Manual discovery of all IT assets is a time-consuming, cumbersome, error-prone job. And by the time it’s completed, assets may have been changed, added or removed. A tool that automates many
discovery processes is necessary to make the task both cost-effective and comprehensive.
Many existing tools are good at discovering the existing technologies in your data center, but may be less efficient, or possibly ineffectual when it comes to matching those assets with the people and business processes they serve. To achieve this objective, the tool must automate as much of the discovery as possible, but also allow for the input, mapping and organization of information gathered from those with “tribal knowledge” of how things really work.
Tools that provide scanning for network and security purposes are common, but typically don’t show the big picture. Data collected by these tools must be correlated to provide a better view of the enterprise-wide infrastructure, including the interrelationships among the various components and people who use those components. And since these tools are usually deployed at the departmental level, it is often difficult for the discovery team to gain access, especially if they don’t know they exist in the first place. Unfortunately, turf wars are not uncommon in data center transformations.
Tools can also miss dependencies. Scans may occur at the wrong time to catch an occasional
communication flow, so it is important to let dependency mapping scans run over long periods or at
different intervals. And since an enterprise data center does not remain static during or after a
discovery project, any tool should refresh the data on an ongoing basis. Likewise, the actual
transformation doesn’t take place over night so it’s critical that operations and support teams are aware
of changes as they occur. The initial discovery of assets and relationships should dove-tail into a
thorough change management program.
Without a highly automated tool, a data center discovery process requires particular expertise and can
consume considerable time and resources. This often results in the need for external consultants to
perform the work, which can be expensive. And as with any manual/point-in-time discovery, the
information is static while the data center remains dynamic.
At the end of the day, IT organizations are held accountable to rapidly respond to and support the goals
of the organization, delivering the best return on investment as they do so. A comprehensive, automatic
discovery tool that keeps the IT inventory configuration management database (CMDB) and
relationship/dependency map up-to-date should be one of the lasting deliverables of any data center