According to the State of Observability 2023, observability leaders are four times as likely to resolve instances of unplanned downtime in minutes, versus hours or days. This is notable as 76% of all respondents report that downtime can cost up to $500,000 per hour. It’s clear that a faster approach to issue resolution can drive significant cost savings.

Splunk, in collaboration with Enterprise Strategy Group, released the State of Observability 2023, an annual global research report that examines the role of observability in managing today’s increasingly complex technology environments. The report defines observability leaders as organizations with at least 24 months of experience with observability.

In addition, leaders achieved the highest rank in these five factors: the ability to correlate data across all observability tools, the adoption of AI/ML technology within their observability toolset, skills specialization in observability, the ability to cover both cloud-native and traditional application architectures and the adoption of AIOps.

Key findings from the research also include:

  • Fewer outages, disruptions to customers. Leaders experience 33% less outages per year than beginners. (On average, beginners report six outages, while leaders experience two.)
  • Greater visual clarity drives ROI. Due to observability, a little over 80% of organizations can find and fix problems faster. In addition, 81% can see into hybrid ecosystems.
  • Stronger assurance to meet reliability goals. 89% of leaders are completely confident in their ability to meet availability and performance requirements for their applications, 3.9x the rate of beginners.
  • Hybrid will persist. Organizations report maintaining 165 business applications (on average), with about half in the public cloud and half on-premises. As the number of apps grows, observability will remain vital to unify visibility across environments.
  • AIOps instrumental to CX. AIOps capabilities included in an observability practice outperform legacy solutions, by automatically determining the technical root cause of an issue (according to 34% of respondents,) to predicting problems before they turn into customer-impacting incidents (31%), to better assessing the severity of an incident (30%.)

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