Defining True CMDB Data Quality
In enterprise environments consisting of 40 to 50 million records across hundreds of hybrid sources, data quality is not a buzzword—it is a strict operational requirement. True CMDB quality ensures that all Configuration Items (CIs) are validated, non-duplicated, and accurately maintained throughout their lifecycle. Reference structures must be constrained, and CI-to-CI dependencies must be mapped flawlessly. There is no ‘easy button’ for this level of accuracy; it requires treating data quality as a continuous service improvement process.
CMDB Quality is a tough term to define because it encompasses people, process and technology. We believe the core principles of current, accurate and complete data must be adhered to in order to achieve the highest probability of Service Management success. During my years in the industry working with many different customers, the mature companies realize that there’s no “easy button” for anything as important as data accuracy and they have to embrace it as a continual service improvement process.
The Trap of Over-Relying on Discovery
Many organizations treat discovery and dependency mapping as the Holy Grail of ITSM. The hard reality is that no single discovery tool covers the entire, ever-changing landscape of custom applications, ephemeral cloud workloads, and hybrid infrastructure. More importantly, discovery tools are designed to find assets, not resolve data conflicts. A trustworthy CMDB requires aggregating multiple viewpoints and reconciling the data before it ever enters the system.
Why Data Quality Must Precede CMDB Implementation
The CMDB is not a dumping ground; it should only store the validated data required to support automated configuration management. Therefore, data quality initiatives must begin before the CMDB is implemented. While it is possible to remediate data issues post-implementation, doing so drains engineering resources and delays the ROI of the entire Service Management platform. A baseline of current, accurate, and complete data is the linchpin for ITSM success.
Of course the earlier you get started, the less overall effort is required to achieve appropriate data quality levels. But it is never too late to adopt a continuous improvement initiative around data quality, because at the end of the day, the overall value of your Service Management platform is dependent on it. We always recommend beginning the data quality process before the CMDB is implemented, as this will provide the smoothest glide path to achieving a world-class data quality for your CMDB.
A CMDB that is current, accurate and complete is the linchpin for success in a Service Management deployment. Without it, all of the other processes linked to the CMDB will certainly not achieve their ultimate value. If you’ve already implemented a CMDB, don’t worry: it’s never too late to tackle your data accuracy issues. Blazent can certainly help remediate data issues after CMDB implementation, but it does shift the focus of resources and can be a more time-consuming exercise than addressing it prior to implementation.
The Five-Step Data Evolution Process
To manage over 230 distinct data sources, Blazent employs a rigid Data Evolution Process to guarantee accuracy before population:
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Atomization: Breaking down source system attributes to their most granular level.
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Identity Management: Aligning replicated attributes (serial numbers, asset tags, hostnames, IP addresses) for comparative analysis.
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Foreign Key Analysis: Validating complex dependencies and relationships that exist between isolated infrastructure systems.
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Purification: Resolving conflicts to create a single, unified ‘golden record’ based on all available data points.
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Historicity: Maintaining data from every source across time in scalable historical data lakes, providing a foundation for predictive analytics and compliance auditing.”