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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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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

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...