The use of BigData and predictive analysis has swept the world of ITSM off its feet. The goal of analytics today is: predict, search and optimize. Artificial Intelligence combined with machine learning and automation has made business easy, fast and reduced the risks involved.
The modern complex, heterogeneous domain of infrastructure and organizations lack a single window to collect and analyze IT service management data. Arranging the contrasting tools to a single location in the cloud helps create domain-specific dashboards, ensuring quicker and higher productivity.
The biggest challenge for ITSM is managing the diverse data together and making it easily accessible to different IT teams for organizations to handle global launches within shorter release timelines.
Big Data refers to exponentially growing volumes of data that overburden businesses daily. In 2017, experts estimated that by 2020, every second for every person on earth would create 1.7 megabytes of data.
Big Data and machine learning tools make the job easier by intelligently extracting insights and making predictions from the enormous amounts of data collected. Predictive analysis is leading the world forward today.
Analytics or predictive analysis uses AI, automation, data mining and machine learning to provide accurate predictions for companies for future ventures and create custom solutions for customers. For example, an organization could automate a portion of the work in the service desk and the Network Operations Center (NOC), reducing operations costs. This approach applies to IT Ops, ITSM including others.
“The chart above shows the different analytical aspects and tools that help in creating an integrated and easy overview of any subject. The time saved enables operational personnel to investigate real-life and more complex problems instead of focusing on simple events.”
Machine learning helps ITSM manage the huge amounts of data and understand data patterns better in the forms of:
a) Predictive Analysis: Predicting results of future ventures, issues associated with any change and providing insights into the future of customer satisfaction and needs.
b) Providing Recommendations: Service desk agents or end-users can get recommendations according to their requirements, making providing services faster and better.
c) Predictive Maintenance: Machine learning can provide insights into prevention failures by predicting exactly the areas that might need maintenance.
d) Autoresponders: It is possible to solve some issues and complete tasks without any human involvement with high accuracy, saving both time and energy.
e) Managing Demands: Machine learning and analytics can efficiently predict the future needs of customers and demands for IT solutions, helping to understand the capacity, stock levels and so on.
The world of ITSM is constantly evolving. The future of ITSM lies in automation, AI, low-code platforms and the adoption of ESM (Enterprise Service Management) to meet customer needs and help businesses function efficiently in a highly competitive world. In this modern era of rapidly changing technologies, implementing ITSM is more crucial than ever.
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