Components of GRC: Metrics
Summarize
Summary of Components of GRC: Metrics
The GRC: Metrics application in ServiceNow Australia release enables organizations to define, collect, and manage various types of metrics to monitor performance across entities such as business units. It simplifies metric management through reusable templates and supports automated, manual, and calculated data collection methods.
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Key Features
- Metric Definitions: Serve as templates establishing metric properties like unit, direction, frequency, and category. They streamline metric creation by allowing metrics to be attached to multiple entities without duplication. Types include:
- Automated metric definitions - data collected automatically from tables or scripts.
- Manual metric definitions - data collected via manual input and tasks.
- Calculated metric definitions - data aggregated from child metrics.
- Metric Data: Created when metrics are executed. For manual metrics, data is gathered from metric data tasks upon task closure. Ad hoc metric data tasks can be created for off-cycle updates but do not affect aggregated data or thresholds unless involved in calculated metrics at the entity level.
- Metric Definition Data: Automatically generated aggregated data from executed metric definitions, with variance percentages showing changes compared to prior periods.
- Metric Data Tasks: Specific to manual metrics, these tasks collect responses from data owners. Metrics managers can enforce approval workflows (simple or advanced) for these tasks, ensuring data quality and governance.
Key Outcomes
- Efficient metric management across multiple entities without redundant setup.
- Flexible data collection methods supporting automation and manual input.
- Visibility into metric trends with variance analysis between current and previous periods.
- Governance over manual metric data collection via approval workflows, enhancing data integrity.
A metric consists of several components such metric definition, metric data, metric definition data, metric data tasks. All of these elements or parts contribute to the metric collection process in various ways.
Metric definitions
A metric definition is a template-level record that helps set the core properties of a metric. These properties include the unit, direction, nature, precision, frequency of data collection, and category of the metric. The metric itself collects scores, which are then aggregated into the defined metric template. The advantage of creating and using a metric definition lies in its ability to streamline the process of creating metrics using these metric definition templates. For instance, imagine you have several business units, and you must collect revenue data for each of them. Without a metric definition, you would have to create separate templates for every business unit and repeatedly specify the metric properties. However, by using a metric definition, you simplify this task. Once you have created the metric definition, you can easily attach your entities (business units in this case) and collect the metrics without duplicating efforts.
The GRC: Metrics application provides the following types of metric definitions:- Automated metric definition: Data is collected automatically.
- Manual metric definition: Data is collected manually.
- Calculated metric definition: Data is collected by aggregating data from other child metrics.
Metric data
When you execute a metric, the metric data gets created. In the case of manual metric definitions, the values of metric data are copied from the metric data tasks when the metric data tasks are closed. To address off-cycle requests for the most up-to-date information on existing metric definitions and metrics, you can create ad hoc metric data tasks on manual metrics. On the metric data form, the option Ad hoc denotes if the metric data task was created as an ad hoc task. It is important to note that these ad hoc tasks do not contribute to the aggregated metric definition data, are not considered for entity hierarchy rollup, and are not evaluated for threshold rating, Variance(%). However, in a calculated metric definition, if the Calculation level is set to Entity, and there are ad hoc tasks from the manual metric definitions, then these tasks are aggregated to derive the calculated metric definition data.
For a scripted automated metric definition, the values are updated when you execute the script. For a basic automated metric definition, the values are updated from the selected table. The field Variance (%) shows the variation in between current period and the previous period metric data. This difference is displayed in percentage. The field Last period data refers to the previous period's metric data.
Metric definition data
Metric definition data gets automatically created when the metric definition gets executed and aggregated. On the metric definition data page, the field Variance (%) shows the variation in between the current period and the previous period's metric definition data. This difference is displayed in percentage. The field Last period data refers to the previous period's metric definition data.
Metric data tasks
Metric data tasks only apply to manual metric definitions. These tasks are generated whenever manual metrics are executed and the data owners provide responses for these tasks manually. You can provide responses to multiple metric data tasks using the metric data table. For more information, see Metric data table.
A metrics manager has the authority to determine whether a metric data task needs approval. If approval is necessary, you can choose between two methods: Simple Approval or Advanced Approval by using the Metric approval property. For more information about this property, see Components installed with Operational Sustainability Management (formerly ESG Management).