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Enterprise complexity continues to grow rapidly and defy imaginable scales; a typical infrastructure setup spans multiple geographies involving 1000s of servers with strong coupling of network, storage, security and application services.
In addition to this, consider virtual machines competing for physical resources, necessitating a new level of capacity planning. Along with the need to support a massive, connected infrastructure, there is also an increased demand for a better customer satisfaction and seamless user experience.
The ITSM world is evolving from a traditional service model which comprises of a queue of tickets handled by a finite number of technicians, who are in a constant state of flux due to the unmanageable velocity of tickets coming in. In the context of new-age requirements and changes, the service delivery model has been forced to transcend its boundaries to support the need for extensive and innovative automation and limited reliance on manual intervention.
GAVel is the Big Data platform used by GAVS to provide superior service levels to users at decreased costs. At its core, GAVel embeds operational intelligence by answering three key questions:
- How are my IT operations performing?
- Where are the problems in IT operations?
- Can IT operations be safeguarded based on past performance?
To tackle these questions, GAVel leverages predictive analytics and addresses use cases to enhance the quality of service delivery. For e.g. the value added predictive analytics tool has extended the scope of service intelligence from the conventional approach of monitoring tools based on thresholds towards an advanced approach of predicting tickets, future issues and system failures. This enables the development of better contingency plans and automated trigger point based customized solutions. This, in turn, has led to significant benefits such as better capacity planning due to reduced infrastructure downtime, lower number of critical incidents, and increased customer satisfaction.
GAVel also identifies the best technicians to work on a specific issue based on expertise, availability and other factors, thereby reducing SLAs. The system not only improves ticket response time, but also identifies potential problems and provides the opportunity to pre-empt incidents before they occur. This pre-emptive elimination of incidents through the application of statistical models on past incidents and the ability to deploy corrective actions (automated and manual) ahead of time changes the service delivery landscape. The prescriptive layer in the platform also identifies additional event occurrences that can result from an existing niggling issue.
While efficiency, productivity and faster turn-around times are the technical indicators of GAVel’s success, there are also other soft factors that help provide a holistic and positive experience for the user. For e.g., the overall experience is enriched with the help of a web-based dashboard that provides users accessible data points and increased transparency. The user is clear about which technician is working on the ticket, how much time the ticket will take to resolve, and the other systemic impacts likely to occur due to an issue. The user also receives customized self-help through a chat-bot (GAVelBOT), an effective channel for interactive resolution of queries. Delegating some of the control to the user helps in reducing ticket traffic for standard and oft-repeated queries, reflecting in improved CSAT scores.
In light of the digital revolution being propelled by Big Data, cloud and mobile, a system such as GAVel transforms the rules of service delivery. It eliminates challenges with resourcing issues, handles throughput problems with timely ticket resolutions, and predicts future failure events. This helps minimize risks in the overall architecture, while an unambiguous user interface offers the user a rich, social and automated experience.
Organizations are becoming increasingly digital and Artificial Intelligence is being deployed in many of them. Often small tech-savvy start-ups and large firms with huge funds, like those in technology and finance businesses, are deploying sophisticated forms of AI.