Taking the right steps in ITOM is not always a straight path. In our maturity model for ServiceNow ITOM, we go through the exact steps of maturity and capabilities an organization usually takes. This high resolution maturity model has been used in over 30 real-life transformations and is well tested.
During the consolidation phase the main focus is on consolidating data sources, establish CMDB governance and getting the visibility of IT. The CMDB is the beating heart of ServiceNow ITOM and should be the first priority in any ITOM journey. Assuming you are starting from scratch, reaching this step usually takes between 3-6 months. The main tool used for achieving this maturity is ServiceNow Discovery.
Having infrastructure is good, knowing what it does is better. Many organizations are “CI”-driven when it comes to how they work with IT. The responsibilities and ownership tends to be on the level of servers and devices, rather than on services. The goal with service aware IT is to be aware of the context; how is the infrastructure used.
To reach this level of maturity Service Mapping is a powerful tool and the end-goal is to connect the infrastructure to services. In other words, having automated service topologies of cloud, on-prem and microservices. That way when a user is reporting an incident or a change, engineers can instantly see what systems are affected. This step is also useful for internal billing- and cost management purposes. Expect up to 6 months of work to embed this mindset within an organization.
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Once we have the service landscape another common path to take is to become more data-aware. In other words connecting ServiceNow Event Management to display monitoring data and how it impacts services. Did you know more than 25% of enterprises are using more than 50 monitoring sources? Connecting event data is hugely important step and considered a high maturity for ITOM.
The last and final step in the ServiceNow ITOM journey is machine learning and AI. Once events, a CMDB and service landscape is in place the organization can take maximum benefits of applying machine learning to all the data. Some common use cases for machine learning includes:
- Anomaly detection
- Log analytics
- Predictive Alerts
- Suggested solutions