Reconciliation
You can choose several methods for starting a reconciliation job, including manual, scheduled, continuous jobs, API, or a run process workflow.
The reconciliation engine performs the following important reconciliation activities:
Activity | Description |
|---|---|
Identify | Identifies CIs that are the same entity in two or more datasets. |
Merge | Merges CI attributes from a source dataset to a production dataset to create the most comprehensive information in a single configuration item (CI). |
Reconciliation is also used for the following activities:
Activity | Description |
|---|---|
Compare | Compares instances in two datasets and produces a report. |
Copy | Copies instances from one dataset to another. |
Delete | Deletes instances from one or more datasets. |
Purge | Deletes instances that have been marked as deleted from one or more datasets. |
Execute | Executes multiple reconciliation jobs in a sequence. |
The following image represents a high-level overview of reconciliation of the same CI that is discovered by two different datasources:
Structure of a reconciliation job
The reconciliation job is a container for reconciliation activities, and each activity consists of different components. The primary activities are identification and merging. A reconciliation job can have one or more activities, each defining one or more datasets and rules for that activity. In addition, you can use a qualification set to restrict the instances participating in a reconciliation activity.
Jobs can use standard or customized rules. Standard rules use defaults for the Identify and Merge activities and automate the creation of reconciliation jobs. You can also create custom jobs that include other activities.
Identification activities to match instances
You can set an identification rule that the names of two different CIs from different datasets should be equal to Computer_1. When the rule finds a match, those instances are tagged with the same reconciliation ID. The reconciliation ID from the target dataset is copied into the source dataset.
These two CIs are considered as different instances of the same item when they have the same reconciliation ID. After CIs are recognized as different instances of the same item, they are now ready for merging based on which dataset is considered to have the most reliable information.
In another example, a rule intended to identify computer system instances might specify that the IP addresses of all be equal. When the rules find a match, it tags the matching instances with the same reconciliation identity.
You can also manually identify instances in an Identify activity.
Merge activities to merge datasets into a reconciled target dataset
Consider a merge operation involving data from three different datasets. Each attribute from each dataset is given a different precedence value based on how reliable that dataset is considered. The higher the value, the higher the priority that attribute from that dataset has over the others. Finally, the data that is added to the production dataset has the most reliable data merged from all sources. This data is the production data that other applications can access for various ITIL processes and activities.