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Optimising Workplace Utilisation: Why Relying on Badge Data Can Mislead You by 30%

šŸš€ Stop Using Badge Data to Measure Space Occupancy - You Might Be Off by 30%! During numerous discussions with prospects and clients, we often hear that they prefer to use badge data to measure space occupancy, despite it being primarily a security tool. Why? For budgetary reasons. They choose to risk missing out on 30% potential savings on office costs rather than investing a few thousand euros in a specialised study. Strange, isnā€™t it?

Jean-Baptiste JACQUEMIN

Jean-Baptiste JACQUEMIN is Managing Director BeLux, France & Switzerland and part of the Global Customer Success Team.

example badging systems

Badge data, though abundant and easily accessible, is not designed to measure actual workspace occupancy. Its use can lead to significant errors in evaluating space utilisation, often overestimating actual occupancy by a considerable margin.

šŸ¤” Badge System, Space Observation Study, or Sensor System? Which Should You Choose?

To understand which method is the most reliable, it’s crucial to compare badge systems, space observation studies, and sensor systems.

šŸ“‡ Badge Systems:

Badge systems record employee entries and exits, often through electronic access cards. While these data may seem like an easy and cost-effective source for estimating occupancy, they have several major drawbacks according to the badging system:

  1. Overestimation of Occupancy: Badge systems count every entry and exit, including frequent employee movements (breaks, external meetings, etc.). This leads to a significant overestimation of actual occupancy, up to 30% compared to sensor systems.
  2. Lack of Temporal Data: These systems do not capture the duration of presence, only providing a snapshot that doesnā€™t reflect continuous or intermittent space usage.
  3. Primarily a Security Tool: Designed for security and access control, these systems are not suited for fine-grained analysis of workspace utilisation.

example badging systems

šŸ‘€ Space Observation Studies:

Space observation studies involve human observers who note the use of spaces at various times throughout the day. While more accurate than badge data, they also have their limitations:

  1. Visual Accuracy: Observers can visually identify individuals present, but this method remains to a few mistakes.
  2. Limited Real-Time Data: These studies provide a point-in-time snapshot rather than continuous tracking, lacking the precise temporal data needed for in-depth analysis.
  3. Cost and Logistics: Involving human observers can be costly and logistically complex, especially for prolonged studies.

However, this type of study provides a much more accurate view than badging systems in a very short space of time, and also allows us to identify the activities carried out in the spaces – a real advantage in the age of activity-based working.

šŸ› ļø Sensor Systems:

Sensor systems, utilising advanced technologies such as motion detectors, thermal sensors, and presence sensors, offer an automated and continuous solution for measuring space occupancy:

  1. Real-Time Data: Sensors continuously capture presence data, providing an accurate and temporal view of occupancy.
  2. Reliability and Consistency: Unlike human methods, sensors eliminate subjectivity, delivering uniform and reliable data.
  3. Superior Accuracy: Studies show that sensors provide occupancy rates about 10% lower than observation studies and 30% lower than badge systems, reflecting a more realistic measure of space utilisation.

Conclusion

Accurate measurement of workspace occupancy is essential for optimising costs and improving organisational efficiency. Relying on badge data can lead to decisions based on erroneous information, thereby compromising potential savings and space optimization.

Making strategic decisions based on incorrect data can have long-term negative impacts on the organisationā€™s growth and its adaptation to constantly evolving work environments. Understanding the importance of data and knowing which type of data to use for specific decisions is crucial.

To avoid such errors, we recommend using advanced sensor systems or combining multiple methods, such as observational studies, to obtain a comprehensive and accurate picture of your workspace utilisation.

Ready to Learn More?

If you want to understand in detail why these differences exist and how to avoid making decisions based on incorrect data, letā€™s talk. We’d be delighted to present our research on this subject and explore how we can work together to improve your workspace management.

 

Jean-Baptiste JACQUEMIN

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