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How Close Are We To Achieving a Monitoring Integration Strategy? A Survey

Moria Frederickson

In a June 2018 survey of over 400 IT professionals within the VMware User Group community, Blue Medora took a closer look at how various metric collection strategies and access to Dimensional Data impacts IT success. We started with one question: How important is your monitoring integration strategy?

We wanted to test if increased access, data depth and context provided by a Dimensional Data stream positively impacted IT outcomes. Dimensional Data refers to a real-time metric stream provided by a monitoring integration as a service (MIaaS). A Dimensional Data stream will include highly granular behavioral detail — beyond what a single endpoint API connection might include — as well as rich relational context.

75% of Companies Lack Critical Data to Manage IT System Health and Performance

The big takeaway — many organizations are running multiple monitoring tools in an effort to get the appropriate data depth and context they need across their entire IT stack. Sixty percent of respondents are already running three or more monitoring systems and 50% of them plan to consolidate monitoring tools in the next 18 months.

But despite that, two-thirds of IT ops teams still lack visibility into the external resource relationships and three-quarters lack access to component-level data resolution, resulting in more downtime than peers using monitoring integrations that provide this critical capability.

70% of respondents said most of their monitoring integrations were not compatible with more than one monitoring platform — a leading cause of metric silos in many organizations.

Dimensional Data adoption is still in its infancy, but demand is growing. Only 9% of organizations who participated in the survey currently have access to Dimensional Data across the majority of their integrations and are considered fully deployed. On average, 43% of organizations wish to increase Dimensional Data adoption within the next 18 months.


Figure 1: A breakdown of responses to the question "How many unique monitoring platforms do you have in use within your environment?"

Half of respondents indicated that they were trying to consolidate — perhaps in search of that elusive "single source of monitoring truth," but others had no near-term plans to decrease the number of monitoring tools. Some of the reasons they gave for sticking with all the tools they have included:

■ Different technologies monitored (infrastructure, cloud, etc.)

■ Different analytic or visualization capabilities

■ Different use cases (logs, performance, etc.)

■ Individual team preferences

70% of respondents said most of their monitoring integrations were not compatible with more than one monitoring platform, but demand is on the rise. Of that majority, 62% plan to make multi-platform metrics a reality in the near future.


Figure 2: Respondents’ current access to metrics across multiple platforms (without customization) is currently in the minority, but could move to the majority within the next two years.

The multi-platform adoption stats are particularly interesting when taken in context with the monitoring and tool consolidation stats in key finding one. For those not interested or planning to consolidate tools (again about half of the respondents in the survey) another path to eliminating metric silos has emerged--giving all monitoring platforms universal access to the same data set.

But back to that first question — How important is your monitoring integration strategy? We were surprised how much monitoring integrations impact respondents’ business. We’d heard it in conversations over the years but had never crunched the numbers. In four out of the five areas we examined, including productivity, utilization, downtime and cost savings the answer to our question was overwhelmingly yes.

Nearly 40% of the top performers in utilization and productivity gains had fully deployed Dimensional Data.


Figure 3: Of those reporting greater than $100k savings each year due to their monitoring integration strategy, 39% had fully deployed a dimensional data stream.

Of those who fully deployed Dimensional Data, one in five saw more than $1m a year in savings and one in two saw more than $100k in savings. Even we were blown away by the scale of savings.

It seems most monitoring research focuses on the efficacy of monitoring platforms or analytics engines but doesn’t spend a great deal of time looking at the data that goes into them. This research supported the idea that universal access to a Dimensional Data stream or other integration strategy that includes metric breadth, depth and context will likely have a positive context on key business measures like IT productivity, utilization, cost savings and downtime.

About the Survey: Blue Medora partnered with the VMware User Group (VMUG) to survey 410 of the group’s members about the current and near-future state of monitoring integration in their environment. All had IT responsibilities at organizations with over 500 employees. 76% of respondents held an admin or architect role within the IT organization.

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How Close Are We To Achieving a Monitoring Integration Strategy? A Survey

Moria Frederickson

In a June 2018 survey of over 400 IT professionals within the VMware User Group community, Blue Medora took a closer look at how various metric collection strategies and access to Dimensional Data impacts IT success. We started with one question: How important is your monitoring integration strategy?

We wanted to test if increased access, data depth and context provided by a Dimensional Data stream positively impacted IT outcomes. Dimensional Data refers to a real-time metric stream provided by a monitoring integration as a service (MIaaS). A Dimensional Data stream will include highly granular behavioral detail — beyond what a single endpoint API connection might include — as well as rich relational context.

75% of Companies Lack Critical Data to Manage IT System Health and Performance

The big takeaway — many organizations are running multiple monitoring tools in an effort to get the appropriate data depth and context they need across their entire IT stack. Sixty percent of respondents are already running three or more monitoring systems and 50% of them plan to consolidate monitoring tools in the next 18 months.

But despite that, two-thirds of IT ops teams still lack visibility into the external resource relationships and three-quarters lack access to component-level data resolution, resulting in more downtime than peers using monitoring integrations that provide this critical capability.

70% of respondents said most of their monitoring integrations were not compatible with more than one monitoring platform — a leading cause of metric silos in many organizations.

Dimensional Data adoption is still in its infancy, but demand is growing. Only 9% of organizations who participated in the survey currently have access to Dimensional Data across the majority of their integrations and are considered fully deployed. On average, 43% of organizations wish to increase Dimensional Data adoption within the next 18 months.


Figure 1: A breakdown of responses to the question "How many unique monitoring platforms do you have in use within your environment?"

Half of respondents indicated that they were trying to consolidate — perhaps in search of that elusive "single source of monitoring truth," but others had no near-term plans to decrease the number of monitoring tools. Some of the reasons they gave for sticking with all the tools they have included:

■ Different technologies monitored (infrastructure, cloud, etc.)

■ Different analytic or visualization capabilities

■ Different use cases (logs, performance, etc.)

■ Individual team preferences

70% of respondents said most of their monitoring integrations were not compatible with more than one monitoring platform, but demand is on the rise. Of that majority, 62% plan to make multi-platform metrics a reality in the near future.


Figure 2: Respondents’ current access to metrics across multiple platforms (without customization) is currently in the minority, but could move to the majority within the next two years.

The multi-platform adoption stats are particularly interesting when taken in context with the monitoring and tool consolidation stats in key finding one. For those not interested or planning to consolidate tools (again about half of the respondents in the survey) another path to eliminating metric silos has emerged--giving all monitoring platforms universal access to the same data set.

But back to that first question — How important is your monitoring integration strategy? We were surprised how much monitoring integrations impact respondents’ business. We’d heard it in conversations over the years but had never crunched the numbers. In four out of the five areas we examined, including productivity, utilization, downtime and cost savings the answer to our question was overwhelmingly yes.

Nearly 40% of the top performers in utilization and productivity gains had fully deployed Dimensional Data.


Figure 3: Of those reporting greater than $100k savings each year due to their monitoring integration strategy, 39% had fully deployed a dimensional data stream.

Of those who fully deployed Dimensional Data, one in five saw more than $1m a year in savings and one in two saw more than $100k in savings. Even we were blown away by the scale of savings.

It seems most monitoring research focuses on the efficacy of monitoring platforms or analytics engines but doesn’t spend a great deal of time looking at the data that goes into them. This research supported the idea that universal access to a Dimensional Data stream or other integration strategy that includes metric breadth, depth and context will likely have a positive context on key business measures like IT productivity, utilization, cost savings and downtime.

About the Survey: Blue Medora partnered with the VMware User Group (VMUG) to survey 410 of the group’s members about the current and near-future state of monitoring integration in their environment. All had IT responsibilities at organizations with over 500 employees. 76% of respondents held an admin or architect role within the IT organization.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...