How to use AI/ML for predictive incident management

Head of Digital Strategy @ CriticalRiver | Top 100 Diverse Executives | cloud | AI Operations | Web3 DAO Utility | 80% faster time to market | board member

Digital technologies have led to the application of new age technologies that work with minimal human intervention. And while they can increase productivity and drive growth, each mistake can pose a significant challenge for IT and DevOps teams.

An incident or service disruption is an IT manager’s worst nightmare. Very often, factors such as cybersecurity breaches, human error, and the accelerated pace of innovation put significant pressure on enterprise IT infrastructure, resulting in system failures and breakdowns that affect the bottom line.

According to the ITIC 2021 Hourly Cost of Downtime Survey, 44% of respondents (from 1,200 global organizations) reported that hourly downtime costs between $1 million and over $5 million. And 91% of organizations added that even one hour of downtime impacting mission-critical server hardware and applications results in approximately $300,000 in losses on average.

Another report from the Uptime Institute found that the increasing complexity of cloud environments, despite simultaneous innovation, led to system disruptions. The study also showed an upward trend in major outages, with one in five organizations reporting a “serious” or “severe” outage in the past three years.

In most cases, indications of an impending IT incident, although widespread, are often ignored or undervalued due to anticipated risks and unplanned system downtime.

So how can organizations really improve their incident management capabilities to minimize the impact of IT downtime?

The key is to take quick corrective actions that help identify, analyze, and resolve technical failures while reducing business impact. Many organizations are turning to artificial intelligence and machine learning (AI/ML) to identify, diagnose, and fix problems and proactively prevent their recurrence.

Meeting the data challenge

Proactive incident management essentially involves using insights from data patterns to understand events before they happen and taking corrective actions to prevent them from occurring. In contrast to reactive incident management, where problems must be addressed, operational downtime is drastically minimized after you appear. Reactive incident management often results in significantly increased downtime and lost revenue.

However, a major challenge for modern businesses today is that their data and systems typically span both on-premises and cloud. They span both legacy and digital elements, making it almost impossible to standardize data analysis and identify patterns related to potential IT incidents.

Some other risks and challenges include:

• A high volume of ITSM tickets and a lack of expertise: IT teams struggle to handle many open tickets with the minimal resources and knowledgeable support staff available to them, eventually resulting in delayed resolution and poor customer experience.

Multiple Monitoring Tools and Platforms: Multiple monitoring tools used by operations teams require a lot of time and sustained effort, resulting in high costs.

• Data silos and volumes: A typical IT infrastructure produces a large amount of data, e.g. B. ITSM tickets, logs, traces and alerts that are difficult to correlate for pattern analysis.

• No data recording standard: Since no logging standards are used to create and store logs, it becomes difficult to analyze them and gain insights.

Enterprises can bridge this significant gap by applying AI/ML-enabled IT operations. Machine learning algorithms enable organizations to predict hidden behavioral patterns in the vast amount of data across all platforms and leverage AI-powered IT operations to detect anomalies in system behavior before they impact services.

Proactive risk mitigation is a critical aspect that needs to be embedded in the organization’s technology strategy to ensure business continuity. Leveraging AI/ML-led incident management solutions, DevOps teams can improve processes by:

Quickly identify and monitor compromised applications

Ensuring greater resiliency in their DevOps processes through CI/CD

Apply analytics to streamline data challenges

Identify potential hotspots and resolve them before they escalate

Navigating predictive AI/ML incident management

While IT incidents can come out of nowhere, a structured, proactive strategy can help minimize, if not eliminate, the impact. Benefits include reduced time to incident resolution, higher data fidelity and significantly improved ITSM maturity.

Additionally, by identifying potential issues in the early stages of a change request, post-deployment incidents are significantly reduced, leading to improved cost savings and ultimately improved customer experiences with always-on platforms through actionable insights from siled data.

But how can organizations accelerate their ability to address petabyte-scale predictive data incident management challenges? Here are some methods companies should use.

1. Cleanup of incident data: Remove duplicate data and sensitive personally identifiable information (PII).

2. Data grouping: Once the incident data is processed, it is important to group it based on similar text or intent.

3. Problem identification: By applying the AI-based algorithms and incident grouping, you can use analytics to easily find the root cause and the time required to resolve the issue, or apply this data to new change requests to predict the possible incidents.

4. Breakdown, Actionable Dashboards: Insightful, actionable, and customizable dashboards are necessary to make business decisions.

Focus on the above points as you develop your AI-led incident management plan.

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