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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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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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...