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What to Look For in an Analyst Report

Jonah Kowall

The Internet is an amazing medium for anyone looking to articulate an opinion. Everyone should practice writing and expressing themselves, as it's a great tool to build throughout life. The ability to publish information is everyone's right. However, credibility is a whole different challenge. Why do we trust what is published in the New York Times, but don't trust what is published in the tabloids? It comes down to rigor in journalistic practices. In research, it's also tied to a strict methodology or process.

During my time at Gartner, I learned a great deal about the differences between analyst firms, mostly by meeting and discussing things with friends at other analyst firms. I quickly learned which firms will write a whitepaper, and which firms will not create marketing materials for software companies for pay.

In the case of Gartner, the analyst has the freedom to publish anything, if they follow the extremely rigorous research process and can defend the opinion they are creating as an analyst. The process for analysts to publish branded documents, such as a Magic Quadrant, is over 30 pages, but publicly a small subset of this is disclosed. Clients get another deeper look in this document (Gartner subscribers only).

During the publication process for any document, there is rigorous peer review, management review, and editing to handle any issues in process or the fact base. Aside from this process and methodology, the analyst speaks with hundreds of end users of a particular technology through the year on phone calls and at conferences. This allows the analyst to comprehend the reality of a market versus what vendors may care to share with an analyst.

When witnessing small analyst firms attempting to assess markets without end user perspective and without speaking to all the vendors in the research — while blatantly requesting and collecting money directly from vendors before research is even drafted — the red flags come up. I discourage any organization from participating in these blatant acts of extortion. When vendors sponsor and fund this research, it just enables the lie to persist, year after year. This is clearly a major violation of journalistic integrity. The vendors who pay continually jam this poorly crafted research down end users' throats, and avoid the questions about where it comes from. This occurs regularly.

The violations don't just stop there, but clearly there are researchers who infringe on specific research formats, whether it's the Gartner Magic Quadrant, the Forrester Wave, or the IDC MarketScape. The lawyers of the respective firms get involved in these disputes, but small single person "analyst firms" seem to do this regularly and get slapped with cease and desist letters.

Leading researchers in the public domain should be able to publicly discuss, dispute, and learn from others in public forums. You'll see top analyst firms participate in conferences, panels, LinkedIn groups, Twitter, and other public forums. If a researcher is going to build a fact-based opinion of something, they should be able to participate and defend that position. Many of these smaller analyst firms or independent researchers avoid doing so, continually hide, or many times do not even have a name associated with research. I give big kudos and credibility to the researchers and analysts who stand behind what they publish.

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.

What to Look For in an Analyst Report

Jonah Kowall

The Internet is an amazing medium for anyone looking to articulate an opinion. Everyone should practice writing and expressing themselves, as it's a great tool to build throughout life. The ability to publish information is everyone's right. However, credibility is a whole different challenge. Why do we trust what is published in the New York Times, but don't trust what is published in the tabloids? It comes down to rigor in journalistic practices. In research, it's also tied to a strict methodology or process.

During my time at Gartner, I learned a great deal about the differences between analyst firms, mostly by meeting and discussing things with friends at other analyst firms. I quickly learned which firms will write a whitepaper, and which firms will not create marketing materials for software companies for pay.

In the case of Gartner, the analyst has the freedom to publish anything, if they follow the extremely rigorous research process and can defend the opinion they are creating as an analyst. The process for analysts to publish branded documents, such as a Magic Quadrant, is over 30 pages, but publicly a small subset of this is disclosed. Clients get another deeper look in this document (Gartner subscribers only).

During the publication process for any document, there is rigorous peer review, management review, and editing to handle any issues in process or the fact base. Aside from this process and methodology, the analyst speaks with hundreds of end users of a particular technology through the year on phone calls and at conferences. This allows the analyst to comprehend the reality of a market versus what vendors may care to share with an analyst.

When witnessing small analyst firms attempting to assess markets without end user perspective and without speaking to all the vendors in the research — while blatantly requesting and collecting money directly from vendors before research is even drafted — the red flags come up. I discourage any organization from participating in these blatant acts of extortion. When vendors sponsor and fund this research, it just enables the lie to persist, year after year. This is clearly a major violation of journalistic integrity. The vendors who pay continually jam this poorly crafted research down end users' throats, and avoid the questions about where it comes from. This occurs regularly.

The violations don't just stop there, but clearly there are researchers who infringe on specific research formats, whether it's the Gartner Magic Quadrant, the Forrester Wave, or the IDC MarketScape. The lawyers of the respective firms get involved in these disputes, but small single person "analyst firms" seem to do this regularly and get slapped with cease and desist letters.

Leading researchers in the public domain should be able to publicly discuss, dispute, and learn from others in public forums. You'll see top analyst firms participate in conferences, panels, LinkedIn groups, Twitter, and other public forums. If a researcher is going to build a fact-based opinion of something, they should be able to participate and defend that position. Many of these smaller analyst firms or independent researchers avoid doing so, continually hide, or many times do not even have a name associated with research. I give big kudos and credibility to the researchers and analysts who stand behind what they publish.

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.