Information technology (IT) research company Gartner forecasts in a recent study that, “by 2019, IT service desks [using] machine-learning-enhanced technologies will free up to 30% of support capacity”.
This is supported by Marval Africa executive director Edward Carbutt, who believes that the features offered by machine learning will also add a tier of intelligent automation to traditional IT service desks.
“This will aid decision-making, enhancing staff productivity and opening up a level of smarter self-service for the end-user.”
Carbutt notes that the faster networks become, the more data is consumed and generated. “Information is being accumulated at a rapid pace. This poses challenges for IT service management (ITSM) teams, who are inundated with enormous data streams, often too large to process manually.”
However, the application of machine learning to sift, sort, analyse and manage Big Data could simplify the tasks of ITSM teams.
Traditional ITSM departments are under constant pressure, dealing with what Carbutt calls the three ‘Vs’ of Big Data: volume, variety and velocity. “ITSM receives massive volumes of data . . . making effective sorting virtually impossible. The variety of data grows evermore complex, as so much is generated from so many devices and in so many ways.”
Meanwhile, the “sheer velocity” of data means that it must be processed immediately or it is lost. Additionally, the veracity – accuracy and integrity of data – and validity of data also need to be considered. He states that the capability to extract meaningful data is increasingly difficult to achieve. “This is where machine learning proves itself as invaluable.”
Carbutt notes that machine learning is an effective tool, enabling support IT staff to understand ‘patterns’ from the past and make predictions for the future from the large amounts of accumulated data.
Traditionally, ITSM made use of intelligent computing – which collects an abundance of data. Machine learning, when properly applied, automates the process of sorting through the data, identifying patterns and applying them to provide possible solutions to common issues.
“In an age where customer experience is a critical component of a business, the ability to answer requests with more accuracy, speed and precision becomes a differentiator,” adds Carbutt.
Further, the automated process should relieve pressure on ITSM departments, freeing up time for more complex requirements.
Carbutt comments: “Machine learning marries knowledge management with an organisation’s capacity to provide the right knowledge, at the right time, to the right people – enabling ITSM to operate quickly, smoothly and accurately, while providing customers with an enjoyable experience.”
He notes that the cost implications of machine learning are easily negated, when weighed against the benefits. “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.”
He stresses that, in the long run, any investment in machine learning to back up an organisation’s ITSM will result in time savings and “happy customers”.
“Ultimately, ITSM is all about the quality of service provision that enables customers – and ITSM teams – towards operate faster, better and cheaper. Applying machine learning and Big Data principles to ITSM . . . will go a long way towards helping companies to achieve this,” concludes Carbutt.