Reducing Unknown and Blank CMDB Values via ITOM Discovery

SounderRajan
Giga Contributor

3 weeks ago

We have a requirement to improve CMDB data quality by reducing "Unknown", blank, and non-standardized values across all principal classes

I would like to understand whether ServiceNow ITOM Discovery can help address this requirement. Specifically:

  • Can Discovery populate attributes that are currently showing as "Unknown" or blank?
  • Are there any Discovery configurations, patterns, or best practices that help improve attribute completeness and accuracy?
  • Can Discovery contribute to standardizing CI attribute values, or would this typically require CMDB normalization, IRE/reconciliation rules, or other data quality mechanisms?
  • Has anyone implemented a similar solution and seen measurable improvements in CMDB data quality?
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pavani_paluri
Kilo Sage

3 weeks ago

Hi @SounderRajan ,

 

ITOM Discovery can definitely help improve CMDB data quality, but its effectiveness depends on whether the target attributes can actually be collected from the discovered devices.

 

A few points based on my experience:

  • Yes, Discovery can populate blank or "Unknown" attributes if the information is available from the source system (OS, network device, cloud provider, etc.) and the corresponding pattern or sensor is collecting it.
  • If Discovery cannot retrieve a value from the source, the attribute will remain blank or unchanged. Discovery cannot invent missing data that does not exist on the target system.
  • Regularly reviewing and updating Discovery Patterns can help increase attribute coverage, especially for custom applications or device types where OOTB patterns do not collect all required information.
  • Ensuring proper credentials, MID Server health, and pattern execution success is also critical, as incomplete discoveries often lead to missing attribute values.

 

For standardizing values, Discovery is usually only one part of the solution:

  • Discovery improves attribute completeness by collecting data directly from source systems.
  • CMDB Normalization helps standardize values such as manufacturer, model, software publisher, and product names.
  • IRE (Identification and Reconciliation Engine) and reconciliation rules help ensure the most trusted source updates attributes and prevent conflicting values from multiple data sources.
  • CMDB Health Dashboards can be used to identify classes and attributes with high percentages of blank or invalid values.

 

We've seen measurable improvements in CMDB quality when Discovery is combined with:

  1. Consistent Discovery schedules.
  2. Pattern optimization/customization where needed.
  3. CMDB Normalization.
  4. Proper IRE and reconciliation governance.
  5. Ongoing CMDB Health monitoring.

Discovery is excellent for reducing blanks and filling missing technical attributes, but for standardization and overall data quality, it should be used alongside CMDB Normalization, IRE/Reconciliation, and CMDB Health capabilities rather than as a standalone solution.

 

Mark it helpful if this helps you to understand. Accept solution if this give you the answer you're looking for
Kind Regards,
Pavani P

 

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