The Challenges of Rising MTTR — And What to Do

Data volumes are soaring. Environments are increasingly intricate. The risk of applications and systems encountering breakdowns is sky-high, and the mean time to recovery (MTTR) for production incidents is moving in the wrong direction. 

Disruptions not only jeopardize critical infrastructure but also have a direct impact on the bottom line of organizations. Swift recovery of affected services becomes paramount, as it directly correlates with business continuity and resilience.

MTTR is a pivotal metric for observability. It serves as a barometer of an organization’s ability to restore normalcy following production incidents. Indeed, the faster the turnaround time in resolving these issues, the smoother the sailing for businesses. 

In essence, MTTR emerges as a key indicator not only of observability’s effectiveness but also of an organization’s overall operational efficiency.

Despite the escalating investments in observability solutions and the proliferation of available tools, the trajectory of MTTR tells a sobering tale. The results of the 2024 Observability Pulse survey of over 500 IT professionals indicates that MTTR is getting longer for their organizations. 

This raises pertinent questions about the efficacy of existing observability systems and the strategies employed to manage operational incidents in complex environments.

The Troubling Trend of MTTR in 2024

We asked Pulse respondents a couple of questions regarding their organizational MTTR, and the results showed either minimal progress or regression for organizations.

Almost 23% said they’re making great strides in reducing their MTTR, and another 9% said they’ve greatly reduced their MTTR. However, almost 1 in 5 said their MTTR needs to improve, and a large plurality of respondents—41%—said they’re making slow progress in this area.

Most crucially, we asked respondents to describe their current MTTR during production incidents. Just under 1 in 5 (18%) said theirs is under an hour. For 44%, it’s a few hours, and one-quarter said half a day. Just over 1 in 10 said it takes more than a day to recover from production incidents. A small number, 2%, said their MTTR can be weeks or more.

Still, that means 82% of respondents are dealing with MTTR of over an hour. This continues a troubling year-over-year trend in our survey results. In 2021, 47% of respondents said their MTTR was over an hour, in 2022 it was 64%, and in 2023 it grew to 74%. 

This means that despite the growing emphasis on observability, and the many tools and processes available that are intended to help in recovery from production incidents—MTTR is heading in the wrong direction.

Why Might MTTR Be Lengthening for Organizations?

The reasons why MTTR continues to lengthen for organizations likely varies from team to team. However, some other stats from the 2024 Pulse survey offer clues about challenges in environments and how those can have an impact on MTTR.

Another major takeaway from the survey was on the question of the main challenges in gaining observability into cloud native environments. The most common response was a lack of knowledge among the team at 48%. If nearly half of teams have a knowledge gap regarding observability, it’s not hard to see a connection between that and slow MTTR from production incidents.

Additionally, total cost of ownership and large data volumes was cited by 42% of respondents as a cloud native observability challenge. The complexity of environments, and the massive amounts of data produced by the environments, could certainly be a slow MTTR culprit.

Kubernetes is another area to consider when thinking about the cause of lengthening MTTR. About 70% of organizations said they’ve either implemented Kubernetes, are starting to test it or will implement it within the next six months. For those who are running it, monitoring/troubleshooting was cited as the top challenge running Kubernetes in production at 40% of respondents. Security was next at 37%.

What Can Be Done to Reduce MTTR?

Reducing complexity could be part of the answer for reducing MTTR. Teams could look to consolidate services as part of this strategy. Nearly 3 in 10 (28%) of Pulse respondents said they’re planning to move to more of a shared model for observability and security monitoring, up from 15% in 2023.

The issues with MTTR should also inspire teams to consider working smarter, not harder. Organizations can consider technological options around automation, AI/ML and enhanced monitoring to automate processes that take manual effort for many teams today.

Logz.io’s Open 360™ observability platform is designed to help organizations reach the full potential of their critical systems and applications and reduce MTTR. With solutions like Kubernetes 360 for a holistic view of infrastructure and App 360 as a cost-effective alternative to traditional APM, Logz.io can help accelerate your cloud monitoring and gain a better foothold to recover from production incidents. 

With Open 360, organizations can leverage these features to reduce MTTR and get production environments back on track when things go wrong:

• Service Overview unifies telemetry data and insights across your infrastructure and applications into a single interface. 

• Service Map visualizes the data flow, dependencies, and critical performance metrics throughout microservices architecture for easier investigation and troubleshooting. Service Map automatically discovers and maps services and the interconnections between them – providing a single view of your entire distributed system within the context of service performance.

• If you find spiking CPU metrics or latency in your traces, you can immediately gain context around the problem by correlating across your logs, metrics, and traces to investigate the root cause of production issues through our Event Correlation capability.

• Anomaly Detection for App 360 lets users automatically monitor and alert on any issues occurring within specific services and microservices they identify as directly impacting business or SLO-related requirements.

• Alert Recommendations models actions taken by platform users and then advises subsequent users what to do when faced with similar issues through supervised machine learning.
• Logz.io’s Data Optimization Hub features make it easy to remove noisy data that obscures the critical insights needed to troubleshoot quickly. Customers can utilize Logz.io self-service tools or direct support from our Support Engineers to identify and remove noisy data.

Sign up for a free trial of Open 360 today to see how it can help your organization better tackle MTTR.