ManageEngine introduced Spotlight, an AI-powered contextual recommendation engine within the latest version of its flagship IT analytics product, Analytics Plus 6.0.
Spotlight is designed to identify inefficiencies in IT operations and offer corrective strategies, marking an industry first.
By analyzing factors such as IT asset age, failure rates and repair times, the tool helps optimize management and improve productivity.
One key benefit of Spotlight is its ability to enhance cybersecurity by identifying older, more vulnerable assets, enabling IT teams to address potential weaknesses before they become targets for cyberattacks.
With a unified IT metrics library, every user accessing Analytics Plus can draw from a pre-populated library of IT metrics curated by the application or by other admins.
Users can select key metrics, such as resolution time or asset age, and drag-and-drop them into their analysis without needing to recreate them.
This standardizes the KPIs, ensuring all users work with metrics calculated using consistent, unified logic, helping eliminate the risk of divergent interpretations or misaligned analyses.
The Root Cause Analysis feature identifies the primary factors causing an issue or disruption by analyzing the underlying data.
Second, it can be used to backtest and verify whether organizational decisions are positively impacting operational efficiency.
For instance, a decrease in SLA violations might be traced back to an increase in the number of technicians actively supporting the help desk.
Similarly, a reduction in repair time could be linked to more frequent maintenance cycles.
Rakesh Jayaprakash, chief analytics evangelist at ManageEngine, explained IT managers commonly monitor their IT operations using dashboards, which display a curated selection of key metrics deemed important by the user.
However, many other critical KPIs are essential for IT to operate at an optimal level, and it is unrealistic for IT managers to track every single KPI manually.
“This is where Spotlight steps in,” Jayaprakash said. “Spotlight scans daily IT operations by analyzing all relevant KPIs across every segment of IT, regardless of whether a dashboard or report has been created for them.”
When Spotlight identifies a KPI contributing to inefficiencies, it flags the issue and provides actionable recommendations for IT managers to address the problem.
Jayaprakash said the algorithm behind Spotlight is tailored specifically to IT use cases, ensuring that the recommendations are both contextual and customized to the customer’s unique environment.
“This makes the insights and solutions offered by Spotlight particularly relevant and practical for each specific IT infrastructure,” he said.
The no-code ML platform in Analytics Plus eliminates the need for programming expertise by automatically generating its own code that learns from an organization’s historical data.
For example, to build an escalation prediction model, admins can feed ITSM tickets that were escalated or overdue over the past three years, along with the factors believed to influence escalation, into Analytics Plus.
“These factors might include technician workload, time to resolution, and the help desk’s total request volume,” Jayaprakash explained.
Analytics Plus then constructs an ML model that analyzes the influence of each factor on past escalations, learning the patterns and degrees to which these parameters contributed to escalations.
Once trained, the model can be deployed to analyze active tickets, predicting the probability of future escalations.
“This process helps admins identify high-risk tickets, allowing them to prioritize resources more effectively and proactively avoid SLA violations,” Jayaprakash said.
He noted the primary focus of Analytics Plus has been to highlight inefficiencies within IT operations.
Features like Spotlight, which automatically identifies inefficiencies in IT workflows, and workflow analysis, designed to visually represent bottlenecks, are central to this goal.
“By bringing these inefficiencies to light, Analytics Plus helps organizations address them with targeted improvements, offering recommendations on how to optimize processes,” Jayaprakash said.
He acknowledged a major concern for organizations regarding AI-driven automation is data protection.
“This concern is valid, as it is common for software vendors to rely on open-source AI models to meet the increasing demand for AI-augmented analytics,” he said.
At ManageEngine, all AI technology used in Analytics Plus is developed in-house and processed exclusively within ManageEngine’s datacenters, which are equipped with both physical and virtual security controls.
“Customer data is never used to train our models or for any form of monetization, ensuring complete privacy and security for our users,” Jayaprakash said.
