Learning about reconciliation
The reconciliation process of BMC CMDB compares data from multiple data providers and aims to create a single complete and accurate production dataset. This production dataset with reliable data is also called the 'golden dataset'. This production dataset becomes a source of reference for other applications such as ITSM, for various ITIL processes and activities.
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:
- Identifies CIs that are the same entity in two or more datasets.
- 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:
- The compare activity compares instances in two datasets and either produces a report or executes a workflow based on the comparison results.
Renaming a dataset does not change the DatasetId, so all reconciliation definitions that include the dataset still work with the new name.
- The copy activity copies instances from one dataset to another.
- The delete activity deletes instances from one or more datasets.
This activity does not delete the dataset itself.
- The purge activity deletes instances that have been marked as deleted from one or more datasets.
- The execute activity executes a reconciliation job.
Reconciliation aims to eliminate duplicated data, retain only relevant information, and make sure that the data is correct and complete.
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 of which defines 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 identify and merge activities and automate the creation of reconciliation jobs. You can also create custom jobs that include different activities.
Identification activities to match instances
For example, 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.
An instance must be identified before it can be compared or merged.
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.