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The State of Monitoring 2017: More Alerts, More Tools, More Focus on Customer Experience

Michael Butt

Applications and infrastructure are being deployed and commissioned at a faster rate than ever before, the number of tools it takes to effectively manage these services is multiplying, and the expectations placed on IT to ensure customer satisfaction is increasing, according to The State of Monitoring 2017 report from BigPanda.

The urgency to ensure reliability and uptime resonates across the board, and it's clear that IT leaders are focused on solutions that will not only work today, but can scale and adapt to tomorrow.


Below, we review some of the key takeaways from this year's report.

1. Alert noise is only getting louder

More than three quarters of the 1500+ respondents stated that reducing alert noise is a challenge, and the number of respondents reporting high alert volumes (100-500, 500-1000, or 1000+ alerts per day) has increased across the board over 2016. This group reports extremely low levels of satisfaction with their ability to respond to alerts, which is reflected in the fact that only 26% are able to remediate the majority (75-100%) within 24 hours. Furthermore, those with high volumes of alerts are more concerned about complying to customer SLAs and delivering business objectives to schedule.

2. The average monitoring stack is growing

The findings of this year's survey confirm that IT practitioners are relying on a growing number of tools to effectively do their job. According to the report, the average practitioner currently uses 6-7 tools on a regular basis, and over half of respondents reported that they plan to further expand their stack in 2017 – by approximately two tools on average. This means that we are likely to see that figure jump to 8-9 tools on average next year, and that's just per person. The total number of tools required organization-wide to effectively support agile development, uptime and reliability is no doubt much higher, particularly at the enterprise level.

3. Pressure to do more with less?

Overall, company size skewed large, with the majority of respondents hailing from organizations with 1000 or more employees. But interestingly, team size demonstrated the opposite trend, with most respondents reporting a team of less than ten. This may signal that operational independence at larger enterprises is migrating away from a centralized IT, with a larger number of smaller, fragmented teams, or that there is increasing pressure on IT to expand their capacity, without increasing headcount.

4. The frequency of both code and infrastructure change is on the rise

Across the board, the number of respondents reporting daily or weekly code deployments increased, while monthly and yearly deployments declined.

Similarly for infrastructure management, the number of respondents who reported that their organization makes just a few changes per year sharply declined, while all other response groups increased.

5. Room for improvement

Only half of respondents reported that their organization has a defined monitoring strategy in place.

Even more troubling, a meager 13% agreed that they are very satisfied with their approach to monitoring, and just 11% are satisfied based on overall investment.

6. Customer experience is king

For the second year in a row, customer satisfaction far outranked all other performance metrics included in our survey, including some that many might consider “traditional” for IT practitioners, such as MTTR and incident volume. Customer satisfaction was cited as a KPI by a whopping 73% of respondents, while the second most popular metric, SLA compliance, was cited by just 45%.

Methodology: Over 1500 IT professionals took part in this year's survey, representing a wide range of industries and featuring a mix of executives, managers, and individual contributors.

Michael Butt is Director of Product Marketing at BigPanda.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The State of Monitoring 2017: More Alerts, More Tools, More Focus on Customer Experience

Michael Butt

Applications and infrastructure are being deployed and commissioned at a faster rate than ever before, the number of tools it takes to effectively manage these services is multiplying, and the expectations placed on IT to ensure customer satisfaction is increasing, according to The State of Monitoring 2017 report from BigPanda.

The urgency to ensure reliability and uptime resonates across the board, and it's clear that IT leaders are focused on solutions that will not only work today, but can scale and adapt to tomorrow.


Below, we review some of the key takeaways from this year's report.

1. Alert noise is only getting louder

More than three quarters of the 1500+ respondents stated that reducing alert noise is a challenge, and the number of respondents reporting high alert volumes (100-500, 500-1000, or 1000+ alerts per day) has increased across the board over 2016. This group reports extremely low levels of satisfaction with their ability to respond to alerts, which is reflected in the fact that only 26% are able to remediate the majority (75-100%) within 24 hours. Furthermore, those with high volumes of alerts are more concerned about complying to customer SLAs and delivering business objectives to schedule.

2. The average monitoring stack is growing

The findings of this year's survey confirm that IT practitioners are relying on a growing number of tools to effectively do their job. According to the report, the average practitioner currently uses 6-7 tools on a regular basis, and over half of respondents reported that they plan to further expand their stack in 2017 – by approximately two tools on average. This means that we are likely to see that figure jump to 8-9 tools on average next year, and that's just per person. The total number of tools required organization-wide to effectively support agile development, uptime and reliability is no doubt much higher, particularly at the enterprise level.

3. Pressure to do more with less?

Overall, company size skewed large, with the majority of respondents hailing from organizations with 1000 or more employees. But interestingly, team size demonstrated the opposite trend, with most respondents reporting a team of less than ten. This may signal that operational independence at larger enterprises is migrating away from a centralized IT, with a larger number of smaller, fragmented teams, or that there is increasing pressure on IT to expand their capacity, without increasing headcount.

4. The frequency of both code and infrastructure change is on the rise

Across the board, the number of respondents reporting daily or weekly code deployments increased, while monthly and yearly deployments declined.

Similarly for infrastructure management, the number of respondents who reported that their organization makes just a few changes per year sharply declined, while all other response groups increased.

5. Room for improvement

Only half of respondents reported that their organization has a defined monitoring strategy in place.

Even more troubling, a meager 13% agreed that they are very satisfied with their approach to monitoring, and just 11% are satisfied based on overall investment.

6. Customer experience is king

For the second year in a row, customer satisfaction far outranked all other performance metrics included in our survey, including some that many might consider “traditional” for IT practitioners, such as MTTR and incident volume. Customer satisfaction was cited as a KPI by a whopping 73% of respondents, while the second most popular metric, SLA compliance, was cited by just 45%.

Methodology: Over 1500 IT professionals took part in this year's survey, representing a wide range of industries and featuring a mix of executives, managers, and individual contributors.

Michael Butt is Director of Product Marketing at BigPanda.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.