A metric is a measurable value. It is the actual data, such as the count of exceptions, the number of scheduled hours, or the cost of actual labor. You can use metrics Measures and tracks workforce performance by comparing planned with actual workload or coverage, or by showing variances at any organizational level. to provide data for Dataviews A configurable tool for analyzing data and taking actions on a group of employees or an organization., reports, and dashboards, thus making them available to managers and other users. You can also use them as building blocks for Key Performance Indicators (KPI) Measures the result of an activity in an organization so that you can compare it to operational or strategic goals and attempt to improve performance. (KPIs).
Analytics defines a number of standard metrics, such as Actual Hours, Late In Exception Count, and Scheduled Productive Costs. It provides a metric named All Exceptions which provides a count of exceptions regardless of their type.
You can also create an unlimited number of custom metrics. For example, you could create a metric named Late In & Early Out Exceptions by applying the Late In Exception and Early Out Exception mapping categories to the Exception Count data source. You could create a metric named Work Hours by applying the Worked pay code mapping category to the Actual Hours data source.
To create a custom metric or KPI Key Performance Indicator measures the result of an activity in an organization so that you can compare it to operational or strategic goals and attempt to improve performance. you must first determine what it is you want to measure. KPI Builder supports three types of measuring units: hours, cost, and count.
After you have selected a measuring unit, you need to determine which objects you want to measure and identify the data source on which you will build your metric.
KPI Builder currently provides data sources associated with the following objects:
- Data sources associated with pay codes
- Data sources associated with exceptions
- A data source associated with punch The entries on a timecard that mark the beginning (in-punch) or end (out-punch) of a work interval, such as the beginning of a shift or transfer. variances
The data sources that are associated with pay codes include Actual Hours and Actual Cost, Scheduled Hours and Scheduled Cost, and Projected Hours and Projected Cost. You can filter these data sources by applying a mapping category that contains one or more pay codes.
The data sources that are associated with exceptions include Exception Count, the hours related to those exceptions (Exception Hours), and the Hours Over Exception (which is the difference between the exception threshold [limit] and the actual exception hours value), In addition, there is a data source that tallies how many exceptions have been reviewed and marked as reviewed (Exception Reviewed Count). You can filter these data sources by applying a mapping category that contains one or more exception types.
The Punch Variance data source has a very specific use: the difference between a raw punch and its rounded Rounding is a way to simplify payroll accounting and reporting, and to enforce shift start and end times. Punch rounds divide hours into equal segments of an hour. punch. The standard metrics generated from that data source, Time Paid Not Worked and Time Worked Not Paid, should be sufficient for most applications. As such the Punch Variance data source is less useful in building custom metrics.
The data source that you select depends on what question you are trying to answer. For example, do you want to look at overtime hours or the hours related to absenteeism? Perhaps you want data related to the hours or costs that can be tied back to one or more pay codes in the system, whether it is leave, absenteeism, shift differential, or on-call time,
You may want to create your own metrics that answer questions related to on-call. How many on-call hours did you pay last week, last month, or last quarter? These metrics may serve as a better means of organizing data, especially in large a organization that has defined multiple pay codes that represent on-call.
KPI Builder provides standard metrics that return counts of specific exceptions, like Missed Out-Punch or Early-In. However, if you want to see some combination of exception counts, like Late-Out, Early-In, and Missed In- and Out-Punches and no others, you could put all of them into one mapping category and then have break-outs for each individually. You would be more likely to build custom metrics for exception hours, as KPI Builder provide no standard metrics for exception hours other than Total Exception Hours. You might want to break down Total Exception Hours into hours for Late-Ins or Early-Outs or Tardiness. Or you might want to see some offset or variance - for example, Late-In and Early-Out vs. Early-In and Late-Out to see the real lost time, rather than just the number of offset hours. For these purpose, you would be likely to create a custom metric.
Building metrics is really generic. What is the unit of measurement? What is the type (actual, projected, or scheduled)? What data source do you want to use? When you assign a pay code mapping category or an exception mapping category to a metric, you are determining the specific data that the metric will return from the data source.
Note: Hours and cost are somewhat interchangeable. If I have a metric that collects all my Leave-related pay codes for hours, I can use that metric to leverage a similar metric that collects the Leave-related pay codes for cost.