Metrics
Learn about the difference between Point-in-time and Over-time metrics.
Metric characteristics
There are two different types of time behavior for metrics. These are defined as Point-in-time and Over-time.
Point-in-time
Point-in-time metrics represent data at one specific point in time. Headcount is an example of a Point-in-time metric, as it represents the total number of employees on a certain day. If a period is selected (e.g., January 2018), the last day of the period (e.g., January 31st, 2018) will be used.
Point-in-time metrics are not additive over time, meaning that the metric values for multiple time periods should not be added together. For example, adding the headcount for January 30th with the headcount for January 31st does not make sense. The following screenshot shows a Breakdown chart of Headcount in January 2018. The values in the chart represent the data on the last day of the selected period (January 31st in this case).
Over-time
Over-time metrics represent a collection of values that occur over a time range. Usually these metrics are counting events that happened in a specific period (e.g. a month). Resignation count is an example of an Over-time metric, as it represents the total number of employee resignations in a specific period.
Over-time metrics are additive over time, meaning that the metric values for multiple time periods can be added together. For example, the Resignation Count in January 2018 can be added to the Resignation Count in February 2018 for a total Resignation Count for both months. Use the trailing time calculation to add the metric values for multiple periods together.
The chart below shows a Trend chart of Resignation Count with a 24 month timeline and a December 2018 end point.
The data within each time period (month, in this case) represent the resignations during that period.
