Using Discovery Data for Change Management
August 3, 2018
- Posted by: Russell Wood
Change Management has always been one of the hardest processes to successfully implement within an IT estate. Implement it badly, and staff can see it as an unnecessary burden or even a blocker to being able to do their jobs. Confidence in data is normally low. How many times have you seen somebody with their own spreadsheet as they don’t trust the standard dataset?
Lots of companies who are looking for an easier way to streamline their change management functions look at discovery data in order to ease this problem, but does just bringing in a new solution fix everything as vendors normally claim?
If you listen to the marketing materials, it seems like the perfect solution. Never worry about asset data again as it will always be up to date, but in the real world how much of this is true?
Well in my experience some discovery data tools will only give you half the picture leaving you not much better off than you were before.
So, what do I want from my discovery data?
Firstly, being a leader in discovery across the entire estate means you can either use Agentless or Agent scanning technology to create a picture of your whole environment from servers to workstations to printers. You have a central repository of every IT asset with an IP address that you’ve scanned.
Secondly, using functionality such as Asset Visions Dependency Mapping you can understand what machines are talking with what other machines. If you do take a machine down, then you’ll know how it affects the other machines it talks to, reducing your risk and all the associated carnage that could result.
Finally, with the Scalable-developed Chaklun database running and supported by Scalable, you can be confident that your data will be correctly normalized allowing you to easily make decisions on upgrades and patches.