Is Monitoring Burning Out Your DevOps Teams?

Monitoring activities consume more of your team’s time than any other daily responsibility and could lead to burnout.

business-burnout

Do you know that monitoring activities consume more of your team’s time than any other daily responsibility? Our recently released Moogsoft State of Availability Report would tend to indicate that you may not. The research reveals that managers incorrectly believe teams spend equal time across all of their activities, giving as much attention to investing in the future as they do to keeping their systems alive.

This disconnect between management and teams poses some significant problems. For a start, teams are not experimenting and innovating nearly as much as leaders thought they were.

But there might be an even more pernicious issue lurking: burnout.

The Problem: Outdated Monitoring Strategies Cause Stress and Toil

Organizations have largely prioritized data acquisition over data analysis and actionability. Consequently, teams have adopted a tremendous number of monitoring tools. We learned from our research no less than an average of 16 monitoring tools (and it is not unusual for this to be as many as 40).

Unfortunately, all of these applications suck up precious time teams can ill afford to give up. Just managing and maintaining these proliferating solutions demand a significant amount of time and energy.

In addition to demanding considerable resources, siloed monitoring tools also slow the response. More often than not, system-wide issues are connected, but siloed tools cannot provide a line of sight across the IT ecosystem. This lack of cohesion creates inefficiencies, multiplying the number of places incidents occur, orphaning knowledge, slowing down communication and, ultimately, increasing downtime.

Such inefficient and ineffective monitoring strategies keep DevOps teams from working on proactive, rewarding initiatives. Consumed by toil and unplanned work, teams have little time left for activities like advancing automation, paying down technical debt or adopting DevOps practices.

Teams struggling to ensure availability also suffer. In fact, the fall-out from subpar customer experiences — negative reviews, leadership pressure and a decreased Net Promoter Score (NPS) — are often indicative of a poor employee experience.

To improve the employee experience and stave off burnout, leaders must modernize their monitoring strategies and find time in their team’s days for future-driven work. This process also injects innovation into organizations and ensures their longevity.

The Answer: Invest in Tech Stability

Organizations must prioritize tech stability to ensure the sustainability and scalability of their teams and infrastructures. Here are the six steps to transition IT teams from simply maintaining availability to building stability.

1Measure your current state.

Gather stakeholders to determine business goals as they relate to availability and identify which apps, services and infrastructure are critical to the success of the business. Then, analyze your tech assets by cost and value.

2Set meaningful KPIs.

Standardize KPIs that give leaders visibility into how much time teams spend on activities like unplanned work, feature improvements and tech debt. With an accurate understanding of team activities, management can make necessary adjustments, like reducing unplanned work. Also, set KPIs tracking mean time to detect (MTTD) and mean time to remediate (MTTR) to accurately measure the customer experience. Then, actively work to reduce these metrics.

3Reduce the tool footprint.

Invest only in the tools that help teams improve availability outcomes and do away with the dead weight. By using the most valuable tools and datasets, teams decrease noise and alert fatigue while managers reduce their total cost of ownership (TCO).

4Implement domain agnostic AIOps.

Adopt a domain-agnostic artificial intelligence for IT Operations (AIOps) solution to build efficiencies into otherwise inefficient monitoring strategies. AIOps ingests various types of data from monitoring tools, correlating the data and making it actionable. This technology helps teams provide better availability outcomes by quickly detecting, diagnosing and remediating incidents across the entire system. An advanced platform’s AI and machine learning (ML) technologies go a step further, preventing destructive patterns from recurring and proactively reducing incident volume.

5Manage tech debt.

Because AIOps tools help teams shrink the mean time to detect (MTTD) and mean time to remediate (MTTR), they benefit from less firefighting and more time to pursue proactive work. Where should this proactive work start? Pay down tech debt that hurts teams the most. As system stability increases and incidents and unplanned work lessen, teams should work to release even more time by automating toil and then use their increased capacity to build new features and improve platforms.

6Look to the future.

Set teams free from their monitoring cycles and empower them to innovate the customer experience. In the meantime, improve the employee experience by advancing DevOps practices and allowing teams to invest in their own mastery and learning.

With insight into DevOps teams’ days, leaders can release their employees from dull, repetitive and usually unplanned work. Systematically building tech stability and relying on AIOps to help reduces the time teams must spend on monitoring and incident management, empowering them to do what they do best: innovation and experimentation.

Phil Tee

Phil Tee, Ph.D. is the co-founder and CEO of Moogsoft, a leading provider of artificial intelligence for IT operations (AIOps). Phil has founded and led numerous companies, including Micromuse, RiverSoft (IPO) and Ninjini (acquired by Riverbed). As an innovator and pioneer in artificial intelligence, Phil has co-authored numerous peer-reviewed journals researching the very underpinnings of artificial intelligence, graph theory and network topology, as well as the filing of more than 50 patents in the application of artificial intelligence.

Phil Tee

Phil Tee, Ph.D. is the co-founder and CEO of Moogsoft, a leading provider of artificial intelligence for IT operations (AIOps). Phil has founded and led numerous companies, including Micromuse, RiverSoft (IPO) and Ninjini (acquired by Riverbed). As an innovator and pioneer in artificial intelligence, Phil has co-authored numerous peer-reviewed journals researching the very underpinnings of artificial intelligence, graph theory and network topology, as well as the filing of more than 50 patents in the application of artificial intelligence.