Last Updated Apr 09, 2020 — AI-Powered Analytics expert
AI-Powered Analytics

If you’re like most of our customers, you’re probably trying to adapt your incident management processes to respond to the current crisis. In particular, with most employees working remotely, we see an increased focus on improving their productivity while driving higher levels of efficiency and stability. At the same time, your incident responders must deal with the inevitable surge of minor incidents that such a sudden and drastic change in your organization’s operating model will bring.In these challenging times, it’s more important than ever to focus your team on resolving issues that will keep your business running. How much latent opportunity could you have for reducing minor incidents in your organization? About $2 million worth, according to a Forrester study that reviewed the benefits that clients of Numerify saw from reducing incident volume.

The Quantitative Business Case for Analytics-Driven Incident Volume Reduction

There are many common issues that most IT teams are all too familiar with when it comes to incident tickets, including incident misclassification, unnecessary escalations, and recurring issues. These can all be addressed, but only if an analytics strategy is in place to identify them and if teams are held accountable for addressing them.

With AI-powered IT Business Analytics, Numerify provides IT leaders with the visibility to identify these issues, increase accountability to organizational goals, and streamline incident, problem, and request management processes. Forrester found that Numerify clients were able to reduce incident tickets by 30% over a three-year period — resulting in about $2 million in cost savings.

“Bringing more visibility to the incident-ticket trends across the IT landscape allowed the composite use case to identify and distinguish between repeat informational requests and actual incidents, bringing better resource allocation and prevention of recurring tickets on an ongoing basis,” the report states.

The Qualitative Business Case for Analytics-Driven Incident Management

The report pointed out that IT teams realized benefits beyond the IT cost savings. While it can be hard to quantify some of them, it is important to acknowledge how these benefits improve the overall customer and employee experience. Below are some of the non-quantified benefits the study identified:

  • Improved incident, problem, and request management processes. Using the solution created greater organizational accountability for IT process adherence, and streamlined incident, problem, and request management.
  • Identification of employee training opportunities. After establishing a standard set of metrics, IT teams were able to create knowledge articles and training materials that not only put processes in place, but ensured they would continue.
  • Better support for company acquisition integration. Using analytics to anticipate and plan for the volume of new incidents and calls made to the service center helped IT teams get ahead of issues that come up when new systems are integrated.

How Did They Do It? Best Practices for Reducing Incident Volume

A transparent, data-driven culture is key to realizing the IT cost savings mentioned above. Numerify customers have managed to transform their culture with analytics by following these best practices for incident reduction:

  • Establish accountability, rewards, and ROI: To be successful, senior management must define and reward the right behaviors. This includes establishing standard metrics, assigning accountability, setting targets, and enabling teams to actually meet these targets. The key to success is making the targets and progress visible to senior leaders and their teams with up-to-date data every day.
  • Practice Strategic problem management: Ruthlessly prioritize your scarce experts on problem management areas where they will have the biggest difference from an incident volume and impact perspective. This includes using analytics to identify top Incident drivers, user hotspots, most critical applications, services & processes, and highest severity Incidents.
  • Mine your data for root cause: Use techniques like Machine Learning (ML) and Natural Language Processing (NLP) to find clusters of related incidents with a common root cause. Use these insights to eliminate common root causes or reduce Mean Time To Repair (MTTR) for new incidents as they come. These AI techniques were most effective when combining all related datasets for a 360-degree view.
  • Increase incident resolution efficiency: Eliminate unnecessary reassignments and escalations. Identify “shift left” opportunities for incidents to be resolved without escalation. Find opportunities to automate, pushing low complexity incidents to self-service.

Download the ebook to read more about the “5 ways to reduce Incident Volume with AI-driven Analytics.”

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