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If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 1: Cost Advantage

Dennis Drogseth

This is the fourth in my series of blogs inspired by EMA's AIA buyer's guide — directed at helping IT invest in Advanced IT Analytics (AIA), what the industry more commonly calls "Operational Analytics." The goal was to create a "Consumer's Report" approach. And to do that we took it one step further. We created what we called "Shopping Cart Criteria" based on our prior research on AIA adoptions over the past three years.

We divided the sixteen shopping cart criteria into three parts:

■ Cost advantage

■ Environments

■ Scenarios

I will address each of these criteria separately in the next three blogs. In this blog, I'm going to address cost advantage.

Cost, overhead, and time to value are often key challenges in adopting AIA solutions. In the past, these factors have often been especially onerous. But we saw strong levels of improvement among many vendors, and surprising areas of innovation among others. For instance, four of the vendors in our buyer's guide were primarily SaaS. And three had SaaS options as well as on-premise.

For cost, we provided pricing models and maintenance fee percentages. Then we dove into three critical areas:

■ Time to Value

■ Administration and Support

■ Toolset Consolidation

Time to Value

"Time to value" is defined differently by different vendors, which we captured based on the data we were provided as well as phone interviews. We documented factors relevant to ease of deployment, time to learn unique environments, and proof points from deployment interviews, with special weight to documented times for achieving critical strategic results. In our research, we saw valid assessments ranging from meaningful value delivered within a single day, and in fact in two cases within several hours, to more conservative estimates in terms of weeks and months.

Administration and Support

Here we looked at design and upgrade requirements, as well as insights into administrative overhead from deployments, as well as maintenance options and costs. We gave serious weight to the breadth and depth of professional services offered — querying vendors on a list of options ranging from "planning and deployment" (which was provided by all but one vendor) to support for "business activity management" (supported only by two).

Toolset Consolidation

One of the most compelling reasons for investing in AIA is toolset consolidation — which can bring both OpEx and CapEx cost savings. For this criterion, we considered breadth and ease of integrations, breadth of effective stakeholder support, and proof points from commentaries in deployments. The number of fully supported integrations we saw across our thirteen vendors ranged from 10 to more than 100 out-of-the-box. We also noticed a growing trend not only to assimilate data from monitoring and other sources, but also to promote data outward to third-party tools and dashboards, as well as IT service management solutions for workflow, trouble ticketing, and in some cases CMDB/CMS updates.

Read Part 2: Enivornments

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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 ...

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 ...

If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 1: Cost Advantage

Dennis Drogseth

This is the fourth in my series of blogs inspired by EMA's AIA buyer's guide — directed at helping IT invest in Advanced IT Analytics (AIA), what the industry more commonly calls "Operational Analytics." The goal was to create a "Consumer's Report" approach. And to do that we took it one step further. We created what we called "Shopping Cart Criteria" based on our prior research on AIA adoptions over the past three years.

We divided the sixteen shopping cart criteria into three parts:

■ Cost advantage

■ Environments

■ Scenarios

I will address each of these criteria separately in the next three blogs. In this blog, I'm going to address cost advantage.

Cost, overhead, and time to value are often key challenges in adopting AIA solutions. In the past, these factors have often been especially onerous. But we saw strong levels of improvement among many vendors, and surprising areas of innovation among others. For instance, four of the vendors in our buyer's guide were primarily SaaS. And three had SaaS options as well as on-premise.

For cost, we provided pricing models and maintenance fee percentages. Then we dove into three critical areas:

■ Time to Value

■ Administration and Support

■ Toolset Consolidation

Time to Value

"Time to value" is defined differently by different vendors, which we captured based on the data we were provided as well as phone interviews. We documented factors relevant to ease of deployment, time to learn unique environments, and proof points from deployment interviews, with special weight to documented times for achieving critical strategic results. In our research, we saw valid assessments ranging from meaningful value delivered within a single day, and in fact in two cases within several hours, to more conservative estimates in terms of weeks and months.

Administration and Support

Here we looked at design and upgrade requirements, as well as insights into administrative overhead from deployments, as well as maintenance options and costs. We gave serious weight to the breadth and depth of professional services offered — querying vendors on a list of options ranging from "planning and deployment" (which was provided by all but one vendor) to support for "business activity management" (supported only by two).

Toolset Consolidation

One of the most compelling reasons for investing in AIA is toolset consolidation — which can bring both OpEx and CapEx cost savings. For this criterion, we considered breadth and ease of integrations, breadth of effective stakeholder support, and proof points from commentaries in deployments. The number of fully supported integrations we saw across our thirteen vendors ranged from 10 to more than 100 out-of-the-box. We also noticed a growing trend not only to assimilate data from monitoring and other sources, but also to promote data outward to third-party tools and dashboards, as well as IT service management solutions for workflow, trouble ticketing, and in some cases CMDB/CMS updates.

Read Part 2: Enivornments

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 ...