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  "title": "Workplace analytics",
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  "description": "Workplace analytics describes evaluating usage data from an office – bookings, check-ins, no-shows – to steer space, services and cost.",
  "summary": "Workplace analytics describes the systematic evaluation of office usage data: bookings, check-ins, no-shows, cancellations, visitor numbers and their distribution across floors and weekdays. The goal is not observing people but steering space, cleaning, services and investment based on real usage.",
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    {
      "heading": "The difference between booking and usage",
      "level": 2,
      "text": "The most important distinction in this field is between reserved and used. A booking says somebody intended to come. Only the check-in proves somebody was there. The difference – the no-show rate – is often the most interesting metric of all, because it shows how much capacity is blocked by unclaimed reservations."
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      "heading": "Metrics that actually carry decisions",
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      "text": "Three matter in practice: peak day attendance as the basis for desk count, occupancy per area to identify unused space, and the no-show rate as an indicator that rules need attention. Everything else is useful but rarely decision-relevant."
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      "heading": "The privacy question",
      "level": 2,
      "text": "Workplace analytics targets spaces, not people. Technically that is secured through anonymization periods and a role concept, organizationally through clear purpose limitation in the works council agreement. Organizations that do not draw that line early risk the whole initiative failing on codetermination."
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    "title": "Workplace analytics",
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