Reducing MTTR and the Hidden Costs of Downtime Through AI & Automation
Of all the KPIs that gauge the health and operational fitness of an enterprise, Mean Time to Repair (MTTR) from an outage or downtime is one of the most crucial. Yet while MTTR is a universally recognized metric, many organizations still fail to consider the total cost of MTTR when deciding where and how to invest in their IT environments.
System downtime doesn’t just impact customer experience and workforce productivity in the moment; these outages also bring more substantial, long term, and often hidden costs to the organization. To alleviate these adverse impacts, businesses should leverage platforms capable of deploying artificial intelligence (AI) and automation to keep the period of incident resolution and MTTR as brief as possible.
Assessing the Business Cost of Downtime
For the most part, organizational leaders know that as soon as service starts to degrade, their operation begins to lose money; but they typically underestimate just how much money is lost. Research shows the average cost of unplanned downtime can easily reach $5,600 and $9,000 per minute. Such figures are sobering considering that MTTR for serious issues can easily stretch well past an hour.
The cascade of negative impacts from outages includes lower customer satisfaction, lost productivity, reputational damage, and morale problems among engineers struggling with excessive ticket backlogs to address incidents. Further compounding the challenge is today’s status quo: a reliance on monitoring solutions that are unable to keep up with the increasingly complex and scalable IT systems in a modern enterprise.
Even when monitoring tools are able to catch symptoms of an issue quickly, the task remains of identifying the root cause of the problem to keep it from happening again and again. That’s why the R in MTTR is so important. Across industries, MTTR has had several definitions, spanning from Mean Time to Respond (the time needed to begin work on a service ticket) to Mean Time to Repair (the time until a problem is fixed for good).
The fact is that shortening all of these time frames is a good thing. That’s why a comprehensive approach that incorporates AI and automation to address the entire incident management lifecycle is needed to ensure maximum system uptime and agility at scale.
Optimizing MTTR through AI + Automation
MTTR involves not just restoring operations but conducting the necessary work to analyze and permanently repair the underlying issues affecting performance. Key functions include monitoring all IT resources and applications that power business services; generating alerts on any changes and how they may affect particular business services; correlating information across resources to determine root cause; and providing actionable recommendations on how to permanently resolve the issue.
All these functions must be done at both the scale and speed of business. Their scope and complexity mean that, rather than settling for traditional MTTR techniques hampered by excessive manual processes, organizations must leverage AI and machine learning (ML) for a more advanced, autonomous approach to MTTR – one that enables deeper log analysis, proactive decision support, and scalable auto-remediation capabilities.
Platforms that take a rigorous approach to optimizing MTTR across the entire IT landscape and incident management lifecycle are necessary for delivering optimal business outcomes. Such platforms detect correlated anomalies and patterns in both logs and metrics, using them to automatically flag and characterize critical incidents and isolate their root causes.
The right platform can automatically ingest and analyze thousands of log streams from applications and infrastructure in real-time in order to identify and remediate both the surface issues and underlying root causes that contribute to downtime. MTTR is further shortened through the ability to tie existing incident management workflows together with automatic root cause identification, regardless of the triggering signal.
ScienceLogic’s SL1 Platform Enables Autonomic IT for Stronger MTTR
As a pioneer in automated IT operations, ScienceLogic has helped define the gold standard for AI-driven solutions and delivers that level of excellence to clients every day with its SL1 platform. SL1 enables clients to achieve stronger visibility, more comprehensive monitoring, intelligent automation and more proactive analytics and auto-remediation capabilities across even the most complex hybrid cloud environments.
With the ScienceLogic SL1 platform, enterprises and service providers of any size can conquer fragmented and siloed data challenges using turnkey service request fulfillment, Configuration Management Database (CMDB), Configuration Item (CI) lifecycle and incident management capabilities. This allows for the tracking of both on-premises and multi-cloud assets in real-time to quickly resolve tickets and reduce backlogs within a ServiceNow, Cherwell or any other service desk environment.
These are just some of the attributes that illustrate SL1’s power to reduce MTTR and deliver more overall value for the enterprise. To learn more about how ScienceLogic can help your organization realize the potential of tomorrow’s autonomous business to streamline troubleshooting and reduce the substantial costs of downtime, visit: https://sciencelogic.com/solutions/automated-troubleshooting.
