Discoverable Vs. Non-Discoverable Data
In the HPE UCMDB product and user community there are two terms that help define how data originates: “discoverable” and “non-discoverable.” Discoverable data implies that the UCMDB Discovery functionality can be used to collect information through automated jobs, such as networking addresses or computer names. “Non-discoverable” implies that data cannot be obtained by said automated jobs, such as business service names, financial contract information, or responsible party.
However, this basic premise holds true only if you rely solely on automated discovery. Your business services may or may not exist in a system somewhere (or there may be several versions of your services). Your financial asset information most certainly exists in a system. It just doesn’t exist in a place that one particular tool can access without specific effort.
Most HPE UCMDB efforts stop at collecting discoverable data. Attempts are made to recreate the data in a new effort or to use wasteful federation to visualize data from a remote system on an ad-hoc basis. These tactics, though, fail to leverage the high value (and expensive to gather, but mostly free to leverage) data that your other tools already have.
Leveraging Your Data
In the Effectual PIE framework, we treat discovery as just one of many integration sources. Since all data is super valuable, we don’t differentiate or segregate the value of data based on where it comes from. All of our collection methods go through the same process of normalization, reconciliation, and auto completion that makes the UCMDB so powerful. This explanation, of course, is a simplification. There are always small customizations required to handle contradictions and to set priorities, but these are easy to handle when you know how and what to do. And we do.
You would no sooner build a business service with the input from just a single person, so why would you want to operate your enterprise with just a single source of data? Our PIE solutions allow you to bring those many expert sources together and use the best possible set of information in everything you do. We turn “big data” into useful, actionable information.