Components of GRC: Metrics
Summarize
Summary of Components of GRC: Metrics
The GRC: Metrics application enables organizations to effectively define, execute, and manage metrics through various components, ensuring streamlined data collection and reporting. Understanding these components is essential for ServiceNow customers to optimize their governance, risk, and compliance processes.
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Key Features
- Metric Definitions: Templates that set core properties such as unit, direction, nature, precision, frequency, and category of metrics. This allows for consistent metric creation across different business units without duplicating efforts.
- Types of Metric Definitions:
- Automated: Data collected automatically.
- Manual: Data collected manually through tasks.
- Calculated: Data derived from aggregating other child metrics.
- Metric Data: Generated upon executing a metric, with manual definitions allowing for ad hoc tasks to gather up-to-date information. Note that ad hoc tasks do not affect aggregated data.
- Metric Definition Data: Automatically created upon execution of the metric definition, showing variance between periods for performance tracking.
- Metric Data Tasks: Specific to manual definitions, these tasks require data owner responses and can be subject to approval processes.
Key Outcomes
By leveraging these components, ServiceNow customers can enhance data accuracy in their metrics, improve the efficiency of metric creation, and ensure effective tracking of performance through aggregated data. This structured approach to metrics facilitates better decision-making and compliance management within the organization.
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 need to 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've 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 ESG Management.