This Is Your Mainframe, All Grown Up

Dennis O'Flynn

During my IT career, I've found myself in various roles across multiple platforms. In that time, there's been one constant that I've observed: There is often a disconnect between different IT teams and across environments. We need to work to bridge those gaps, particularly in the evolving relationship between the mainframe and distributed worlds.

Over time, I've seen a lot of change in the mainframe landscape, though probably none as seismic as its recent integration with modern technologies such as mobile. This need to accommodate distributed, open systems (systems of engagement) alongside the traditional mainframe environment (systems of record) creates a larger need – to bridge that long-standing gap between mainframe and distributed teams.

Your mainframe must now operate across the enterprise, in order to interact directly with users. And that's not going to change anytime soon. And so navigating that divide is no longer an option – it has become a business imperative.

This is the new normal of mainframe.

But how are today's IT organizations troubleshooting enterprise application performance problems?

And managing mainframe resources to ensure efficiency?

In many cases, not very well, with days, weeks, even months, spent in war rooms. Plus, many organizations still approach the mainframe and distributed environments as separate worlds. Given the inter-related nature of today's enterprise, this approach is no longer effective. Now, mainframe and distributed teams need a shared view of IT and must communicate on the same level.

So we thought, in times like these, it would be helpful to have a new maturity model, a guide to helping organizations improve processes amid change. We enlisted the help of Alan Radding, veteran IT journalist and blogger. Together, we created a model that incorporates new mainframe roles and workloads alongside open systems, such as cloud and mobile, while encompassing new tools to address management and operations in this new environment.

With this model we're hoping to help IT organizations improve application performance management – plus the management of mainframe costs – as distributed and mainframe systems continue to converge.

The new model defines the following five categories for maturity across your enterprise, from the hardware and software you're employing to the way your organization is structured and how your teams interact:

- Application Technology

- Mainframe Attributes

- Organization

- Performance Technology

- Process

The 5 levels of maturity that you see in the model range from highly siloed and divided IT organizations (ad hoc), to highly integrated enterprises that effectively support and enhance the business (business revenue-centric).

Of course, there are many challenges to achieving enterprise maturity, such as:

- facing resistance to change

- changing skills as experienced mainframers retire

- management visibility of the expanded IT infrastructure

- end-user engagement

- increasingly complicated troubleshooting

But the end result is definitely worth it. By achieving enterprise maturity, you can ensure that your mainframe is more than just a legacy system. And especially through integration with the distributed side, it can drive your business forward.

Dennis O'Flynn is Director of Product Management for Compuware's Mainframe Solutions Business Unit.

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This Is Your Mainframe, All Grown Up

Dennis O'Flynn

During my IT career, I've found myself in various roles across multiple platforms. In that time, there's been one constant that I've observed: There is often a disconnect between different IT teams and across environments. We need to work to bridge those gaps, particularly in the evolving relationship between the mainframe and distributed worlds.

Over time, I've seen a lot of change in the mainframe landscape, though probably none as seismic as its recent integration with modern technologies such as mobile. This need to accommodate distributed, open systems (systems of engagement) alongside the traditional mainframe environment (systems of record) creates a larger need – to bridge that long-standing gap between mainframe and distributed teams.

Your mainframe must now operate across the enterprise, in order to interact directly with users. And that's not going to change anytime soon. And so navigating that divide is no longer an option – it has become a business imperative.

This is the new normal of mainframe.

But how are today's IT organizations troubleshooting enterprise application performance problems?

And managing mainframe resources to ensure efficiency?

In many cases, not very well, with days, weeks, even months, spent in war rooms. Plus, many organizations still approach the mainframe and distributed environments as separate worlds. Given the inter-related nature of today's enterprise, this approach is no longer effective. Now, mainframe and distributed teams need a shared view of IT and must communicate on the same level.

So we thought, in times like these, it would be helpful to have a new maturity model, a guide to helping organizations improve processes amid change. We enlisted the help of Alan Radding, veteran IT journalist and blogger. Together, we created a model that incorporates new mainframe roles and workloads alongside open systems, such as cloud and mobile, while encompassing new tools to address management and operations in this new environment.

With this model we're hoping to help IT organizations improve application performance management – plus the management of mainframe costs – as distributed and mainframe systems continue to converge.

The new model defines the following five categories for maturity across your enterprise, from the hardware and software you're employing to the way your organization is structured and how your teams interact:

- Application Technology

- Mainframe Attributes

- Organization

- Performance Technology

- Process

The 5 levels of maturity that you see in the model range from highly siloed and divided IT organizations (ad hoc), to highly integrated enterprises that effectively support and enhance the business (business revenue-centric).

Of course, there are many challenges to achieving enterprise maturity, such as:

- facing resistance to change

- changing skills as experienced mainframers retire

- management visibility of the expanded IT infrastructure

- end-user engagement

- increasingly complicated troubleshooting

But the end result is definitely worth it. By achieving enterprise maturity, you can ensure that your mainframe is more than just a legacy system. And especially through integration with the distributed side, it can drive your business forward.

Dennis O'Flynn is Director of Product Management for Compuware's Mainframe Solutions Business Unit.

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Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

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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 ...

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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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

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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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

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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 ...

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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.