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IT Professionals Say Fast Actionable Data Remains a Myth

Rex McMillan

Data may be pouring into enterprises but IT professionals still find most of it stuck in siloed departments and weeks away from being able to drive any valued action. Coupled with the ongoing concerns over security responsiveness, IT teams have to push aside other important performance-oriented data in order to ensure security data, at least, gets prominent attention. A new survey by Ivanti shows the disconnect between enterprise departments struggling to improve operations like automation while being challenged with a siloed structure and a data onslaught.

Survey findings from over 400 IT professionals showed that security remains the top priority (70%) in obtaining real-time actionable data. Other key priorities get secondary consideration as they suffer from siloed departments. Automation (46%), user productivity and troubleshooting (42%) and customer experience (41%) have to take a back seat as IT focuses attention on data that may indicate a critical risk. Additionally the survey found that application deployment takes IT teams the most time to perform, followed by backup and data protection.

Conversely, the survey found that onboarding/offboarding suffers the least (20%) due to siloes, an indication IT and HR have found a way to work far more closely with integrated goals.

Key findings of the report were:

■ Only 10% of respondents said the data they receive is actionable within minutes.

■ More than half of IT professionals (51%) report they have to work with their data for days, weeks or more, before it's actionable.

■ One in three respondents said they have the resources to act on their data but more than half (52%) said they only sometimes have the resources.

■ 15% of IT professionals say they have too many data sources to count.

■ 37% of professionals said they have about 11-25 different sources for data.

Siloed Organizations Slow Down Data Action and Performance

To improve performance, notably application deployment, automation and troubleshooting user productivity issues, IT professionals need a more unified approach when working across organizational departments and existing silos. Security remains a major data insights requirement, but it's also important to note that IT organizations need to find better ways to work with their data or it will continue to impact other critical IT priorities.

Organizations need to start erasing siloes and enabling IT teams to have more insight into departments. Better access to application data, for example, will help IT to use the data resources enterprises are paying for, and realize value from the investment.

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 ...

IT Professionals Say Fast Actionable Data Remains a Myth

Rex McMillan

Data may be pouring into enterprises but IT professionals still find most of it stuck in siloed departments and weeks away from being able to drive any valued action. Coupled with the ongoing concerns over security responsiveness, IT teams have to push aside other important performance-oriented data in order to ensure security data, at least, gets prominent attention. A new survey by Ivanti shows the disconnect between enterprise departments struggling to improve operations like automation while being challenged with a siloed structure and a data onslaught.

Survey findings from over 400 IT professionals showed that security remains the top priority (70%) in obtaining real-time actionable data. Other key priorities get secondary consideration as they suffer from siloed departments. Automation (46%), user productivity and troubleshooting (42%) and customer experience (41%) have to take a back seat as IT focuses attention on data that may indicate a critical risk. Additionally the survey found that application deployment takes IT teams the most time to perform, followed by backup and data protection.

Conversely, the survey found that onboarding/offboarding suffers the least (20%) due to siloes, an indication IT and HR have found a way to work far more closely with integrated goals.

Key findings of the report were:

■ Only 10% of respondents said the data they receive is actionable within minutes.

■ More than half of IT professionals (51%) report they have to work with their data for days, weeks or more, before it's actionable.

■ One in three respondents said they have the resources to act on their data but more than half (52%) said they only sometimes have the resources.

■ 15% of IT professionals say they have too many data sources to count.

■ 37% of professionals said they have about 11-25 different sources for data.

Siloed Organizations Slow Down Data Action and Performance

To improve performance, notably application deployment, automation and troubleshooting user productivity issues, IT professionals need a more unified approach when working across organizational departments and existing silos. Security remains a major data insights requirement, but it's also important to note that IT organizations need to find better ways to work with their data or it will continue to impact other critical IT priorities.

Organizations need to start erasing siloes and enabling IT teams to have more insight into departments. Better access to application data, for example, will help IT to use the data resources enterprises are paying for, and realize value from the investment.

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 ...