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Flying Blind — The 2013 IT Operations Quotient Report

Sasha Gilenson

IT Operations is now overwhelmed — by the volume, velocity and variety of change and configuration data, lacking insight or actionable information, all making change and configuration problems a chronic pain.

As shown by recent surveys at the Gartner Data Center Summit and ServiceNow Knowledge13 conferences, where Evolven surveyed over 300 IT Operations professionals asking questions critical to IT operations management, 84% of IT professionals said that they want to significantly improve their IT operations management.

The 2013 IT OQ (Operations Quotient) Report provides a good indication to IT executives as to whether IT ops investments have yielded desired results, using the IT Operations Quotient (OQ), a metric for evaluating operational ability to support existing business services and incoming business requirements.

When an Incident Occurs, Can You Quickly Know What Changed?

Only 7% of the professionals surveys indicated that, using their current IT management tools, they could quickly identify what changed in order to respond to problems and incidents.

The first question IT operations asks themselves when an incident occurs is "what changed?" Due to the complexity and dynamics taking place in the modern data center, with overwhelming configuration data and frequent changes, this question has become quite formidable.

Between applications, environments, and individual instances, mistakes and unauthorized changes happen, demanding that IT ops spend significant amounts of time managing configuration values.

Traditional IT management tools were not designed to deal with the complexity and dynamics of the modern data center. These tools have not been automated to collect data down to granular details, analyzing all changes and consolidating information to extract meaningful information from the sea of raw change and configuration data.

Without systems to manage and organize this growth, IT will drown in its own data.

Can You Automatically Validate that Your Release Deployed Accurately?

Only 8% of the participants surveyed agreed that they could currently automatically validate the accuracy of their deployments. Available release management tools are unprepared for one-off changes or changes that do not follow policy.

IT organizations regularly transition changes to production environments, checking changes throughout a set of pre-production environments.

Now IT is under even more pressure. To meet business requirements, application deployments have accelerated and software deployment schedules have driven up high-paced change activity. The increasingly agile nature of application and infrastructure change requests, leaves IT operations at a loss as they are inundated by change requests that run the gamut from the critical and high priority to the minor and unimportant.

With a typical environment having thousands of different system configuration parameters, any little change can impact performance. So it’s not surprising to see many companies going through painful stabilization periods after a release, as well as production outages.

Even when using automated tools for deployment, the lack of detailed visibility into the release means IT ops can’t ensure accurate, error-free deployments.

Can You Quickly Identify the Incident’s Root Cause?

As shown in this survey, the vast majority of IT professionals surveyed concurred that they lack the capabilities to quickly identify an incident’s root cause. IT organizations find themselves challenged when assessing system failure and tracking down the root cause, such as if a patch wasn't deployed or a server failed.

Any minute misconfiguration or omission of a single configuration parameter can quickly lead to an incident with high impact. With an infinite number of these configuration parameters in play when an environment incident hits, finding the root cause consumes both precious time and manpower, making MTTR woefully high in most organizations.

The root cause of downtime and incidents often start at the most granular level of configuration changes where today's configuration management and change management tools don't provide visibility. The different groups in organizations, like IT Development, Support, and Operations, tend to point the finger of blame for issues, and fail to diagnose or deal with the root cause of the problem.

After a major incident, root cause analysis should focus on root cause of the failure in order to not only resolve the incident but to head off a recurrence. Even when IT teams manage to suppress a failure, and operations can return to "normal", the true root cause may still remain unresolved, leaving the organization exposed to further chaos.

Can You Automatically Verify the Consistency of Your Environments?

From our survey, only 5% of the respondents felt that currently they can automatically verify the consistency of their environments, where they need to go into the fine, granular details and identify the make-up of even minor changes, having to process the enormous amounts of configuration data, for verifying the consistency between servers and environments.

As IT organizations regularly transition changes to production environments, IT teams need to check changes throughout a set of pre-production environments that can include system test, performance test, UAT, staging, etc (changes are also mirrored in a Disaster Recovery environment). IT has sought to diversify their workloads, spreading deployments over multiple IT environments to mitigate risk, yet also doubling complexity.

The high volumes of changes means that not all changes consistently make their way to all environments (pre-prod, prod, DR). The configuration parameters must be validated for consistency in real-time.

IT Operations Analytics Helps

With performance at risk from any disruptions to stability, IT teams need to know exactly what has changed in an environment.

Managing IT environments with intelligent automated analytics will drive more sophisticated proactive processes like comparing environment states, validating releases, and verifying consistency of changes,helping to prevent or identify critical issues. So rather than continue to feed bloated system tools, IT Operations should strive to simplify and implement configuration management based on IT Operations Analytics, and turn the situation around from what can’t be managed to being what can be done about performance and availability.

Sasha Gilenson is the Founder and CEO of Evolven Software.

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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.

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Flying Blind — The 2013 IT Operations Quotient Report

Sasha Gilenson

IT Operations is now overwhelmed — by the volume, velocity and variety of change and configuration data, lacking insight or actionable information, all making change and configuration problems a chronic pain.

As shown by recent surveys at the Gartner Data Center Summit and ServiceNow Knowledge13 conferences, where Evolven surveyed over 300 IT Operations professionals asking questions critical to IT operations management, 84% of IT professionals said that they want to significantly improve their IT operations management.

The 2013 IT OQ (Operations Quotient) Report provides a good indication to IT executives as to whether IT ops investments have yielded desired results, using the IT Operations Quotient (OQ), a metric for evaluating operational ability to support existing business services and incoming business requirements.

When an Incident Occurs, Can You Quickly Know What Changed?

Only 7% of the professionals surveys indicated that, using their current IT management tools, they could quickly identify what changed in order to respond to problems and incidents.

The first question IT operations asks themselves when an incident occurs is "what changed?" Due to the complexity and dynamics taking place in the modern data center, with overwhelming configuration data and frequent changes, this question has become quite formidable.

Between applications, environments, and individual instances, mistakes and unauthorized changes happen, demanding that IT ops spend significant amounts of time managing configuration values.

Traditional IT management tools were not designed to deal with the complexity and dynamics of the modern data center. These tools have not been automated to collect data down to granular details, analyzing all changes and consolidating information to extract meaningful information from the sea of raw change and configuration data.

Without systems to manage and organize this growth, IT will drown in its own data.

Can You Automatically Validate that Your Release Deployed Accurately?

Only 8% of the participants surveyed agreed that they could currently automatically validate the accuracy of their deployments. Available release management tools are unprepared for one-off changes or changes that do not follow policy.

IT organizations regularly transition changes to production environments, checking changes throughout a set of pre-production environments.

Now IT is under even more pressure. To meet business requirements, application deployments have accelerated and software deployment schedules have driven up high-paced change activity. The increasingly agile nature of application and infrastructure change requests, leaves IT operations at a loss as they are inundated by change requests that run the gamut from the critical and high priority to the minor and unimportant.

With a typical environment having thousands of different system configuration parameters, any little change can impact performance. So it’s not surprising to see many companies going through painful stabilization periods after a release, as well as production outages.

Even when using automated tools for deployment, the lack of detailed visibility into the release means IT ops can’t ensure accurate, error-free deployments.

Can You Quickly Identify the Incident’s Root Cause?

As shown in this survey, the vast majority of IT professionals surveyed concurred that they lack the capabilities to quickly identify an incident’s root cause. IT organizations find themselves challenged when assessing system failure and tracking down the root cause, such as if a patch wasn't deployed or a server failed.

Any minute misconfiguration or omission of a single configuration parameter can quickly lead to an incident with high impact. With an infinite number of these configuration parameters in play when an environment incident hits, finding the root cause consumes both precious time and manpower, making MTTR woefully high in most organizations.

The root cause of downtime and incidents often start at the most granular level of configuration changes where today's configuration management and change management tools don't provide visibility. The different groups in organizations, like IT Development, Support, and Operations, tend to point the finger of blame for issues, and fail to diagnose or deal with the root cause of the problem.

After a major incident, root cause analysis should focus on root cause of the failure in order to not only resolve the incident but to head off a recurrence. Even when IT teams manage to suppress a failure, and operations can return to "normal", the true root cause may still remain unresolved, leaving the organization exposed to further chaos.

Can You Automatically Verify the Consistency of Your Environments?

From our survey, only 5% of the respondents felt that currently they can automatically verify the consistency of their environments, where they need to go into the fine, granular details and identify the make-up of even minor changes, having to process the enormous amounts of configuration data, for verifying the consistency between servers and environments.

As IT organizations regularly transition changes to production environments, IT teams need to check changes throughout a set of pre-production environments that can include system test, performance test, UAT, staging, etc (changes are also mirrored in a Disaster Recovery environment). IT has sought to diversify their workloads, spreading deployments over multiple IT environments to mitigate risk, yet also doubling complexity.

The high volumes of changes means that not all changes consistently make their way to all environments (pre-prod, prod, DR). The configuration parameters must be validated for consistency in real-time.

IT Operations Analytics Helps

With performance at risk from any disruptions to stability, IT teams need to know exactly what has changed in an environment.

Managing IT environments with intelligent automated analytics will drive more sophisticated proactive processes like comparing environment states, validating releases, and verifying consistency of changes,helping to prevent or identify critical issues. So rather than continue to feed bloated system tools, IT Operations should strive to simplify and implement configuration management based on IT Operations Analytics, and turn the situation around from what can’t be managed to being what can be done about performance and availability.

Sasha Gilenson is the Founder and CEO of Evolven Software.

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 ...