How to Reduce IT Downtime

Reducing IT downtime is critical to ensuring smooth business operations, minimizing productivity loss, and preventing financial damage. By adopting monitoring tools, regular maintenance, and automated incident response systems, organizations can significantly cut down on unplanned outages and recovery time. 

To improve cross-department collaboration and prevent IT system downtime, establish clear lines of responsibility and a well-defined plan that addresses the root causes of downtime. Equally important is ensuring that each team understands their specific responsibilities and how to implement solutions to address these causes effectively.  

“It’s essential to recognize that quick response to outages depends on having clear communication channels and effective collaboration between operations and security teams,” says Derek Ashmore, application transformation principal at Asperitas Consulting, in an email interview. 

Active IT practices are essential for minimizing downtime and maintaining system resilience, says Ashmore. “Automating infrastructure changes and application deployments is key to reducing human error.” 

It’s equally important to automate testing for infrastructure and application changes as much as possible. Ashmore suggests implementing real-time monitoring of telemetry data through security information and event management (SIEM) tools to actively identify issues and threats.  

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He also recommends regular incident response drills, such as chaos engineering, which introduces faults to test system resilience. 

He says post-incident root cause analysis should be conducted to address and mitigate root causes. “Change boards can help teams communicate upcoming changes transparently and identify dependencies,” he adds.  

Incident Response Plans Critical  

For reactive measures, Ashmore advises having a comprehensive incident response plan with clearly defined escalation paths. “Automating response and containment processes, such as isolating compromised systems, can significantly improve how teams handle outages or service degradation events,” he says.  

John Gordon, president and general manager of HP Managed Solutions, says he agrees that to prevent IT downtime, the first step is moving away from the traditional mindset of ‘reactive support’ where issues are addressed only once they arise. 

“With today’s advanced AI tools, telemetry, and proactive insights, we should be addressing IT proactively,” he says via email. “This means ongoing monitoring so we can prevent issues before they become widespread.” 

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With the right solutions, IT security and operations teams should be able to keep track of the health of their fleet or rely on a trusted partner who can manage it for them. 

“My advice for CIOs is to allocate resources towards preventing issues before they arise,” Gordon says.  

Metrics to Measure Results  

According to Ashmore, focusing on key success metrics helps IT teams stay efficient and minimize downtime. 

Mean time between failures (MTBF) and mean time to repair (MTTR) are critical for understanding how often things break and how fast they can be fixed. 

Incident response time is also crucial, as quicker reactions reduce the impact of outages, while system uptime is a central measure of reliability. “The higher the uptime percentage, the better,” Ashmore says. 

Finally, customer satisfaction scores can offer insight into how downtime affects users, helping teams gauge the effectiveness of their efforts. 

Gordon says another metric to measure the return on investment of reducing downtime is the number of support tickets. “If support tickets are dropping, there’s a good chance you’re reducing downtime for employees,” he says. 

Eliminating Handoffs, Investing in Prevention  

Steve Watt, CIO at Hyland, says via email that communication and eliminating handoffs is the key to operational efficiency. “Your response team must be a fused team from the beginning with a mix of security, infrastructure, technical, non-technical, and leadership,” he says. “At the same time, you need to get the size right so that the team can operate quickly and effectively.” 

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He adds there is no magic number to this, so it must be developed with the purpose over time to create a team that can operate as independently and quickly as possible. “The key here is that the team needs to be able to work autonomously,” he explains. “If they must check with many different stakeholders to act on information, then you are already losing the battle.” 

He says it’s important that response teams have clear definitions about business priorities and what should guide decision-making. 

For example, if there has been a major outage, it is more important that accounting systems or customer support ticketing are brought online first. “Understanding that priority and flow of your business is critical in a large response,” Watt says.  

From Gordon’s perspective, preventing issues from showing up proactively is a full-time job and takes investment. “We are constantly improving what we can see, what issues we can identify, and how we can automate remediations for all our clients,” he says. 

Adopting Automation, AI 

Watt explains that automation used to be more of an IFTTT model (IF This Then That), where a company had tightly defined criteria of an error condition that could trigger an automated action -- such as low disk space, low memory or a service stops responding. 

“What is coming is the ability for autonomous tools to abstract information from systems and help diagnose and triage much more complicated systems interactions that might have needed an engineer to intervene,” he says.  

In addition to automation, Ashmore predicts AI’s use in failure prediction will grow and become ubiquitous in IT. “It will expand beyond simple machine learning prediction algorithms and provide self-learning, enabling us to predict failures in situations that have yet to be seen,” he says.  

Ashmore explains that systems will also implement AI to provide self-healing through automated recovery, remediation, scaling, and intelligent workload distribution. “AI will be used for AI-driven decision support, the automated generation of incident playbooks, incident response, and root cause analysis,” he says.