Continuing in our Performance Analytics build, we’re now talking about job collectors.
A Performance Analytics Job Collector is the scheduled process that collects and stores indicator data points for historical trend analysis. It’s what allows you to look back and see how KPIs (like incident volume, SLA compliance, or backlog) have changed over time. Job Collectors will also gather Breakdown data as well. Word of caution: excessive data or too many breakdowns might cause issues with Job Collectors. They will error out, and the data will not be collected. When you first setup them up, they may run fine, with no issues. My recommendation is to monitor them for issues over time.
Job Collectors can be configured in different ways:
- At scheduled intervals (e.g., daily, hourly, weekly), the collector runs.
- Each time it executes, the job queries the source table (for example, Incidents, Requests, or Changes) defined in the indicator source. It then processes that data to calculate the values for each associated indicator, such as the number of open incidents, the average resolution time, or total requests completed.
- Once those calculations are complete, the results are written into the pa_scores table, where they are stored as timestamped records. These stored data points become the foundation for historical reporting and trend analysis, allowing dashboards and scorecards to show how performance changes over time.
The examples below show two types of Performance Analytics data collection jobs, PA Incident Daily and PA Incident Historic.
The Daily job is scheduled to run automatically each day at 4:00 a.m., capturing new data points for ongoing trend analysis.
In contrast, the Historic job is configured to run on demand, not on a daily schedule. This is because historical data only needs to be collected once to backfill past records, once captured, that history remains constant and does not need to be recalculated.
In the next step we will talk about Indicators.
