aggregation involves collecting data from various sources and compiling it into a single repository. This can involve tasks like data cleaning, standardization, and storage.
While organizing data into a centralized location is a necessary starting line, it's crucial to distinguish between data aggregation and generating actionable insights. Simply having a big pile of organized data does not automatically translate to better decision-making.
Data insights, on the other hand, go beyond mere collection. They involve analyzing aggregated data to identify patterns, trends, correlations, and anomalies that can inform business decisions. This requires applying analytical techniques, statistical modeling, and domain expertise to extract meaning from the data.
The key difference lies in the level of interpretation and actionability. Aggregation is a technical process; insight generation is an analytical process. Aggregated data provides the raw material; insights provide the actionable intelligence.
Unlocking data’s value requires democratizing access to tailored insights across the organization, recognizing that different teams (e.g. leasing in Phoenix vs. asset management in NYC) have distinct needs.
Targeting and contextualizing data is essential for collaboration and aligning operational goals with market demands. This requires sorting, filtering, and prioritizing insights by role, geography, portfolio, and purpose.
Ultimately, it isn't about dashboards for the sake of dashboards. It's about arming people with the right insights, at the right time, so they can make more informed decisions.
Wrapping Up:
At the end of the day, the real value of data in commercial real estate isn’t just about numbers—it’s about people. It’s about using what we know to build stronger relationships and create better experiences for the people who use our spaces every day.
That means going beyond just collecting data. We need insights we can act on—insights that shape how we design, manage, and operate our buildings in smarter, more meaningful ways.
By putting the right data practices in place, we unlock a clearer picture of who’s using our spaces and how. With that understanding, we can make decisions that actually improve well-being and drive better outcomes—for our customers and for the assets themselves.
