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.
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.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/09/19/how-to-leverage-aiml-for-predictive-incident-management/?sh=11d608986a0c
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.
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.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/09/19/how-to-leverage-aiml-for-predictive-incident-management/?sh=11d608986a0c
