Optimizing Application Performance with Change Management Improvements - Part 2

Mike Cuppett

This blog is an excerpt from DevOps, DBAs, and DBaaS by Mike Cuppet.

Start with Optimizing Application Performance with Change Management Improvements - Part 1

Yes, There Really Is a Problem

It is not that we do not believe user-reported information; it is just that experience tells us that other factors can be in play that make it necessary to get the full representation of the problem. One user would complain several times a week about application slowness, which was causing the person's performance metrics to drop. Upon investigation using a packet capture tool, it was determined that the live video streaming to the user's computer was causing the application slowness. This person was advised to stop the streaming and given the heads up that the company could "see" everything. Nothing illegal was happening, but complaining about self-inflicted impaired performance caused by news/entertainment traffic does not boost careers if that information is shared.

Continuing with our hypothetical problem: the user-side investigations recorded slowness consistently in the 5–17 seconds range, with very few outliers, which narrows the actual slowness impact significantly. If you are lucky, the captures you already have point to a single call that represents the majority of the slowness, allowing immediate focus on what is likely the root cause.

As member of a DevOps IT shop, you know that software releases occur nightly. Unfortunately, the users did not report the problem immediately, making it difficult to establish when the problem was introduced, (except that everything seemed to be good a few weeks ago; and, by the way, the problem occurs at different times of the day; otherwise, performance is acceptable). The release report shows at least five changes that may have impacted this functionality: four were implemented successfully, and one had to be rolled back with no root cause documented. Here, the binary release check has failed the organization. Release success or failure does not communicate information needed by the business or IT. Code that is successfully deployed with functionality validated by a tester does not tell the entire story (for example, performance degradation being introduced). DevOps testing purposely initiates more comprehensive answers. Excessive testing vets the software thoroughly and automatically, making it feasible to include tests designed to measure performance. It gives the green light only on performance that matches or is faster than a predefined value or the previous code version timing.

As DevOps teams "shift-left" and work in conjunction with business leaders as product managers, IT (now DevOps) truly becomes partners with the business. The "IT alignment to the business" goal included in the annual IT strategy deck for the last decade becomes obsolete. The perceived (or actual) misalignment was not only because the business teams did not understand what IT really did, other than spending offensively huge chunks of money to drive business operations, IT also wholly failed to come to the table as a business partner; instead remaining aloof and detached from everything but technology.

Thirty years ago, IT, MIS, or data processing (whatever the name) was given the mission of finding ways to complete work faster than teams of people could by having computers do mundane, repeatable tasks. Ironically, DevOps in many ways reaches back 40 years to repeat the tactical execution of having computers do mundane tasks: repetitive code testing, deployments, infrastructure as code, and more. Between then and now, far too many manual steps were added to processes that now need to be remediated. Forty years ago, computer work likely resulted in teams of people losing their jobs, but DevOps does not have the same mandate as in the data processing years. Instead, highly skilled engineers and programmers are freed from repetitive tasks and allowed to partner with the business to generate and implement game-changing technologies and applications.

DevOps wants and needs to shift talented, intelligent, experienced staff into roles that deliver measurable benefits for the company. Repeatable tasks can be done much faster by computers, but computers do not generate ideas. Computers running data analytics programs churn through data millions of times faster than humans, but computers still do not have the capability to find answers in the data, interpret the data, or act on the data like people do. People assimilate varying data points to produce value in new ways. DevOps needs people to create opportunities to help the business leapfrog competitors.

It is not intended to get rid of people; instead, it wants to make people more effective and focused on executing business strategies, not hampered by mundane tasks. Accomplishments have moved from "Designed a new algorithm for . . ." to "Improved customer experience . . . reduced costs . . . implemented a new revenue channel . . ."

DBAs and DevOps teams should take a positive stance and attitude toward the goals of Agile and DevOps, knowing that each person's impact on the organization can make tremendous strides to create better customer experiences and software products, and continually improve business processes, all with prospective top- and bottom-line impacts.

DevOps Answers

Change management analysis in DevOps extends beyond binary conclusions to business impact statements. Reporting successful or failed statuses alone shifts to informative, customer-centric statuses such as the following:

• "Change 123 implementing function A successfully reduced execution time 40%; now averaging 7 milliseconds per call."

• "The change to reorganize table ABC successfully reduced report execution time, allowing the business to meet contractual requirements."

• "Change 456 failed and was rolled over successfully with change 512. Testing for change 456 did not include a critical data test; later found and tested for change 512, which allowed the failure to advance. Teams had rectified, tested, and implemented the needed test earlier this week, having change 512 already in the pipeline. The 512 push completed successfully within the change window, eliminating the risk."

DevOps' fail fast edict can really benefit the company by progressing software products continuously and without having laborious rollbacks, rework, retests, and reimplementation. In the previous third scenario, the DevOps team knows that a communication was missed because change 456 should have never made it to the release stage, let alone production.

So as change management communications pivot from mundane status updates to business impact updates, opportunities to improve application performance become more apparent. Moving from a message that the code was implemented successfully to a message that the code decreased customer query time by 67% tells a better story. There is a large chasm between code that works and code that works and executes expectantly fast while generating an audit trail. Adding a new feature that performs poorly is not really a feature — it is a bug and a frustration for customers. Adding a feature that is expected to increase mobile app usage 400% without increasing infrastructure resources is not a feature, but a colossal failure. The DevOps movement provides the needed tactical response with infrastructure as code. When traffic is expected to spike, adding resources to existing virtual hosts or spinning up additional hosts with a button click or two simplifies infrastructure readiness and resiliency.

Read Optimizing Application Performance with Change Management Improvements - Part 3

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Optimizing Application Performance with Change Management Improvements - Part 2

Mike Cuppett

This blog is an excerpt from DevOps, DBAs, and DBaaS by Mike Cuppet.

Start with Optimizing Application Performance with Change Management Improvements - Part 1

Yes, There Really Is a Problem

It is not that we do not believe user-reported information; it is just that experience tells us that other factors can be in play that make it necessary to get the full representation of the problem. One user would complain several times a week about application slowness, which was causing the person's performance metrics to drop. Upon investigation using a packet capture tool, it was determined that the live video streaming to the user's computer was causing the application slowness. This person was advised to stop the streaming and given the heads up that the company could "see" everything. Nothing illegal was happening, but complaining about self-inflicted impaired performance caused by news/entertainment traffic does not boost careers if that information is shared.

Continuing with our hypothetical problem: the user-side investigations recorded slowness consistently in the 5–17 seconds range, with very few outliers, which narrows the actual slowness impact significantly. If you are lucky, the captures you already have point to a single call that represents the majority of the slowness, allowing immediate focus on what is likely the root cause.

As member of a DevOps IT shop, you know that software releases occur nightly. Unfortunately, the users did not report the problem immediately, making it difficult to establish when the problem was introduced, (except that everything seemed to be good a few weeks ago; and, by the way, the problem occurs at different times of the day; otherwise, performance is acceptable). The release report shows at least five changes that may have impacted this functionality: four were implemented successfully, and one had to be rolled back with no root cause documented. Here, the binary release check has failed the organization. Release success or failure does not communicate information needed by the business or IT. Code that is successfully deployed with functionality validated by a tester does not tell the entire story (for example, performance degradation being introduced). DevOps testing purposely initiates more comprehensive answers. Excessive testing vets the software thoroughly and automatically, making it feasible to include tests designed to measure performance. It gives the green light only on performance that matches or is faster than a predefined value or the previous code version timing.

As DevOps teams "shift-left" and work in conjunction with business leaders as product managers, IT (now DevOps) truly becomes partners with the business. The "IT alignment to the business" goal included in the annual IT strategy deck for the last decade becomes obsolete. The perceived (or actual) misalignment was not only because the business teams did not understand what IT really did, other than spending offensively huge chunks of money to drive business operations, IT also wholly failed to come to the table as a business partner; instead remaining aloof and detached from everything but technology.

Thirty years ago, IT, MIS, or data processing (whatever the name) was given the mission of finding ways to complete work faster than teams of people could by having computers do mundane, repeatable tasks. Ironically, DevOps in many ways reaches back 40 years to repeat the tactical execution of having computers do mundane tasks: repetitive code testing, deployments, infrastructure as code, and more. Between then and now, far too many manual steps were added to processes that now need to be remediated. Forty years ago, computer work likely resulted in teams of people losing their jobs, but DevOps does not have the same mandate as in the data processing years. Instead, highly skilled engineers and programmers are freed from repetitive tasks and allowed to partner with the business to generate and implement game-changing technologies and applications.

DevOps wants and needs to shift talented, intelligent, experienced staff into roles that deliver measurable benefits for the company. Repeatable tasks can be done much faster by computers, but computers do not generate ideas. Computers running data analytics programs churn through data millions of times faster than humans, but computers still do not have the capability to find answers in the data, interpret the data, or act on the data like people do. People assimilate varying data points to produce value in new ways. DevOps needs people to create opportunities to help the business leapfrog competitors.

It is not intended to get rid of people; instead, it wants to make people more effective and focused on executing business strategies, not hampered by mundane tasks. Accomplishments have moved from "Designed a new algorithm for . . ." to "Improved customer experience . . . reduced costs . . . implemented a new revenue channel . . ."

DBAs and DevOps teams should take a positive stance and attitude toward the goals of Agile and DevOps, knowing that each person's impact on the organization can make tremendous strides to create better customer experiences and software products, and continually improve business processes, all with prospective top- and bottom-line impacts.

DevOps Answers

Change management analysis in DevOps extends beyond binary conclusions to business impact statements. Reporting successful or failed statuses alone shifts to informative, customer-centric statuses such as the following:

• "Change 123 implementing function A successfully reduced execution time 40%; now averaging 7 milliseconds per call."

• "The change to reorganize table ABC successfully reduced report execution time, allowing the business to meet contractual requirements."

• "Change 456 failed and was rolled over successfully with change 512. Testing for change 456 did not include a critical data test; later found and tested for change 512, which allowed the failure to advance. Teams had rectified, tested, and implemented the needed test earlier this week, having change 512 already in the pipeline. The 512 push completed successfully within the change window, eliminating the risk."

DevOps' fail fast edict can really benefit the company by progressing software products continuously and without having laborious rollbacks, rework, retests, and reimplementation. In the previous third scenario, the DevOps team knows that a communication was missed because change 456 should have never made it to the release stage, let alone production.

So as change management communications pivot from mundane status updates to business impact updates, opportunities to improve application performance become more apparent. Moving from a message that the code was implemented successfully to a message that the code decreased customer query time by 67% tells a better story. There is a large chasm between code that works and code that works and executes expectantly fast while generating an audit trail. Adding a new feature that performs poorly is not really a feature — it is a bug and a frustration for customers. Adding a feature that is expected to increase mobile app usage 400% without increasing infrastructure resources is not a feature, but a colossal failure. The DevOps movement provides the needed tactical response with infrastructure as code. When traffic is expected to spike, adding resources to existing virtual hosts or spinning up additional hosts with a button click or two simplifies infrastructure readiness and resiliency.

Read Optimizing Application Performance with Change Management Improvements - Part 3

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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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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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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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 ...

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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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...