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10 Ways AI Can Help Boost Enterprise Cloud Transformation Process


AI can boost Cloud transformation in many ways. The full benefits of this method are numerous and highly efficient, but compared to other methods, the AI also looks like it has a distinct advantage.

These few stages of transforming Cloud services are considered to be the 10 stages of Cloud transformation today, so they have been the basis of this development for the past 5 years and are also considered the main pain points of this process.



1. A Comprehensive Cloud Transformation Plan was Needed

Comprehensive Cloud transformation begun back in 2012 and the idea was to establish a clear Vision. The goal was to transform myLoc from a classic service provider to a Cloud service provider.

The right technology mix was the first thing that the developers needed to come up with. This process took them about a year, and in 2013 they came up with a combination of OpenStack, KVM, and CEPH.

After that they established a comprehensive plan to successfully implement myLoc to the Cloud solution. Having a comprehensive solution all the way was very important because of the nature of Cloud service.


The developers recognized this need because they needed to switch from the usual infrastructure to Cloud as soon as they could, because the old methods remained the same from 1998 up to that point.

The first thing in focus was customer service. After that they came up with the mentioned technology mix, which was one of the most important decisions made.



2. Operations were treated as a business

The IT was becoming a service, and the developers around the World became well aware of it, recognizing a shift in the market. External customers became a thing, but internal customers were also in demand for this service. Because of this wide spread of the market, a careful evaluation and selection of the right Cloud services mix had to be made for the business of information.
Cloud needed to become a business and the right types of services had to be developed for the customers. The main ideas were usually received from the customers’ surveys and stuff like feedback.



3. Shared Compelling Case for Transformation

They developers were aware that the Cloud transformation was not a short term goal. Achieving this goal they had set obviously needed years of invested time and it also depended on a lot of people. Ample support was needed from various organizations, and people had to rally and work together.
In 2012, the existing business was still running very well, and stock holders and share holders were not easily swayed to make this transformation. Why fix something that isn’t broken – was their logical response. But, people who could see further into the future needed to reassure everybody, even if this transformation was not made over night, and was not even successful at first.


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4. Comprehensive Cloud Transformation Team

Not only did the plan needed to be made comprehensive, but a comprehensive team was needed. This goal required everyone in all the joined companies to support the transformation effort. Everyone needed to switch to the new platform, which meant new methods of reaching the customers and all other separate departments. The platform also needed to answer to the demands and needs of each department perfectly.
A, team had to be formed that had a clear goal everyone identified with, and they especially needed stakeholders to be on the same boat as everyone else. The Cloud transformation could only succeed if every participant was on board.



5. Effectively Managing the Stakeholders

This was perhaps the hardest part of the entire project. Stakeholders were fundamental for the success of the transformation effort. The developers needed to present them with a clearly defined structure of the transformation effort and they also needed input from them. Their insight was not only necessary but very valuable.



6. Communicate Effectively and Purposely

This was another key part of the transformation. Regular meetings were held where all participants had to attend and have a voice on. The message had to be clear and understood by everyone. This actually strengthened the entire effort and made risks of mistakes minimal.



7.Establish a Baseline on the Technology

It is obvious that the technology used was crucial to this as well. Careful evaluation of the tech available led to the OIpenStack, KVM, and CEPH choices. The tech was chosen based on the service they required to do, with no bias prejudice.



8. Define how success is measured

Having the right services was the success baseline from the start for myLoc. The right service was identified for all the targeted customer groups, and they were thoroughly verified and validated through surveys. After having this knowledge, the success was measured by how well those services were met.



9. Using a Phased Approach

The main goal needed to be divided into stages, and the first ones were to organize Cloud storage and Cloud Backup services.
The second was offering infrastructure as a service, a public Cloud based on Helion OpenStack.
After that they needed to bring a Private Cloud Platform as a service and also additional Software by the end of 2016.



10. Managed Perceptions and Expectations

Meeting the needs of internal and external customers was the priority. MyLoc spared no effort in determining this and they ensured that all the customers understood the service before they used it. They also continue to be close to customers and feedback.


Now, after going over these main pain points, it becomes obvious how implementing AI in all this is important. Having an effective end to end solution including data collection, processing and storage methods is crucial, as well as the synchronization of every participants work and effort towards this transformation, which is not possible without an automated system that synchronizes everything perfectly.


For instance, logs monitoring is one of the most demanding processes in the entire transformation, and it is simply not possible without this kind of help. It is almost impossible to keep track of the volume of logs generated by the increasing number of services and applications spread across the physical and virtual hardware of private or hybrid cloud deployments. It is crucial to understand what is causing issues in the environment and take appropriate, effective and proactive actions to mitigate such problems. There’s no simple way that any human being can keep up with the volume, in a effective and timely manner.



Watch this 2-min video to learn how Loom AIOps solution leverages powerful AI to help organizations handle their cloud transformation issues:



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Loom Systems delivers an AIOps-powered log analytics solution, Sophie, to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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