Business Benefits of Clean Operational Data in Your CMDB

Clean operational data is essential to maximize your CMDB investment. The right data helps with faster root cause analysis, remediation, and automation. It also prevents having to redo your CMDB.
Many organizations that invested in ITIL enabling processes and technology have adopted the CMDB (Configuration Management Database) as the single source of truth for systems of engagement and action. The CMDB is a fundamental component to incident, problem and changes processes in diverse IT environments. ITSM platform vendors like ServiceNow have led the way in changing the role of the CMDB to align important service data to customer-facing business services and applications. However, getting your ServiceNow CMDB to an accurate and automated state can be a challenge for even the most forward-leaning IT departments. To accomplish this task at scale, you must leverage clean operational data to populate your CMDB.

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The Benefits of Clean Operational Data in Your CMDB

Operational data consists of different types of data such as CI (Configuration Item) classification and category, asset attributes, relationships, location and owner. By identifying all dependent CI’s, you can make accurate assumptions of the state of a business service and leverage automated workflows and remediation for continued service assurance. Business owners will also get the right level of visibility into the health and availability of IT services along with accurate SLA’s.
Clean operational data also creates improved ROI of the CMDB by driving efficiencies through automated actions and freeing-up human or staff cycles.

If Bad Operational Data Makes Its Way into the CMDB, It Can Have Adverse Consequences

Inaccurate, missing, or inconsistent data directly impacts the role of automation action, notification, and remediation. Service desk teams and business owners will start to question the integrity of the data, leading to the potential of longer outages or missed incidents/SLAs. Bad CMDB data can lead to longer root cause analysis or provide misleading assessments. Furthermore, if this data is leveraged by a machine learning or artificial intelligence system, it creates undesired false positives or incomplete outcomes. The result of an inaccurate CMDB is either a complete redo or added expenses to clean it up using internal or external labor.

How to Avoid the Common CMDB Pitfalls and Fill It with Clean Operational Data

It starts with automating data synchronization from a reduced number of operational platforms that focus on creating a common data model. A common data model can eliminate data silos and focus your team’s efforts on automated discovery, device identification, centralized attribute collection, and real-time relationship data.
It’s important to note that ephemeral data like elastic cloud services, containers, or short-lived VM’s challenge the normal discovery and attribute collection process. These are the key points that ScienceLogic has focused on with our ServiceNow integration over the last few years. Our joint customers have seen tremendous benefits with using ScienceLogic’s clean operational data to maximize their CMDB investment.

Learn more about the benefits of integrating ScienceLogic with ServiceNow to make your CMDB a true source of truth.

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