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Shifting to Analytics Driven Management for IT Operations

Today’s market environment demands businesses to change and adapt rapidly according to market dynamics, while still remaining in control. For business, these dynamics can mean sifting through what can amount to petabytes of data to act tactically and strategically.

Business Intelligence (BI) analytics tools help companies catch what could have been missed opportunities, using robust infrastructure to sift through mountains of data, and applying intelligent analytics. This way, business can identify hidden trends, customer relationships, buying behavior, operational and financial patterns, business opportunities and other vital information allowing business to take part in the market proactively.

Through BSM initiatives, IT is charged with supporting the changing demands of business, maintaining availability and ensuring that performance remains high. Similar to the business side experience, the IT landscape has grown in complexity, supporting a wider and growing range of technologies and platforms (Virtualization, Cloud, Open Source etc.), and accelerated application release schedules. This now means IT faces near-overwhelming quantities of information.

So while business progresses via BI, adopting analytics for management decisions, ironically the organization supporting this infrastructure, IT Operations, has adhered to an older, static-process driven paradigm. By not applying the analytics-based approach (like business) for their own operations, IT jeopardizes system stability, ultimately exposing business to the risk of devastating consequences.

Mountains of Data

Mountains of dynamic information confront IT. One of the prominent areas is in the cloud scenario. Self-service provisioning has multiplied the amount of activities occurring outside of static processes. The new provisioning opportunities are beyond IT management, leaving IT with limited visibility to what happens there. For example, an organization sets up a private cloud with a dynamic management system, allowing self-service provisioning of servers for the testing team. Traditionally, testing professionals would have come to IT and request an environment, and IT would oversee and manage this entire process. Now the process is independent, when testing needs an environment, they just create it.

Today’s Approach: Static Processes Drive IT

IT Operations has been running on static processes and strict workflows. For instance, ITIL has a process for Change Management that works according to certain steps. There are also a set metrics for measuring performance, like the amount of changes that successfully went through or failed.

IT Ops can plan as much as possible, but it won’t ensure that everything will occur as planned.

For example, when IT implements an application upgrade, and makes changes to the environment, IT administration can go through an entire established process, and still the application doesn’t function as planned. IT managers check the processes that the upgrade went through, yet still performance lags. Then they need to go into the fine, granular details and see every step, identifying the make-up of even minor changes, seeing how it was deployed to all the servers, what is the consistency between servers, have there been additional interference to the servers. They need to take this enormous amount of data – configuration and granular changes – and pinpoint what was the root cause.

Workflow-driven Management Processes

Static processes operate through workflows. The workflow only supports part of the process but there are so many things surrounding the workflow, happening outside the workflow. Business demands can force shortcuts to be taken. Steps in the workflow can be skipped in order to get immediate approval, even omitting the test stage.

Workflows Create False Security

Even when processes are enforced, like having registrations as part of the workflow management, this creates the belief that everything has been solved. There is no organization that can claim they operate completely within the bounds of established processes and approvals.

This situation creates a sense of false security that IT is on top of all the changes. IT Ops can think that everything works perfect and then the organization religiously adheres to their processes, relying on CMDB systems and workflows, ultimately undermining operations.

A Shift in Paradigm to Analytics Driven Management

Neurologists will explain that the brain has two distinct hemispheres. The right side of the brain collects information, while the left side is cognitive and analyzes this information, translating all of the sensory input into usable data.

This is really the same model for today’s IT organization, where operations need to know what is happening now. IT Ops can find itself stuck, trying to adjust static processes while keeping track of and handling dynamic events, and then getting caught off-guard when issues arise. The solution is to approach this situation with dynamic analytics, for dealing with all the changing data, and to see what is really happening. This goes beyond those few designated indicators that were usually watched, rather IT Ops needs Analytic Driven Management, similar to how business has adopted BI, extracting actionable information out of mountains of data to help decision makers respond efficiently.

About Sasha Gilenson

Sasha Gilenson is Founder and CEO of Evolven. Prior to founding Evolven in 2007, he spent 13 years with Mercury Interactive (acquired by HP), managing the QA organization and participating in establishing Mercury Interactive's Software as a Service (SaaS). Sasha played a key role in the development of Mercury Interactive's worldwide Business Technology Optimization (BTO) strategy and drove field operations of the Wireless Business Unit, all while taking on the duties as the Mercury Interactive's top "guru" in quality processes and IT practices domain.

Related Links:

www.evolven.com

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Shifting to Analytics Driven Management for IT Operations

Today’s market environment demands businesses to change and adapt rapidly according to market dynamics, while still remaining in control. For business, these dynamics can mean sifting through what can amount to petabytes of data to act tactically and strategically.

Business Intelligence (BI) analytics tools help companies catch what could have been missed opportunities, using robust infrastructure to sift through mountains of data, and applying intelligent analytics. This way, business can identify hidden trends, customer relationships, buying behavior, operational and financial patterns, business opportunities and other vital information allowing business to take part in the market proactively.

Through BSM initiatives, IT is charged with supporting the changing demands of business, maintaining availability and ensuring that performance remains high. Similar to the business side experience, the IT landscape has grown in complexity, supporting a wider and growing range of technologies and platforms (Virtualization, Cloud, Open Source etc.), and accelerated application release schedules. This now means IT faces near-overwhelming quantities of information.

So while business progresses via BI, adopting analytics for management decisions, ironically the organization supporting this infrastructure, IT Operations, has adhered to an older, static-process driven paradigm. By not applying the analytics-based approach (like business) for their own operations, IT jeopardizes system stability, ultimately exposing business to the risk of devastating consequences.

Mountains of Data

Mountains of dynamic information confront IT. One of the prominent areas is in the cloud scenario. Self-service provisioning has multiplied the amount of activities occurring outside of static processes. The new provisioning opportunities are beyond IT management, leaving IT with limited visibility to what happens there. For example, an organization sets up a private cloud with a dynamic management system, allowing self-service provisioning of servers for the testing team. Traditionally, testing professionals would have come to IT and request an environment, and IT would oversee and manage this entire process. Now the process is independent, when testing needs an environment, they just create it.

Today’s Approach: Static Processes Drive IT

IT Operations has been running on static processes and strict workflows. For instance, ITIL has a process for Change Management that works according to certain steps. There are also a set metrics for measuring performance, like the amount of changes that successfully went through or failed.

IT Ops can plan as much as possible, but it won’t ensure that everything will occur as planned.

For example, when IT implements an application upgrade, and makes changes to the environment, IT administration can go through an entire established process, and still the application doesn’t function as planned. IT managers check the processes that the upgrade went through, yet still performance lags. Then they need to go into the fine, granular details and see every step, identifying the make-up of even minor changes, seeing how it was deployed to all the servers, what is the consistency between servers, have there been additional interference to the servers. They need to take this enormous amount of data – configuration and granular changes – and pinpoint what was the root cause.

Workflow-driven Management Processes

Static processes operate through workflows. The workflow only supports part of the process but there are so many things surrounding the workflow, happening outside the workflow. Business demands can force shortcuts to be taken. Steps in the workflow can be skipped in order to get immediate approval, even omitting the test stage.

Workflows Create False Security

Even when processes are enforced, like having registrations as part of the workflow management, this creates the belief that everything has been solved. There is no organization that can claim they operate completely within the bounds of established processes and approvals.

This situation creates a sense of false security that IT is on top of all the changes. IT Ops can think that everything works perfect and then the organization religiously adheres to their processes, relying on CMDB systems and workflows, ultimately undermining operations.

A Shift in Paradigm to Analytics Driven Management

Neurologists will explain that the brain has two distinct hemispheres. The right side of the brain collects information, while the left side is cognitive and analyzes this information, translating all of the sensory input into usable data.

This is really the same model for today’s IT organization, where operations need to know what is happening now. IT Ops can find itself stuck, trying to adjust static processes while keeping track of and handling dynamic events, and then getting caught off-guard when issues arise. The solution is to approach this situation with dynamic analytics, for dealing with all the changing data, and to see what is really happening. This goes beyond those few designated indicators that were usually watched, rather IT Ops needs Analytic Driven Management, similar to how business has adopted BI, extracting actionable information out of mountains of data to help decision makers respond efficiently.

About Sasha Gilenson

Sasha Gilenson is Founder and CEO of Evolven. Prior to founding Evolven in 2007, he spent 13 years with Mercury Interactive (acquired by HP), managing the QA organization and participating in establishing Mercury Interactive's Software as a Service (SaaS). Sasha played a key role in the development of Mercury Interactive's worldwide Business Technology Optimization (BTO) strategy and drove field operations of the Wireless Business Unit, all while taking on the duties as the Mercury Interactive's top "guru" in quality processes and IT practices domain.

Related Links:

www.evolven.com

Hot Topics

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...