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Leveraging AI/ML For Predictive Incident Management

Digital technology has led to the use of new age technology that works with minimal human intervention. While they can increase productivity and drive growth, any failure can pose a serious challenge to IT and DevOps teams. Incidents or service outages are an IT manager’s worst nightmare. All too often, factors such as cybersecurity breaches, human error, and the accelerated pace of innovation put tremendous pressure on a company’s IT infrastructure, resulting in system failures and downtime that impact profits.

According to ITIC’s 2021 Cost of Downtime Hour Survey, 44% of participants (1,200 global organizations) reported downtime costs between $1 million and $5 million per hour. 91% of organizations added that even one hour of downtime affecting mission-critical server hardware and applications costs an average of approximately $300,000. Another report by the Uptime Institute found that despite simultaneous innovations, the increasing complexity of the cloud environment has led to system downtime. The survey also found that the number of major outages is on the rise, with one in five organizations reporting a “serious” or “critical” outage in the past three years.

In most cases, the signs of an impending IT incident, while common, are often overlooked or underestimated due to anticipated risks and unplanned downtime.

So how can organizations really improve their incident management capabilities to reduce the impact of IT downtime? The key is to take corrective action quickly to help identify, analyze and resolve technology disruptions while minimizing business impact. Many organizations are turning to artificial intelligence and machine learning (AI/ML) to identify, diagnose and solve problems and proactively prevent them from reoccurring.

Solving data problems

Proactive incident management essentially involves using data model insights to understand incidents before they happen and take corrective action to prevent them. In the process, it significantly reduces business downtime, unlike reactive incident management, which involves solving problems as they arise. Reactive incident management often significantly increases business downtime and lost revenue.

The biggest challenge facing modern businesses today is that their data and systems often span both on-premises and in the cloud. They span legacy and digital elements, making it nearly impossible to standardize data analysis and identify patterns associated with potential IT incidents.

Some other risks and challenges include:

  • High volume of ITSM tickets and lack of knowledge: IT teams struggle to manage many open tickets with minimal resources and expert support staff, resulting in delayed resolution and poor customer experience.
  • Multiple monitoring tools and platforms: Using multiple monitoring tools requires a lot of time and ongoing effort for operations teams, resulting in high costs.
  • Data reservoirs and volumes of data: A typical IT infrastructure generates large volumes of data such as ITSM tickets, logs, traces and alerts that are difficult to correlate for pattern analysis.
  • No data logging standards: Because there are no logging standards for creating and storing logs, it is difficult to analyze them and gain insight.

Companies can bridge this huge gap with AI/ML-enabled IT operations. Using machine learning algorithms, companies can predict patterns of behavior hidden in vast amounts of data across all platforms and use AI-enabled IT operations to detect any anomalies before system activity impacts service.

Proactive risk mitigation is a critical aspect of a company’s technology strategy to ensure business continuity. With an AI/ML-driven incident management solution, DevOps teams can improve processes by doing the following:

• Quickly identify and monitor risky applications

• Bringing greater resilience to their DevOps processes through CI/CD

• Apply analytics to simplify data problems

• Identify potential hot spots and address them before they escalate

Navigate AI/ML Predictive Incident Management

Although IT incidents can happen suddenly, a structured proactive strategy can help minimize, if not eliminate, the impact. Benefits include faster incident resolution, improved data accuracy and significantly improved ITSM maturity. Additionally, by identifying potential issues in the early stages of change requests, post implementation incidents are significantly reduced, resulting in improved cost savings and ultimately improved customer experience on the always-on platform through actionable insights from collected data.

But how can companies accelerate the solution to petabyte-scale predictive data incident management challenges? Here are some approaches companies should take.

  1. Data Cleanup: Deduplication of data and sensitive personally identifiable information (PII) data.

2. Grouping of data: Once the event data is processed, it is important to group them  based on similar text or intent.

3. Identifying the problem: Using artificial intelligence-based algorithms and event grouping, you can easily use analytics to find the cause and timing of a problem to fix a problem, or use this data for new change requests to predict potential events.

4. In-depth, actionable dashboards: Business decisions require thoughtful, actionable and customizable dashboards.

Keep the above in mind when developing an AI-driven incident management plan.

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