Best Practices for handling Normalization
To view the best practices video for Normalization, see .
Before you begin
- Ensure that you have completed the initial data load,
- Ensure that the CIs are residing in the respective datasets.
Normalization helps you clean your data and populate the impact information. The Normalization engine allows you to run validations and integrity check rules on your CIs and makes them more consistent. For example, you can set Normalization rules to specify that all occurrences of Microsoft Word, MS Word, Word should be normalized to MS Word thus resulting in data consistency.
Ensure that you run Normalization to clean the data, default missing data (if you do not provide a value for data, Normalization assigns a default value to it), and add impact information for every CI.
Impact is an attribute available as one of the Normalization features, this attribute should be populated. For example, if there is a relationship between Computer System and a software running on it set the impact to 100% when the Computer System goes down the Software goes down. To add impact information see, Configuring impact normalization rules. This impact information helps you to maintain your service model as described in Service Model topic.
The biggest value derived out of the Normalization process is to default the impact information. If you know the impact of CIs, feed it into Normalization.
It is a myth that Normalization in an inline mode is generally considered detrimental to performance. It does take the loader longer to run, but then you do not have a stack of CIs waiting in a batch job to run.
The Atrium CMDB normalizes CI as they arrive. If you are manually creating CIs and have Normalization in inline mode, it does slow the performance but not to an extent that you will notice it.The overall performance is the same, it is just a matter of where you spend time whether to perform inline Normalization or to run a batch job for multiple CIs.
If you are performing real time normalization, best practice is to run reconciliation in real time for which you should use the continuous mode, see Creating a continuous reconciliation job to create a continuous reconciliation job.
When you are performing an initial load of CIs, you should not use inline Normalization mode at dataset level. You should run a batch job. After the initial data load is complete, you can turn to inline Normalization for the selected Dataset.
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