A Gartner study reads, “By 2019, IT service desks utilizing machine learning enhanced technologies will free up to 30% of support capacity.”
A tier of intelligent automation will be added to the contemporary IT services in the form of Machine Learning. This will help in decision making, the productivity of staff, enhancing staff and thus providing better service to the end users.
In today’s digital world the faster the network, the more is the data consumed. The constant evolution of technology and, the information is passed around at an amazing speed, and not only that it is getting accumulated at some point. So this is quite a challenge for the IT admin team, who has to keep things under control when such huge data zips past every hour/day. This is where ITSM comes into the light where it defines to simply the entire task.
Traditional ITSM departments are under constant pressure, dealing with the three “Vs” of Big Data, Volume, Variety, and Velocity.
The 3 “Vs” of Big Data like Volume, Variety, and Velocity is the core factor and this is what bothers the ITSM department of the organization. They are under constant pressure to process it immediately before it is lost.
In every application or process, the importance remains in the fact that how fast a data reaches its desired outcome. It gives relevance to the work altogether with Veracity and Validity. Though we may have to ensure the accuracy it is also imperative to consider how latest the information is, because Machine Learning is all about dealing with the latest.
For instance, think if a service provider had all the required information of the customer in real-time, and simultaneously the solution is spread out at a click of the mouse, don’t you think it is an effective method. This is the power of machine-learning where it provides real-time solution in helping customers.
Machine Learning enables support IT staff to understand ‘patterns’ from the past. It is an effective tool that makes predictions for the future from the large amounts of accumulated data. Traditionally, IT service management collects an abundance of data and makes use of intelligent computing. Machine Learning, automates the process of sorting through the data when properly applied and provide possible solutions to common issues.
In an age where you should have the ability to answer requests with more accuracy is a critical component of a business, the speed and precision becomes a differentiator.
Machine Learning- The automated process enables a ‘self-service’ functionality that customers are able to resolve and leverage on common issues. It relieves some of the pressure that ITSM departments feel – freeing up their time to focus on the more complex requirements.
By helping to support ITSM staff in real-time predictive analytics, and automation further adds to the basket of potential in their engagements with customers.
In an organization, Machine Learning is associated with knowledge management that provides the right knowledge, at the right time, to the right people – enabling ITSM to operate smoothly, quickly, and accurately, while providing customers with an enjoyable experience.
When it comes to the numbers, the cost implications of Machine Learning when weighed against the benefits are easily negated. Faster service provision with fewer errors means fewer returns to fix the same problems, and the ability to simultaneously handle multiple queries with more accuracy. In the long run, any investment in Machine Learning to back up your ITSM will quickly be made up in gaining time and happy customers.
When you calculate the cost implication of applying Machine Learning vis-a-vis benefits, it can be ignored. Imagine a faster service with no errors, and the ability to multi-task simultaneously and that too with accuracy is worth the effort.
Ultimately, ITSM is all about the quality of service that enables customers –to operate faster, better and cheaper. Applying Machine Learning and Big Data to ITSM, with the backing of ITIL frameworks, will go a long way to help companies to achieve their objective.