Content
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Handle of the data
Data is always computed during the night, so data from today is available tomorrow. The working days come from the building settings, if you change the opening day’s for a building the future (from today on) will include these days. (If you would like to include the weekend in the report, you have to change it in the building settings. From the date of change on the Ad. Analytics will calculate also the new days on, data in the past will be not calculated)
For dashboards use sensor data we only take valid sensor data, if a workspace have no valid data for one day or more we handle this workspace like he would not exist (all calculations are done without this workspace). A sensor could have invalid date if something of the following issues were detected:
There is a physical shock on the device (for instance a knee bumping on the desk sensor)
There is an electrical static discharge
The USB is plugged/unplugged
If a sensor is not able to communicate with the server, he will buffer the usage data up to 10 days or more (depends on the usage). As soon as the sensor is able to communicate again, he will send the data to the server and these data are visible in the following day.
All data in the Analytics are anonymous and will be stored for max. 2 years. After 2 years, we delete the analytics data.
Layout for all dashboards
The layout of the is composed of 3 parts: the navigation, the filters and the report:
Navigation
Context
The context represents what kind of information you would like to analyze. At the moment, the following contexts are available:
Utilization
Bookings
Utilization %
No-show
Point of view
Depending on the context, you can then select a point of view in order to do comparison. The Organization point of view will allow you to compare buildings and floors, whereas Floor will allow you to compare the workspaces on the same floor.
Help
It will bring you to this documentation.
Reports
Categorization vs. Number of hours
As a workspace manager, you are looking for patterns, outliers and key differentiators between the workspaces. When comparing them, the hours of utilization seem to be the right metric, but when averaged on several days you a losing an important differentiation criterion.
Let’s have a look to an example:
Monday[hours]
Tuesday [hours]
Wednesday hours]
Thursday [hours]
Friday [hours]
Average Hours
ROOMZ Category
Desk 1
11
0
0
0
0
2.2
Rarely
Desk 2
2
2.5
2.5
2.5
1.5
2.2
Frequently
Here, each desk has an average utilization of 2 hours. But as you can see, there is a clear difference. While the desk 1 has been well utilized on Monday, it was not utilized during the rest of the week.
Based on several years of experiences, ROOMZ, developed a machine learning-based algorithm allowing to categorize the utilization and bookings. The workspace will be in one of the following. very rarely, rarely, frequently or very frequently.
Note: unlike the basic analysis, working hours no longer need to be taken into account in this algorithm.
Utilization & Bookings
The utilization represents the real presence. This information can be obtained thanks to the ROOMZ Sensor.
The bookings represent the information that usually comes from the meeting room's reservation system. For the desk, it is generally hosted on ROOMZ Cloud.
Per Organization
Per Floor
Subordinate pages
Child pages (Children Display) | ||||||||||
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