The Critical Role of Data in Portfolio Strategy and Occupancy Planning
Just as buildings need a solid foundation, strategies crumble when built on poor or incomplete data. This leads to misaligned decisions, wasted resources, and failed implementations—a pattern I frequently observe in portfolio strategy and occupancy planning.
If the data in an organization’s Computer-Aided Facility Management (CAFM) systems is incomplete, poor-quality, or worse, nonexistent, developing portfolio and occupancy strategies can be likened to solving a complex puzzle with missing pieces—frustrating and impossible.
Planners need complete and accurate supply data (floor plans, space types, organizational hierarchies) and demand data (employee assignments, seat allocations, hybrid work arrangements) to make informed decisions about their real estate portfolio and occupancy strategy.
From Directional Strategies to Actionable Plans
It is important to consider the key differences between Portfolio Strategy and Occupancy Planning in order to understand the quality of data required to make informed decisions. The more actionable the plan, the more detailed data is required.
Portfolio Strategy
Portfolio strategy focuses on developing a high-level roadmap for portfolio optimization and workplace transformation, evaluating the following:
Analysis of business growth/contraction projections and their impact on headcount
Evaluation of current space utilization rates and occupancy patterns
Assessment of lease terms, expiration dates, and market conditions
Review of workplace strategy including hybrid work policies and their impact on space needs
Financial analysis of operating costs and potential savings opportunities
Occupancy Planning
Once strategic decisions are made, occupancy planning translates that strategy into action through the development of detailed occupancy scenarios, stack plans, and move plans:
Detailed space programming and department-specific requirements
Analysis of current vs. future seat counts by department
Evaluation of departmental adjacencies and workflows
Infrastructure assessment (power, data, specialized space requirements)
Phasing and sequencing of moves to minimize business disruption
While both processes require quality data, portfolio strategy relies more heavily on projected future states and hypothetical scenarios, whereas occupancy planning requires granular current-state accuracy at the individual employee and workspace level.
Pitfalls of poor quality data in decision making
Organizations that attempt to execute workplace moves based on incomplete, inaccurate, or outdated CAFM data inevitably face significant operational challenges and unnecessary costs. While making broad assumptions during initial planning phases is sometimes necessary, there comes a critical point where having clean, comprehensive, and well-structured supply and demand data becomes essential for successful execution of moves and changes.
Delayed delivery
When working with insufficient data, organizations must pause their workplace transformation initiatives to gather and validate essential information needed for implementation. This pause, though seemingly delaying progress, is a vital investment that prevents costly mistakes and employee dissatisfaction.
Back to the drawing board
The process of collecting and analyzing missing data often reveals previously unconsidered challenges, such as departmental adjacency requirements, specialized space needs, or technology infrastructure dependencies that weren't apparent during initial planning phases.
These new insights may require reevaluating the original strategy, sometimes necessitating a return to preliminary planning. While this extends the timeline, it ensures more accurate move plans and better serves both organizational objectives and employee experience.
Data Quality and Governance
Organizations must understand the key difference between data completeness and data quality in their CAFM systems. Data completeness means having all necessary information about spaces, employees, and organizational structures populated, while data quality refers to the accuracy, reliability, and currency of that information. Having employee records in a system doesn't automatically guarantee their assignments or space requirements are up-to-date.
Organizations need systematic processes to verify data accuracy and maintain its currency as workplace strategies evolve. While readily accessible, reasonably accurate data can speed up move planning and implementation, it's important to recognize that workplace data is dynamic. As organizational structures change and new workplace requirements emerge, perfect data quality is unrealistic—but having reliable data remains essential to begin planning any workplace transformation initiative.
Evaluate Data Readiness
As you evaluate your organization's data readiness for strategic initiatives, consider these key questions:
How complete and accurate is your current data? Assess gaps in your data collection and validation processes.
What systems and processes are in place to maintain data quality over time? Consider implementing regular data audits and updates.
How well does your data infrastructure support strategic decision-making? Identify areas where better data collection could improve planning outcomes.
The time to address data quality is not when you're in the midst of executing a strategic initiative—it's now. Begin by assessing your current data landscape and developing a clear roadmap for improving data quality and completeness. This investment in your data foundation will pay dividends in more effective strategic planning and execution.
