Performance Analytics concepts
Summarize
Summary of Performance Analytics concepts
Performance Analytics in ServiceNow provides core functionality to measure, analyze, and forecast business performance using key indicators and data over time. It enables organizations to track performance metrics (KPIs) regularly, visualize data, and apply breakdowns to gain detailed insights. This foundational capability supports other applications like Benchmarks but focuses here on the essential components within Performance Analytics itself.
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Key Features
- Indicators (KPIs): Define performance measurements collected at regular intervals from business services, activities, or organizational behaviors. Indicators combine data sets with aggregation methods (e.g., count) and can be generated automatically, manually, or calculated from other indicators. Indicators are viewed in Analytics Hub, dashboards, or KPI Details.
- Breakdowns and Elements: Allow grouping or filtering of indicator scores by qualitative attributes such as Priority or Assignment Group. Breakdowns can be automated, manual, or external, with elements representing specific values (e.g., Critical, High, Low). You can apply up to two levels of breakdown for detailed analysis.
- Data Collector: The engine for taking periodic snapshots of process tables and storing scores. Data collector jobs are scheduled to align with indicator source frequency and can run automatically or manually for historical data collection.
- Aggregation Functions: Enable summarizing indicator scores over time, such as applying a 3-month sum. Aggregations can be configured in indicators or dynamically applied in Analytics Hub or widgets as time series.
- Breakdown Mappings and Sources: Define how breakdowns relate to indicator sources, either via field mappings or scripted queries. Breakdown sources specify the unique elements included, potentially limiting values to relevant groups or recategorizing data using bucket groups for customized categorization.
- Snapshots: Collections of record lists taken at data collection times, primarily for automated indicators with record collection enabled. Snapshots enable detailed record-level analysis and are available for indicators and first-level breakdowns.
- Day Definition: A day is consistently defined as a 24-hour period, without using business day concepts.
- Indicator Sources: Filtered data sets from tables or database views specifying conditions and collection frequency. Multiple indicators can share a source to ensure consistent data points.
- Scripted Breakdowns: Use scripts to query indicator sources dynamically for customized breakdown mappings.
Key Outcomes
- Enables systematic performance measurement across business processes using reliable, scheduled data collection.
- Supports detailed analysis through thematic grouping (indicator groups), breakdowns, and multi-level filtering.
- Facilitates trend forecasting and decision-making by aggregating indicator scores over time and applying statistical functions.
- Integrates smoothly with ServiceNow’s Platform Analytics for visualization and dashboard reporting.
- Allows customization and flexibility via scripted breakdowns and bucket groups to tailor metrics to organizational needs.
- Provides easy access to underlying data snapshots for in-depth record-level insights.
- Includes a complimentary limited version for Incident Management, with full functionality available through subscription.
Performance Analytics uses terms and concepts that can differ from industry norms due to the unique nature of the ServiceNow platform.
Performance Analytics includes the following concepts and components:
Key components
- Indicators
-
(KPIs) define a performance measurement taken at regular intervals of a business service, an activity, or organizational behavior. These performance measurements result in a series of
indicator scores over time.
Businesses track these scores to measure current conditions and to forecast trends.
Technically, an indicator combines a data set and a data aggregate, such as Count, along with optional conditions.
Key characteristics of indicators include:- Indicator scores can be generated automatically from a set of records defined in an indicator source, entered manually, or calculated from other indicators.
- Indicator scores can be viewed or analyzed in Platform Analytics data visualizations and KPI Details. In the Core UI, view them in the Analytics Hub or in widgets on dashboards.
For convenience, you can organize indicators thematically into an indicator group.
Synonyms: Metrics, business metrics, KPIs
- Breakdowns and elements
- enable you to group or filter indicator scores by a qualitative attribute such as Priority, Category, or Assignment Group. You can apply a breakdown on the Analytics Hub, in KPI Details, and on dashboards.
For example, you can look at the Number of Open Changes by Assignment Group. Or you can see the Number of New Changes by Priority.
The values for each breakdown are called breakdown elements. For example, the Priority breakdown may have the elements Critical, High, and Low. Breakdowns are categorized as automated, manual, or external, depending on where these elements come from. Automated breakdown elements are specified in breakdown sources. Manual breakdowns have their elements entered manually to define an organization. Lastly, an external breakdown specifies the JDBC data source and SQL statement for retrieving breakdown elements.
You cannot apply more than two levels of breakdown to an indicator.
- Data collector
- is the engine that takes periodic snapshots of your process tables and stores them in the Scores and Snapshots tables. You can set up data collector jobs to run automatically according to a schedule. Usually set a job schedule to match the frequency in the indicator source. One job usually generates scores for multiple indicators that use the same indicator source. You can also set up jobs that run manually, such as historical jobs, which you run only when collecting data for a new indicator.
Other concepts
- Aggregate/Aggregation
- can refer to either of the following functions:
- The Performance Analytics function of aggregating, or collecting, indicator scores over time. The indicator configuration includes the frequency with which indicator scores are collected.
- Statistical functions applied to collected indicator scores over a time period. For example, you can apply a 3-month SUM to indicator scores. Aggregation functions can be added either in the indicator form or later in the the Analytics Hub or widget. Aggregation functions in the Analytics Hub or widget are named time series.
- Breakdown mappings
- specify the relationships, or 'map,' breakdowns to indicator sources. A breakdown mapping either specifies a field on the indicator source or specifies a script that queries the indicator source. The latter is sometimes called a scripted breakdown mapping, and a breakdown with such a mapping is called a scripted breakdown.
- Breakdown sources
- specify which unique values, called breakdown elements, a breakdown contains. A breakdown source is defined as a set of records from a table or database view or as a bucket group. Multiple breakdowns can use the same breakdown source. For example, instead of seeing ALL assignment groups for the Number of Open Changes indicator, you can limit the element list to just those groups that are part of the change process by configuring the Breakdown Source.
- Bucket groups
- are used to recategorize data so it can be used as a
breakdown, for example by grouping a range of values into discrete buckets.
To work with a bucket group, create a breakdown source that uses Bucket [pa_buckets] as the facts table and specifies the bucket group in a condition. If a breakdown built on this source uses a breakdown mapping with a script, the breakdown groups the values that the script returns into buckets. If the breakdown mapping specifies a field instead of using a script, the breakdown groups the values of the mapped field into buckets.
In the data architecture, bucket groups are defined in Bucket Group [pa_bucket_groups] records and buckets in Bucket [pa_buckets] records. Each Bucket [pa_buckets] record contains a Bucket Group field that is a reference to a Bucket Group [pa_bucket_groups] record.
- Day
- A day in Performance Analytics is always defined as 24 hours. Performance Analytics does not use the concept of 'business days.'
- Indicator sources
-
are data sets
consisting of filtered records from one table or database view.
An indicator source configuration specifies a
table, such as Incident [incident], conditions for filtering records from that
table, and a frequency that you base on the conditions. An indicator source cannot
specify a rotated table. Multiple indicators can use the same indicator source. Data
collection jobs query the database once for each indicator source. Thus, all
indicators that use the same indicator source get data from the same point in time.
Typically, an indicator tracks the situation on a certain date. The indicator source conditions usually include a date-related filter, such as [Opened][on][Today]. Indicators collected less frequently might specify a larger date range, such as [Closed][on][This month].
- Scripted breakdown
- is a breakdown that uses a script to query the indicator source as its breakdown mapping.
- Snapshots
- are the lists of records (sys_ids) that are collected at the time that the scores for those records are collected. A snapshot is made only for automated indicators with Collect
records selected.
The snapshot/list of records can be retrieved in the Analytics Hub or KPI Details.
Snapshots are kept for the main indicator and for first-level breakdowns. Second-level breakdown snapshots are derived as an intersection of the two first-level breakdown snapshot lists.