By Chris Riley on August 5, 2014 3 Comments Change Management, leta€™s face it, it’s usually a checklist item and a CYA tool.
Most of the time organizations look at change management as a way to spot problems AFTER they happen. Fortunately change management, the tools, can be detached from change management the governance. You can do this by attaching to the delivery process log analysis, automated load testing, automated functional testing, and configuration monitoring. By doing so, it should be clear where the weaknesses, such as errors, slow performance, and changing variables occur.
Take all the observations, and make decisions about the process, not being myopic about only the code and or a specific release, like we are used to doing. For example what if you see that while you have been releasing more features, your time to deploy has increase substantially. In the first case the results tell us we need chunk our feature releases into fewer features per release, with perhaps more frequent releases. In any case, the information tells us not that something happened, but that we need to improve something in our delivery process.A  It is not an end, it is a means. After you talk to the developer you realize, these changes are for the better, and should be kept.
Such killer features and tools are taking change management from the project checklist item, and CYA tool, into the area of being a killer weapon in understanding and improving your delivery processes. You started extremely well, by actually mentioning things which happen outside the UDV world such as ITIL and ITSM. But then, as Devops sp often do, you veer away from reality into an evangelical diatribe on how to use and apply change managment techniques to UDV’s advantage.
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But in the world of DevOps where change is part of the culture and processes.A  Change Management needs to be more than a cost center, and really a way to improve what you are doing. And in the world of DevOps, which embraces change as a way to iterative-ly improve on processes, change management is usually viewed as something to avoid, or just annoying. For example a slow regression test, or manual production release process that takes too long from the point the release is ready to when ita€™s flagged for delivery. This could be done in a tool like LogEnteries by looking at their live tail feature, and compare historical states of the application to the current one. In the second one it tells us we need take a look at the efficiency of our code, and perhaps have a dedicated performance effort. The killer feature in ScriptRock is that you can export configuration management scripts after a change. Ultimately improving the quality of applications.A  So stop thinking about change as a response, and more of a way to make things run better. They also already have the functionality which allows them to be utilized in a proactive way to improve processes. If you only look at it from a release by release point of view, all knowledge as lost; A you are really performing fast waterfall, not DevOps. Or with a perceptualdiff tool like Applitools which will show you the visual difference of the application over time, and identify steady states.
Which starts a snowball effect from regression testing, to release down time, to responses to new bugs.
Like making each component on the business logic, and view layer more compartmentalized utilizing separate threads. So imagine this, a developer un-known to you, the sys admin, makes a major change to the frameworks on integration environments. And that even since the article was published I do not have a hard and fast way for you to address change, other than to say there is no hard and fast.
When you have that then you can consider the tooling setup that allows you to audit, document, and train in a non-waterfall fashion. Allowing IT to identify why a change occurred, or as a way to evolve with the organization – as is defined in ITIL and ITSM, is almost never leveraged.

Or for example in a very similar scenario where your application gets more functionality, but the performance slows down a lot, as told to you by a load testing tool like BlazeMeter. I think that change control morphs a little, and the role of admins is to build in the mechanisms to respond not prevent. It is clear that a continuous stream of code cannot work if the processes that govern it are waterfall.
Where you setup a policy for infrastructure steady state, and get notifications when infrastructure stops matching the policy.
These changes impact infrastructure in a big way, and need to be replicated to dev, test, and prod.
Of course how you observe needs to be clearly defined, and tests and dashboards created prior to this.
And I would say the worse thing to do is to wait for an answer on how to do it versus trying to build a solution organically by starting somewhere. You can also learn a lot from reference architectures that are out there, and the amazing case studies, like what Capital One (in financial services mind you) is doing with their logging and DevOps dash boards. This, in turn, implies that some basic service support disciplines (especially Incident and Change Management) are in place and robust enough to allow this. If this not the case SLM will need to manage Customer expectation carefully until the service provider(s) can commit to formal SLAs. This should collect views at management and users levels as well as those of the service providers to create as holistic a picture as possible.
Reviews, at regular intervals, will ensure that all elements of SLM are effective and efficient.

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