How To Leverage AI/ML For Predictive Incident Management

Innovation How To Leverage AI/ML For Predictive Incident Management Chandra Gundlapalli Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Sep 19, 2022, 06:15am EDT | Share to Facebook Share to Twitter Share to Linkedin Digital Strategy Head @ CriticalRiver | Top 100 Diverse Leaders | Cloud | AI Ops | Web3 DAO Utility | 80% Speed-To-Market | Board Member getty Digital technologies have led to the application of new-age technologies that operate with minimal human intervention.

And while they may heighten productivity and drive growth, any failure can pose a significant challenge for IT and DevOps teams to resolve. 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 place significant pressure on enterprises’ IT infrastructure, leading to system failures and outages impacting the bottom line.

According to the ITIC 2021 Hourly Cost of Downtime Survey, 44% of participants (of 1,200 global organizations) said that hourly downtime costs anywhere from $1 million to over $5 million. And 91% of organizations added that even an hour of downtime impacting mission-critical server hardware and applications averaged about $300,000 in losses. Yet another report by the Uptime Institute found that the increasing complexity of cloud environments led to system disruptions despite simultaneous innovation.

The study also indicated an upward trend in major outages, with one in five organizations reporting a “serious” or “severe” outage in the past three years. MORE FOR YOU Google Issues Warning For 2 Billion Chrome Users Forget The MacBook Pro, Apple Has Bigger Plans Google Discounts Pixel 6, Nest & Pixel Buds In Limited-Time Sale Event In most cases, indications of an impending IT incident, while prevalent, are often left ignored or unassessed for anticipated risks and unplanned system downtime. So how can organizations really enhance their incident management capability to minimize IT downtime impact? The key lies in undertaking quick, corrective measures that help identify, analyze and resolve tech disruption while reducing the impact on business.

Many organizations are turning to artificial intelligence and machine learning (AI/ML) to identify, diagnose and fix issues and proactively prevent them from reoccurring. Addressing The Data Challenge Proactive incident management essentially involves leveraging data pattern insights to understand events before they happen and take corrective measures to prevent them from occurring. In the process, it dramatically minimizes business downtime as opposed to reactive incident management, which involves addressing problems after they occur.

Reactive incident management often has a significantly higher business downtime and revenue loss. However, a big challenge for modern enterprises today is that their data and systems typically span both on-premise and cloud. They straddle both legacy and digital elements, making it almost impossible to standardize data analysis and recognize patterns related to possible IT incidents.

Some other risks and challenges include: • A high volume of ITSM tickets and a lack of expertise: IT teams have a hard time handling many open tickets with the minimal resources and expert support staff they have, eventually leading to 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, leading to high costs. • Data silos and volume: A typical IT infrastructure produces a large amount of data, such as ITSM tickets, logs, traces and alerts, which are hard to correlate for pattern analysis.

• No data logging standard: Since no logging standards are used for creating and storing logs, it becomes difficult to analyze them and gain insights. Enterprises can address this substantial gap by applying AI/ML-enabled IT operations. Through machine learning algorithms, enterprises can predict hidden behavioral patterns in the vast amount of data across all platforms and harness AI-enabled IT operations to detect any abnormalities in system behavior before it impacts services.

Proactive risk mitigation is a critical aspect that must be embedded into enterprise tech strategy to ensure disruption-free operations. By harnessing AI/ML-led incident management solutions, DevOps teams can improve processes by: • Quickly identifying and monitoring applications at risk • Ensuring greater resilience in their DevOps processes through CI/CD • Applying analytics to streamline data challenges • Identifying potential hot spots and resolving 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 the impact, if not eliminate it altogether. Benefits include faster time to incident resolution, increased data fidelity and significantly improved ITSM maturity.

In addition, by identifying the potential problems during the initial stages of a change request, post-deployment incidents are substantially reduced, leading to improved cost savings and, eventually, an elevated customer experience with always-on platforms through actionable insights from siloed data. But how can businesses accelerate solving petabyte-scale predictive data incident management challenges? Here are some methods enterprises should apply. 1.

Incident data cleansing: Remove data duplication and sensitive personally identifiable information (PII) data. 2. Data grouping: Once the incident data is processed, it is vital to group it based on similar text or intents.

3. Problem identification: Applying the AI-based algorithms and grouping of incidents, you can easily use analytics to find the root cause and required time to address the issue or apply this data to new change requests to predict the possible incidents. 4.

Drilled down, actionable dashboards: Insightful, actionable and customizable dashboards are necessary for making business decisions. Focus on the above when developing your AI-led incident management plan. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives.

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