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5 Ways ITSM Analytics Improves IT Service Delivery

Sridhar Iyengar

Over the past few years, IT service management (ITSM) has become increasingly important to an organization's IT strategy, and companies are seeking new ways to improve IT service delivery and efficiency via better ITSM processes. This increases the importance of tracking and measuring critical KPIs.

However, due to overwhelmingly large amounts of data, users find it challenging to manually access, track and analyze critical help desk information quickly. Using advanced IT analytics, managers can identify blind spots and hidden gaps in their ITSM process as well as make accurate decisions by monitoring key metrics.

Here is how advanced IT analytics can make the best of your IT service desk.

1. Minimize the impact of business downtime

Anticipate service outages by monitoring metrics like frequency of downtime and mean time to repair (MTTR). Using these metrics, build intuitive reports to identify crucial failure points and to understand the impact of an infrastructure change (such as server migration or a software upgrade). Communicate effectively by sharing these reports with your team, and formulate an action plan to handle emergency situations.

2. Optimize resource management

Using real-time dashboards, monitor periods of peak business activity and manage technician workload by measuring critical metrics, including the number of incoming requests, ticket turnaround time and technician performance. Develop an optimal staffing model to suit the increasing volume of customer demands and improve service desk efficiency.

3. Improve service quality

Although ticket resolution rate and technician performance based on closure rate are good parameters to judge the overall performance of your service desk, they aren't always enough. Sometimes, in order to show high closure rates and to prove their capability, technicians will close tickets without properly resolving them, ultimately compromising service quality. To combat this, managers can use analytical tools to derive a correlation between ticket resolution and re-opening rates to accurately determine work efficiency.

4. Maximize ROI on software purchases

A software asset management report can track software licenses (identifying over-licensed or under-licensed software), predict service request trends and measure software utilization rates to calculate unnecessary software expenditures. Teams can plan ahead for future license purchases, maintain compliance rates by conducting internal assessments and purchase software that adds value to the organization, thereby avoiding high costs or compliance risks.

5. Ensure high levels of end-user satisfaction

Maintaining SLA levels is one of the most daunting tasks for service desk teams. Any SLA violation leads to frustrated and angry customers, which causes loss of credibility and revenue for the organization. A real-time SLA dashboard can detect ticket priority and assignment and can measure service desk performance against end-user service levels. Using this information, teams can set realistic SLA goals, automate and route ticket assignments, communicate risks of SLA violations and set up escalations proactively.

The importance of analytics is quite clear when it comes to enhancing IT service delivery. Empowering users is the first step toward achieving any form of process efficiency.

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5 Ways ITSM Analytics Improves IT Service Delivery

Sridhar Iyengar

Over the past few years, IT service management (ITSM) has become increasingly important to an organization's IT strategy, and companies are seeking new ways to improve IT service delivery and efficiency via better ITSM processes. This increases the importance of tracking and measuring critical KPIs.

However, due to overwhelmingly large amounts of data, users find it challenging to manually access, track and analyze critical help desk information quickly. Using advanced IT analytics, managers can identify blind spots and hidden gaps in their ITSM process as well as make accurate decisions by monitoring key metrics.

Here is how advanced IT analytics can make the best of your IT service desk.

1. Minimize the impact of business downtime

Anticipate service outages by monitoring metrics like frequency of downtime and mean time to repair (MTTR). Using these metrics, build intuitive reports to identify crucial failure points and to understand the impact of an infrastructure change (such as server migration or a software upgrade). Communicate effectively by sharing these reports with your team, and formulate an action plan to handle emergency situations.

2. Optimize resource management

Using real-time dashboards, monitor periods of peak business activity and manage technician workload by measuring critical metrics, including the number of incoming requests, ticket turnaround time and technician performance. Develop an optimal staffing model to suit the increasing volume of customer demands and improve service desk efficiency.

3. Improve service quality

Although ticket resolution rate and technician performance based on closure rate are good parameters to judge the overall performance of your service desk, they aren't always enough. Sometimes, in order to show high closure rates and to prove their capability, technicians will close tickets without properly resolving them, ultimately compromising service quality. To combat this, managers can use analytical tools to derive a correlation between ticket resolution and re-opening rates to accurately determine work efficiency.

4. Maximize ROI on software purchases

A software asset management report can track software licenses (identifying over-licensed or under-licensed software), predict service request trends and measure software utilization rates to calculate unnecessary software expenditures. Teams can plan ahead for future license purchases, maintain compliance rates by conducting internal assessments and purchase software that adds value to the organization, thereby avoiding high costs or compliance risks.

5. Ensure high levels of end-user satisfaction

Maintaining SLA levels is one of the most daunting tasks for service desk teams. Any SLA violation leads to frustrated and angry customers, which causes loss of credibility and revenue for the organization. A real-time SLA dashboard can detect ticket priority and assignment and can measure service desk performance against end-user service levels. Using this information, teams can set realistic SLA goals, automate and route ticket assignments, communicate risks of SLA violations and set up escalations proactively.

The importance of analytics is quite clear when it comes to enhancing IT service delivery. Empowering users is the first step toward achieving any form of process efficiency.

Hot Topics

The Latest

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

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Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...