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The New Convergence: How Value Stream Mapping is Rewiring Product, Platform and DevOps for 2026 and Beyond
For years, organizations have tried to improve speed, quality, and customer experience by layering frameworks on top of frameworks—Agile for iteration, DevOps for automation, ITIL for service stability, TOGAF for architecture, Lean for waste reduction. Each helped, but none solved the core problem: Work still doesn’t flow.

In 2026, the most competitive organizations are converging on a different approach—one that doesn’t start with frameworks at all. It starts with value streams.

Value Stream Mapping (VSM) has re-emerged as the connective tissue that unifies product management, engineering, operations, and architecture into a single, end‑to‑end system of flow. And the results are no longer theoretical. Across industries, and while only as a generalization, many tools, such as DORA et al, teams using VSM as their operating backbone are seeing results such as:
- 25–30% faster time-to-market
- 20–40% reductions in waste and rework
- 2–4x increases in deployment frequency
- Material reductions in technical debt and operational drag
These aren’t magic numbers; they’re the predictable outcome of making work visible, measurable, and accountable across the entire product lifecycle. We know this.

The Modern Lean Product Environment: Where Flow Becomes Strategy

The shift underway is bigger than Agile or DevOps alone. Organizations are moving from project-centric planning to continuous product flow, where teams optimize for:
- Cross-functional collaboration instead of siloed handoffs
- Rapid iteration instead of heavy upfront planning
- Customer-centric learning instead of assumption-driven roadmaps
- Continuous improvement instead of episodic transformation
Lean principles are no longer a manufacturing artifact—they’re the backbone of modern digital operations. And VSM is the mechanism that makes Lean real in complex, multi-team environments.

Why VSM Is the Missing Link Between Agile, DevOps, TOGAF and ITIL 5

Every framework solves a piece of the puzzle:
- Agile accelerates iteration
- DevOps automates delivery
- TOGAF defines architectural guardrails
- ITIL 5 governs service reliability and digital product and service management practices
- Lean eliminates waste
See also;
👉 ITIL Foundation (Version 5) from PeopleCert for the digital product and service management language,
👉TOGAF Standard, 10th Edition – Business Architecture for value streams as a core Business Architecture element, and
👉DORA’s Value stream mapping for software delivery guide for the software-delivery flow.

But none of them describe how value actually flows across the organization.

VSM does.

By mapping the end-to-end flow—from customer need to deployed value—organizations finally see the real bottlenecks: Decision latency, cross-team wait states, redundant approvals, manual testing, unclear ownership, and invisible technical debt.

When teams harmonize their frameworks around a shared value stream, service delivery efficiency can improve materially, and cross-team alignment often improves as well.

MVP + VSM: The Shortest Path to Customer Value

The fastest organizations aren’t just shipping faster — they’re learning faster.

MVP thinking, when paired with VSM, creates a disciplined mechanism for identifying the shortest path to first value. Teams that focus on delivering only the essential, value-critical functionality see:
- 30–50% reductions in time-to-first-value, based on field observations and practitioner reports
- Faster validation of assumptions
- Less investment in features users don’t adopt
VSM makes MVP execution concrete by visually mapping the pipeline, exposing delays, and clarifying what truly matters for early learning.

Feedback Loops as a First-Class Architecture Concern

Persona-driven feedback loops are no longer a UX nicety — they’re a product architecture requirement, for example, telemetry → hypothesis → release decision → adoption signal.

Organizations that systematically connect persona pain points to telemetry, in-app feedback, and post-release signals see:
- ~20% increases in feature adoption, especially in semantic analysis-driven ChatOps and channel management contexts
- Higher retention due to engagement
- Faster iteration cycles due to transparency and shared visibility into common goals
VSM highlights where feedback is missing, delayed, or ignored—and forces teams to close the loop. I have personally seen dozens of times, literally, with relatively simple mitigation and remediation, upon awareness.

Future-State Mapping: The Fastest Path to Deployment Excellence

Current-state maps expose the pain. Future-state maps define the fix.

Teams that redesign their deployment pipelines using VSM routinely achieve:
- 40–60% reductions in deployment lead time
- 2–4x increases in release frequency
- Dramatic reductions in manual work and cross-team handoffs
These gains aren’t theoretical—they’re the natural result of eliminating wait states, automating test-to-deploy steps, and shifting from batch releases to continuous flow. This is what DORA is all about and has covered this area quite well! See DORA’s 2024 report and DORA’s software delivery metrics guidance for deployment frequency and lead time for the latest statistics and information.

Technical Levers: Canary Releases, Feature Flags, Digital Twins

Modern value streams are increasingly defined by technical patterns that reduce risk while increasing speed:
- Canary releases reduce rollback rates by up to 70%
- Feature flags decouple deployment from release, enabling instant rollback
- Digital twins simulate operational impact before changes hit production
Together, these techniques create a safer, faster, more resilient flow of value.

Data-Driven Value Rationalization: Killing Zombie Features

The future of product development is ruthlessly data-driven.

Organizations that adopt hypothesis-driven development, usage analytics, and value realization dashboards consistently:
- Improve decision quality by ~25%
- Reduce investment in low-value features
- Eliminate “zombie features” that drain capacity
- Cut technical debt by up to 40%
This is where product, engineering, and finance finally converge: Value becomes measurable, visible, and accountable. We cover this extensively over at Flowtopia.io and we also welcome direct inquiries to discuss further.

The Real Transformation: From Analog Thinking to Digital Flow

Most organizations still manage digital products with analog mental models—projects, phases, committees, and static plans. But digital work behaves more like microservices: Dynamic, instance-specific, and constantly evolving.

VSM is the operating system that makes this manageable.

It gives leaders a deterministic way to:
- See the system
- Measure the system
- Improve the system
- Align teams around value
- Reduce waste and friction
- Accelerate learning and delivery
In a world where speed, adaptability, and customer experience define competitiveness, VSM is no longer optional. It’s the backbone of modern product and platform operations.
June 22, 2026
DevOps Didn’t Fail — We Just Finally Gave it the Tools it Deserved

Every once in a while, our industry needs a good, sharp elbow to the ribs. Not a cheap shot. A wake-up jab. That’s exactly what Charity Majors delivered in her recent Honeycomb blog post, “You Had One Job: Why Twenty Years of DevOps Has Failed to Do It.”

If you’ve known Charity as long as I have, you know this is her superpower. She’s never been shy about an opinion. She’s dynamic, fearless, and above all else, really sharp. I admire and respect her tremendously, and I genuinely enjoy having her involved in Techstrong activities. She makes every conversation better by refusing to let it get comfortable.

Her argument, boiled down, is this: DevOps set out to do one core thing, get developers to care about their software in production, and by that measure, it failed. Not dead. Not useless. But failed at its primary mission. That’s a provocative take, and like most good provocations, there’s a lot in it I agree with and some I don’t.

Let’s start with the easy part. DevOps has been far from perfect. Anyone who says otherwise is selling something. Too often, “DevOps” became a job title, a tooling checklist, or worse, a rebranding exercise for the same old silos with better conference swag. And yes, getting developers to care about what happens after the code hits Git, all the way through deployment and into real-world behavior, remains uneven at best. Charity is right to call that out.

But here’s where I start to push back, in true Shimmy fashion. Whether DevOps “failed” depends entirely on where you’re standing and what yardstick you’re using.

One of the enduring, almost comical truths of DevOps is this: Developers often think DevOps is too Ops-centric. Ops folks think DevOps is too Dev-centric. Both sides are convinced the other one got the better end of the deal. You know what that usually means? It’s probably a decent deal. Nobody got everything they wanted, which is often the only way progress happens.

The truth, as usual, lives somewhere in the messy middle.

Ask an Ops person what DevOps success looks like, and you’ll hear something very close to what Charity is advocating: Developers who care deeply about reliability, performance, and behavior in production. Ask security teams and you’ll get a different answer. For them, success is when everyone shares responsibility for security, when “shift left” actually shifts something besides PowerPoint slides.

Ask developers, and many will tell you DevOps succeeded when it removed friction. When it let them automate the non-coding work so they could, you know, actually write code. Platform engineers will talk about internal developer platforms, golden paths, and guardrails that let teams move faster without blowing themselves up. SREs, data scientists, and release engineers all bring their own definitions to the table.

That’s not a bug in DevOps. That’s the thing.

DevOps has always been slippery. It resists clean definitions. It refuses to sit still long enough for a standards body to nail it down. At its core, DevOps was never about a single outcome. It was about breaking down silos, increasing communication, and getting more people aligned around delivering value. Success, in that sense, was always going to be plural, not singular.

Charity is absolutely right about one thing that sits at the heart of her argument: Feedback loops matter. If developers don’t see what happens to their code in the wild, they can’t get better at building resilient systems. This is where observability enters the story, and it’s no accident that Charity has been one of its most forceful champions.

But here’s an uncomfortable truth we don’t say often enough. When DevOps first emerged, observability, frankly, kind of sucked. We didn’t even use the word much back then. The tools were blunt. The data was overwhelming but not insightful. We collected logs, metrics, and traces mostly because we could, not because they reliably told us what to do next.

Charity is right again when she argues that without advanced analytics, without machine learning, without the ability to actually make sense of massive volumes of data, observability can’t fulfill its promise. You can’t make wine before its time. You are only as good as your tools.

And what a time to be alive.

AI has changed the equation, not just a little, but fundamentally. It has turned observability from a passive recording system into an active partner in understanding complex systems. Suddenly, those feedback loops Charity talks about are not just possible, they’re actionable. It’s no coincidence that OpenTelemetry and other observability projects are dominating conversations in the CNCF and across the market. The ecosystem finally caught up to the ambition.

But AI’s impact doesn’t stop there. It’s transforming how we write code, test code, secure code, and operate systems. It’s collapsing feedback cycles that used to take weeks into minutes. It’s making it easier for developers to see downstream consequences and for Ops teams to surface upstream insights. In other words, it’s doing exactly what DevOps always wanted to do, just with far better leverage.

So if DevOps wasn’t fully delivering on its promise twenty years ago, that doesn’t make it a failure. It makes it early.

As Andrew Clay Shafer famously said, “The DevOps you get is the DevOps you deserve.” I’d add this: The DevOps you get is also the DevOps your tools allow. DevOps needed AI. It needed better data. It needed observability that could actually observe, not just collect.

From that vantage point, DevOps didn’t fail. It evolved. It struggled. It disappointed at times. But it also laid the cultural and organizational groundwork that makes today’s AI-powered tooling meaningful. Without DevOps, AI would just be another shiny object bolted onto broken processes.

Charity’s essay is valuable precisely because it forces us to confront our assumptions. It reminds us that slogans aren’t outcomes and that intent doesn’t equal impact. On that, we agree completely.

Where we differ is on the verdict. Failure implies finality. DevOps was never finished. It’s still becoming. And now, finally, it has the tools it always needed to be what so many of us hoped it could be.

That doesn’t mean the job is done. It means the job just got interesting.

January 20, 2026
SRE in the Age of AI: What Reliability Looks Like When Systems Learn
With organizations increasingly embedding artificial intelligence (AI) and machine learning (ML) models into production systems, the role of site reliability engineering (SRE) is evolving. Traditional reliability practices, such as monitoring, incident response and service-level objectives (SLOs), remain vital, but must adapt when systems are no longer purely deterministic. When software ‘learns’, behaves dynamically and adapts over time, what does reliability mean?

In this article, we explore how SRE can shift from guarding fixed systems to collaborating with adaptive ones, define new metrics and workflows and build reliability in the era of AI.

From Deterministic Systems to Learning Systems

In classical SRE practice, a service is defined by infrastructure, code and defined behavior: You can predict failure modes, set error budgets and measure latency, availability and throughput. SLOs and error budgets work because system behavior is fairly static.
When an AI/ML model is introduced, say for fraud detection, recommendation engines or auto-scaling decisions, the system begins to adapt. Model weights change, data drifts, input distributions evolve and decision boundaries shift. This introduces new types of risks:
- Model Drift and Concept Drift: Over time, the model’s input distributions or target definitions change, so what was reliable yesterday may fail today.
- Emergent Behavior: The model may interact with other systems or humans in unexpected ways. Feedback loops can cause cascading effects.
- Opaque Failure Modes: Unlike traditional software bugs, model mispredictions or biases may not trigger obvious alerts until significant damage occurs.
- Operational Dependencies: The ML pipeline adds data collection, feature engineering, retraining and model deployment, all of which become part of ‘the service’ that SRE must manage.
Thus, SRE in AI-enabled systems must broaden its scope: Reliability is no longer just infrastructure uptime and latency; it is also model correctness, drift detection, data pipeline health, feedback loop monitoring and business outcome alignment.

Defining New Reliability Metrics for Learning Systems

To adapt, SRE teams should consider additional metrics beyond the standard ones. Examples include:

1. Model Performance Stability
- Track metrics such as accuracy, precision/recall, F1-score (for classification) or RMSE/MAPE (for regression) over time.
- Monitor for deviation or degradation in model performance and set thresholds or SLOs for performance drift.
2. Data Pipeline Reliability
- Measure the freshness of data, the completeness of feature sets and the latency of data ingestion.
- Provide alerts when data lag or missing features exceed thresholds, since the model depends on timely, clean data.
3. Feedback Loop Latency and Correctness
- If the model’s outputs feed back into the system (e.g., auto-tuning, auto-scaling, personalized UX), measure how long feedback takes and whether corrections happen as expected.
- Track unintended loops (e.g., model reinforces bias or user behavior based on its own output).
4. Business Outcome Alignment
- Tie model outputs to business KPIs (e.g., conversion rate, churn reduction and false-positive reduction).
- Monitor when model outcomes diverge from business goals — a sign that the reliability of the ‘learning system’ is drifting.
5. Infrastructure/Model Version Coupling
- Measure how often model versions change, how many deployments of the ML pipeline are done and what are their success/failure rates.
- Consider an SLO for ‘model deployment failure rate’ similar to software release failure.
By integrating these metrics into the SRE dashboard, reliability expands to a broader conversation — from merely “Is the service up?” to “Is the system still learning correctly, safely and effectively?”

Adjusting SRE Workflows for AI-Enabled Services

SRE workflows must evolve to manage the new complexity of learning systems. Here are some recommended adjustments:
- Incident Response Ramps Out to ‘Model Incidents’
  Traditional incidents include high latency, error rate spikes and infrastructure failures.
  In AI systems, incidents might include model drift beyond threshold, data pipeline interruption, inference latency degradation or feedback loop explosions. SRE teams must define playbooks for model-specific incidents: Rollback model, trigger retraining, quarantine features and revert to safe mode.
- Chaos-Testing for Learning Systems
  Just as we perform chaos engineering for infrastructure, site reliability engineers can implement chaos experiments for ML: Simulate data drift, drop features, inject corrupted data and simulate delayed retraining. Observing system behavior under ‘impaired learning’ conditions helps build resilience.
- Service Ownership Expands
  Model owners, data engineers, infrastructure engineers and site reliability engineers must collaborate more tightly. The SRE team’s charter now includes observability for ML pipelines, not just runtime infrastructure. Create shared responsibilities and clear handoffs: Who monitors feature reliability? Who owns model validation? How are alerts escalated?
- Safeguards for Learning Loops
  Set safe-mode fallbacks — if a model behaves unexpectedly (e.g., too many false positives), the system should revert to a default, non-learning behavior. Site reliability engineers should build guardrails: Drift detectors, anomaly detection on model outputs and threshold alarms. Having rollback plans for models is just as important as blue/green for deployments.
Cultural and Organizational Considerations

Reliability in the age of AI isn’t purely a technical shift; it’s also a cultural one. Some key considerations are as follows:
- Education and Literacy: Site reliability engineers must become literate in ML fundamentals (feature drift, bias and training pipelines). Data scientists must understand operational concerns (latency, monitoring and SLOs).
- Cross-Discipline Alignment: Break down silos between ML engineering and SRE/DevOps. Create joint objectives around system reliability, not just model accuracy or infrastructure uptime.
- Clear Accountability: Define ownership of ML artifacts, data pipelines and model runtimes. Who owns ‘the model being live for >24 hours without retraining’? Who owns ‘feedback loop health’?
- Incremental Adoption: Not all systems need full ML life cycle reliability on day one. Begin by monitoring data pipelines, model metrics and feature freshness and scale up as maturity grows.
Looking Ahead: What SRE Means in 2026 and Beyond

As we move further into AI-native systems, some emerging trends will shape reliable operations:
- Auto-Remediation of Pipelines: ML systems may autonomously retrain and deploy models. Site reliability engineers will need to monitor these autopilot loops and ensure that they don’t spin out of control.
- Explainability-Driven Alerts: Systems will detect when model decisions deviate from expected patterns and automatically provide alerts for ‘unexpected learning’. Site reliability engineers will need to understand explainability metrics and attach them to reliability dashboards.
- Self-Healing Model Architectures: Systems may switch architectures automatically when drift is detected. Site reliability engineers will transition to monitoring higher-level meta-services (model orchestrator health, ensemble diversity and fallback routing).
- Ethics and Reliability Convergence: Reliability won’t only be about uptime and accuracy, but also about fairness, bias and transparency. Incidents might include ‘model exhibits bias’ or ‘learning loop reinforces negative feedback’. SRE teams will require ethics playbooks alongside incident playbooks.
Conclusion

The role of site reliability engineers is evolving from being the guardians of static services to becoming navigators of dynamic, learning systems. Reliability in the age of AI demands new metrics, new workflows and new cultural alignment. By adapting our SLO frameworks, embracing data-centric observability and designing safe learning loops, site reliability engineers can ensure that systems don’t just run — they learn responsibly, reliably and deliver business value.
Cloud engineers, SRE practitioners and platform teams: Start by adding model health to your reliability dashboards, review your incident playbooks for ML scenarios and engage your data science teams in defining what ‘good’ looks like when systems learn. The future of reliable software is not just available, it’s adaptive.
November 19, 2025
From Code to Confidence: Building AI Apps That Earn User Trust
As a developer, you’ve probably felt the excitement of deploying your first AI-powered feature. The model performs exceptionally well in testing with solid accuracy metrics, and stakeholders are delighted. Then, reality hits: Users start complaining about biased recommendations, confusing outputs or worse — publicly calling out your application for generating harmful content.

A recent Applause survey found that 65% of users experienced issues with AI applications between January and March 2025, including bias, hallucinations and incorrect responses. Trust has officially become the new battleground for user experience, and traditional testing approaches are no longer sufficient for AI applications.

The good news? By shifting from assumption-driven development to deliberate, user-centered testing processes, you can build AI applications that don’t just work — they earn genuine user confidence.

Most developers approach AI testing in the same way they handle traditional software development: They write unit tests, check edge cases, validate outputs and ship the product. However, AI applications are fundamentally different. They’re probabilistic, not deterministic. They evolve with new data. Most critically, they interact with humans who judge them not just on functionality but on fairness, transparency and trustworthiness.

Many teams attempt to fill resource gaps with automated testing alone. Your recommendation engine might show 95% accuracy in your automated test suite, but real users from different demographic groups may experience vastly different outcomes. Your automated tests missed what matters most — how your AI behaves across the full spectrum of human diversity. This gap between lab performance and real-world trust is a business-critical issue that can sink your application before it gains traction.

Implement Human-Centered Testing From Day One

The solution isn’t more automated tests — it’s involving humans into your testing process from the beginning. Your AI is only as trustworthy as the data and people involved in its training. If your training data comes from a narrow slice of users, your AI will reflect those limitations.

Build testing communities that accurately reflect your actual user base, encompassing diverse ages, backgrounds, languages and accessibility needs. Don’t rely solely on your internal team or contractors who share similar perspectives. Establish regular testing cycles in which human evaluators from diverse demographic groups assess your AI’s outputs for fairness. Track metrics like response quality across user segments, not just overall accuracy.

Bias isn’t a one-time check — it’s an ongoing concern requiring continuous monitoring. As your model learns from new data, new biases can emerge or existing ones can amplify. Users don’t just want your AI to be right — they want to understand why it makes specific decisions. This is especially crucial for AI applications that affect essential life decisions, such as healthcare, finance or hiring.

Build explanation capabilities into your AI architecture from the outset. Test these explanations with real users to ensure they’re understandable, not just technically accurate.

Create Inclusive Feedback Loops

Traditional feedback loops in software development often miss the nuanced ways AI impacts different users. Creating inclusive feedback mechanisms requires intentional design.

Don’t just rely on star ratings or binary feedback. Collect qualitative insights through conversational interviews, demographic-specific focus groups and longitudinal studies that track how trust evolves. Create feedback channels that accommodate different communication preferences — some users prefer quick surveys, others seek detailed conversations and some express concerns through community forums.

Lab testing can’t replicate how users interact with AI in their daily lives. Background noise affects voice AI performance differently in a coffee shop than in a quiet office. Cultural context also influences how users interpret AI-generated content. Test your AI applications in the environments where they’ll be used and partner with diverse user communities for ongoing feedback.

Establish Continuous Trust Monitoring

Building trustworthy AI isn’t a sprint — it’s an ongoing commitment to improvement. Traditional software metrics such as uptime and response time are insufficient for AI applications. You need metrics that specifically track user trust and confidence.

Develop trust-specific KPIs such as user confidence scores, bias detection rates and explanation clarity ratings. Track these alongside your technical metrics. Your AI model should evolve based on real user feedback, not just algorithmic optimization. Create feedback loops where user insights about bias, fairness and trust directly inform your model training and fine-tuning processes.

Users are more forgiving of AI limitations when they understand them. Be upfront about what your AI can and can’t do, and how you’re working to improve it. The temptation is to iterate quickly and fix problems later, but trust issues compound rapidly — once users lose confidence in your AI, regaining it is difficult than building it right the first time.

Building trustworthy AI applications requires a fundamental shift in how you approach development and testing. Begin with these concrete actions:
- Audit your current testing approach. How diverse are your testers? Are you testing for bias and fairness or only for functionality?
- Establish human evaluation processes. Regularly identify ways to incorporate diverse human perspectives into your testing pipeline.
- Create multiple feedback channels. Develop various ways for users to share concerns about bias, fairness and trust.
- Define trust metrics. Establish KPIs that measure user confidence alongside technical performance.
The future belongs to AI applications that not only work well but work well for everyone. By embracing user-centered testing processes and building inclusive feedback loops, you’re not just shipping better software — you’re laying the foundation for AI applications that users will adopt and advocate for.

In AI development, confidence truly comes from quality, and quality comes from deliberately designing for trust from the outset.
November 6, 2025
DevOps Workflow: The Key Elements and Tools Involved
DevOps workflow is more than a work approach. It’s a corporate culture. It involves a range of practices that bridge software development (Dev) and IT operations (Ops). By doing so, it creates a uniform, continuous process without a gap between teams. As a result, it creates an environment with smooth collaboration, communication and shared responsibility.

In 2025, the global adoption rate of this culture is over 78%. Companies implement this methodology to ensure faster and more reliable delivery of services and software solutions with a higher quality of the end product.

If you’re also looking to adopt this philosophy or planning to start using services that work based on it, getting started can seem rather frustrating at first. This guide aims to help you by breaking down the modern DevOps workflow into the key components, practices and tools that need to be adopted for maximum smooth operations.

Continuous Development

The first essential element of the workflow is continuous development. It consists of two parts:
- Planning -The ongoing process of defining the quality factors and requirements for new solutions.
- Development – The continuous process of code writing, software development and unit testing to prepare solutions according to the existing plan.
In the regular work process, the planning and development stages are large and project-focused. In the development/operations culture, it’s an ongoing process. The development is handled in smaller cycles instead. And this helps ensure continuous software delivery and updates.

Continuous Integration

The next key component is DevOps continuous integration. CI is the process of continuous integration of code changes. The changes are usually produced by several contributors and frequently (often, several times a day) inserted into a central repository.

The most important element of this stage is automatic building and testing. CI implies that the solution is tested every time someone makes a change in the code. This practice allows smooth collaboration of several developers within a single project. It also keeps the codebase up to date, cuts down the cost of integration, and allows early bug detection and fixes.

Some of the most common DevOps workflow tools involved in CI include:
- GitHub Actions
- Jenkins
- GitLab CI
- CircleCI
Continuous Delivery and Deployment

In a DevOps workflow diagram, these stages mark the start of the operations process. This is where development and operations intersect.

It consists of two coexistent elements:
- Continuous delivery stands for making the code always ready for release after it has undergone the necessary changes and testing.
- Continuous deployment stands for automatic deployment of the delivered code. Simply put, it instantly goes into production when it’s stable enough, which ensures fast availability of new features and updates to users.
The CD process allows businesses to reduce manual intervention in the production and release processes, eliminating the need for scheduling when new features and releases will roll out. Respectively, it allows a faster time to market for each solution.

Some of the main DevOps automation tools used at this stage include Spinnaker, ArgoCD and Harness.

Infrastructure as Code

IaC is among the DevOps best practices. Its goal is to simplify the management of your company’s IT infrastructure. Simply put, IaC cancels out manual processes of provisioning and management of your networks, storage, servers, and other infrastructure elements and replaces them with automation. Instead of manual tasks, come code and configuration files.

By changing the way they create and maintain their environments, teams can eliminate the possibility of human-made errors. But that’s not the only benefit of this approach. It also creates safe and repeatable management procedures. These make it easier to scale projects. And it also offers greater reliability and quality.

Quality Assurance and Automated Testing

As was mentioned earlier, testing is a pivotal part of the CI and CD pipelines. And it’s also one of the major components of DevOps workflow automation. Automatic testing is applied throughout the entire CI/CD stages to assess the stability and performance of the solution after each change has been made. There are a number of test types that are involved in this process, including unit, integration, end-to-end, and regression testing. But that’s not all.

Beyond the software performance testing after code changes and pre-launch, many teams also integrate pre-release testing of their marketing efforts and SEO, in particular. What does that mean? You can use DevOps automation to run audits of your solution’s/app’s accessibility and SEO to see how well it will perform in SERPs. This is important to ensure that your software will be visible to potential users in search engines when it rolls out.

Furthermore, in today’s online landscape, it also makes pretty good sense to perform additional checks of your visibility in AI search. Since more and more people are using AI-powered engines and tools to research information, being included in generated results is a good way to secure overall visibility. You can do this with the help of a specialized AI search visibility tool.

All in all, using diverse DevOps workflow tools for automated testing will help you detect issues before you get into the production stage or release your solution. This way, you can safeguard UX and business KPIs.

Logging, Observability and Monitoring

Your workflow doesn’t really stop even when the solution is released. The post-production and release stage is all about ongoing assessment of the solution’s performance and improvement.

It consists of three elements that can work simultaneously:
- Monitoring is about tracking performance in real time.
- Logging stands for keeping a well-documented log of all events that require troubleshooting.
- Observability is about collecting deeper insights into the state and stability of the system through various logs, metrics and traces.
What is it all aimed at? It’s all about keeping an eye on any arising bugs and issues. This is crucial to respond to incidents quickly and secure an all-time positive UX.

Common DevOps tools leveraged at this stage include Grafana, Datadog, Prometheus and others.

Collaboration and Communication

As you now see, this culture is all about establishing a smooth and integrated work process that implies the performance of many simultaneous tasks by multiple contributors. Furthermore, it implies mutual and well-defined work of teams from different departments. Hence, this methodology is just as much about your in-house culture as it is about technical aspects of the product’s lifecycle.

There are two cultural aspects in the heart of an effective workflow:
- Collaboration
- Communication
Companies need to integrate effective collaboration solutions. These include shared dashboards, regular meetings, clear documentation and async communication. This should help ensure that everyone involved in the project is on the same page. It should also allow effective interactions across all team members, regardless of their schedules or time zones. This is particularly important for large teams.

To accomplish all of this, teams need to adopt efficient DevOps tools meant for collaboration. Currently, common picks include Jira, Trello, Slack, Microsoft Teams and others.

Security Integration

While DevOps is primarily focused on software quality and fast delivery, security is something that can easily slip through the cracks in the production process. In this case, leading companies bring together DevOps and security and turn their software supply chains into DevSecOps.

What does this mean? Speaking simply, this approach is based on the same fast-moving development and operations pipeline, but that also has mandatory security processes in it. This implies regular and automated vulnerability scans and dependency checks, as well as compliance automation and secret management. This helps detect security breaches early and ensure complete compliance to build and maintain user trust.

Continuous Improvement and Feedback Loops

Ongoing measurement and improvement are the two core principles of a DevOps culture. It’s important to understand that it’s not only about the delivery speed, but actually about positive UX and impeccable quality. That’s why the last two elements of a modern workflow under this culture are feedback loops and continuous improvement.

There are a few things involved in the process. First of all, feedback loops should be both internal and external. Internal loops contain regular and detailed feedback provided by in-house teams – including development, operations and Quality Assurance (QA). External feedback is collected from customer support logs and end-user telemetry (the data collected remotely from users’ devices and applications to assess real-world UX).

Both internal and external feedback loops have a shared goal—to detect issues and areas for improvement that need further attention. Such a continuous improvement approach allows for fast issue resolution, customer-centric development and improved UX.

DevOps automation is also present at this stage of the workflow. At this point, teams can use feature flagging platforms, such as LaunchDarkly. This platform allows automatic rollback of problematic features, has A/B testing functionality and easily integrates with your communication solutions, such as Slack.

Conclusion

DevOps is more than just a workflow organization pattern. It’s a culture built around firm principles of collaboration, communication, automation and continuous improvement. A combination of these principles allows us to create a smooth and efficient workflow that doesn’t just offer fast product delivery, but also helps ensure the best UX and constant growth.

With this guide, you should now have a better understanding of the key elements involved in this process, as well as the key DevOps workflow automation practices and tools. Use this knowledge to build a collaborative environment that drives success!
November 4, 2025
Data-Driven Feedback Loops: How DevOps and Data Science Inform Product Iterations
In the fast-paced world of digital product development, continuous improvement is a necessity rather than a luxury. To stay competitive, businesses must iterate quickly, delivering new features and improvements based on actual usage patterns and customer feedback. This iterative process is greatly enhanced by data-driven feedback loops, which allow teams to systematically collect, analyze and act on data to inform product decisions.

By integrating DevOps practices with data science, companies can not only speed up product iterations but also ensure that each change is guided by real-world insights. In this article, we’ll explore the role of automated data collection and analysis in driving continuous product improvements, the practical applications of data-driven feedback loops and what the future holds for data-driven product development.

The Power of Data-Driven Feedback Loops in Product Iterations

At the heart of modern product development lies the feedback loop — a cyclical process where teams collect data, analyze it and use the findings to inform the next round of development. The more efficiently this loop runs, the faster a product can evolve to meet market demands and customer expectations.

DevOps — which integrates development and IT operations into a single workflow — plays a crucial role in accelerating this feedback loop. By automating deployment, monitoring and incident response, DevOps practices enable rapid iterations. However, without the right data and insights, fast iterations won’t necessarily lead to better products. That’s where data science comes in.

Data science allows organizations to extract actionable insights from vast datasets. When combined with DevOps, it enables a holistic approach to product iterations, where every new release is informed by data, tested in real-time and optimized continuously based on feedback.

Real-World Example: Netflix’s Data-Driven Iterations

A prime example of data-driven feedback loops in action is Netflix. The company uses a combination of DevOps and data science to iterate on its platform rapidly. By collecting data on user behavior — such as how long viewers watch specific content, when do they press pause and what types of shows they prefer — Netflix’s product teams can continuously improve user experience.

For instance, Netflix’s recommendation algorithm is constantly refined based on real-time data from millions of users. Each time a user interacts with the platform, that data is fed back into the system, allowing the algorithm to become more accurate over time. Moreover, Netflix uses A/B testing to evaluate new features and changes before rolling them out widely. This iterative approach ensures that each update improves the platform, leading to higher customer satisfaction and retention.

The Role of Automated Data Collection in Product Development

To implement data-driven feedback loops at scale, automation is key. The manual collection and analysis of data would be too slow to keep up with modern development cycles, especially in large-scale environments where applications generate massive amounts of data in real-time.

Automated data collection involves continuously gathering data from multiple sources, including:
- User interactions: Clicking buttons, time spent on features, navigation paths
- System performance: Response times, server loads, error rates
- Customer feedback: Reviews, support tickets, surveys
This data is then fed into analytics platforms where machine learning (ML) models or statistical tools can process it to identify trends, patterns and anomalies. Tools like Elasticsearch, Logstash, Kibana (ELK stack) and Prometheus enable teams to automate this data collection and monitoring, while Apache Kafka allows for the processing of real-time streaming data.

Improving DevOps Pipelines With Data Science

In the context of DevOps, data science offers several advantages that enhance the product feedback loop. Some of these key areas include:
1. Predictive Analytics for Release Management: Predictive models can help teams determine the best time to release new features based on historical data. By analyzing past deployments and their impact on performance and customer behavior, data science can identify optimal release windows that minimize risk and maximize user engagement.
For example, an e-commerce platform might use predictive analytics to determine the best time to introduce a new feature, avoiding periods of high traffic or when customer support resources are stretched thin.
1. A/B Testing and Feature Rollouts: Data science enables sophisticated A/B testing by analyzing how different user segments respond to new features. By comparing the performance of different versions of a product or feature, teams can make data-driven decisions on which variant should be rolled out globally.
Facebook, for instance, is known for running hundreds of A/B tests simultaneously. When they experiment with a new feature — such as changing the layout of their news feed — Facebook gathers data from a small segment of users and compares their engagement metrics to a control group. This data-driven approach allows Facebook to confidently iterate on its platform, knowing which changes resonate with users before rolling them out at scale.
1. Error Tracking and Incident Response: DevOps relies heavily on real-time monitoring to ensure smooth product operation. Data science enhances this by enabling advanced error detection and root cause analysis. Tools like Splunk or Dynatrace can apply ML to identify patterns in log files and alert teams when anomalies occur, such as sudden spikes in response times. This not only speeds up incident resolution but also helps teams prevent future issues by learning from past failures.
Data-Driven Product Roadmaps: Using Insights to Inform Future Development

Data-driven feedback loops extend beyond individual product iterations — they also play a vital role in shaping long-term product roadmaps. By analyzing how users interact with a product over time, teams can identify high-impact areas to focus on in future development cycles.

For example, let’s say a SaaS platform notices that a significant percentage of users abandon the product after completing a particular task. By analyzing this data, the product team can determine whether this behavior is due to a confusing user interface, lack of features or some other factor. These insights can then guide the roadmap, ensuring that future iterations address the real pain points faced by users.

Moreover, data can reveal emerging trends or opportunities in the market. A fintech company might notice an uptick in mobile usage over desktop interactions and use this data to prioritize the development of new mobile features. Similarly, an e-learning platform might see increased demand for certain types of courses, prompting them to expand their offerings in those areas.

Challenges and Best Practices in Implementing Data-Driven Feedback Loops

While the benefits of data-driven feedback loops are clear, implementing them effectively presents certain challenges. Teams must be mindful of:
1. Data Silos: For data-driven feedback loops to be effective, data needs to flow seamlessly across the organization. However, many organizations suffer from siloed data, where different teams (development, marketing, operations) have access to different datasets that aren’t integrated. Breaking down these silos is critical for building a comprehensive view of how a product is performing.
2. Data Quality: Poor data quality can lead to misleading insights and suboptimal decisions. Ensuring data accuracy, consistency and completeness is a fundamental requirement for any data-driven process. Automated data validation and cleaning processes should be a part of any feedback loop implementation.
3. Balancing Speed with Insight: While DevOps practices focus on speed, there is a danger of prioritizing rapid iteration at the expense of insight. Teams must ensure that they aren’t just shipping features quickly, but also taking the time to analyze the impact of those features on user experience and business outcomes. Continuous learning from data should be built into the DevOps culture.
The Future of Data-Driven Feedback Loops

As AI and ML continue to evolve, the potential for data-driven feedback loops to become even more powerful is immense. Here are a few trends that could shape the future:
1. AI-Augmented Product Teams: As AI models become more sophisticated, they will take on a more central role in guiding product development. AI could eventually recommend entire feature sets, predict customer needs or even autonomously design user interfaces based on past data. This would not only accelerate iterations but also reduce the cognitive load on product teams, allowing them to focus on higher-level strategy.
2. Personalized Feedback Loops: As data collection becomes more granular, feedback loops could become personalized for individual users. Instead of relying on broad trends, teams could optimize product features and experiences for specific user personas, leading to hyper-personalized products that adapt dynamically to user preferences.
3. Real-Time Iteration with AI: Eventually, real-time iteration could become a reality. AI-powered systems would analyze incoming data in real-time, continuously tweaking features, configurations and experiences without the need for manual deployment. Imagine an e-commerce platform that automatically reorders its homepage layout based on a user’s past behavior the moment they visit the site.
Conclusion: A Data-Driven Future for Product Development

The combination of DevOps and data science has the potential to transform product iterations by creating fast, reliable and data-backed feedback loops. As organizations continue to adopt these practices, the ability to iterate based on real-world data will increasingly separate successful products from those that fail to keep pace with changing market dynamics.

By leveraging the power of automated data collection and analysis, teams can ensure that every iteration improves the product in ways that matter to users, ultimately driving higher engagement, satisfaction and long-term success.
November 15, 2024
Why DevOps Needs Cloudified NPM

One of the three “ways”—or principles that DevOps guru Gene Kim articulates as defining DevOps—is amplifying feedback loops. One of the key ways that Ops contributes to this amplification is by implementing rich monitoring instrumentation of operational conditions. The move toward more automated configuration management and the rise of cloud-friendly application performance management suites have gone a long way to providing strong feedback loops. However, network performance monitoring (NPM) is sorely in need of cloudification to become relevant to DevOps.

In one of my previous articles on devopsy.kinsta.cloud, I wrote about the impact of DevOps on IT operations management, and noted that infrastructure management tools have largely lagged behind APM in terms of becoming cloudified and API-friendly. NPM is a case in point. Just to be clear on terms, by network performance monitoring, I’m talking about monitoring that collects metrics such as latency, TCP retransmits and out-of-order and fragmented packets. These four metrics can tell a network engineer a lot about whether the network is the root cause or a major contributing cause to application performance issues. Other metrics and data points that are highly relevant to NPM are interface utilization percentage, traffic flow statistics and, for any apps or services that rely on internet communications, BGP paths. These latter metrics and data sets help engineers locate underlying causes in the network, such as congestion on an interface, large spikes in flows, internet path changes or correlation of performance issues with traffic traversing a particular ISP’s network. Sounds great, right?

The problem is, the way these metrics have been collected is all wrong for DevOps practice. For the last two decades, NPM tools have fallen into two main buckets: free, manual tools such as TCPDUMP, MRTG and WireShark; and monolithic, siloed commercial tools. Nobody will ever dispute the value of free tools, but at a certain point, chasing problems around with manual tools just doesn’t scale, so sooner or later most NetOps organizations have to invest in commercial tools.

Commercial NPM tools were primarily architected in a pre-cloud, pre-DevOps era, when data centers all were inside the WAN, applications were centralized and monolithic and all users were somewhere else on the WAN in a campus or branch office. Metrics collection was performed by appliances via connecting to the span interfaces of major routers and switches at choke points—the junctures of LAN and WAN connectivity.

That era is disappearing rapidly. Most new applications are being built on a distributed basis, leveraging hybrid cloud. With distributed application components, there is a much more complex mesh of communications at play, much of which will cross networks and the internet. Further, in most cloud environments, you simply can’t deploy an appliance—not even a virtual one, since in many cases there simply are no span ports.

The problem with traditional NPM goes beyond a lack of fit with modern application architectures. NPM appliances are designed as vertically integrated systems with a GUI as the only real way to get data out of them. As a result, their APIs are usually at best second-class citizens. This is problematic for DevOps because amplifying the feedback loop means being able to serve meaningful real-world data up across different teams.

The cloud and DevOps gap in NPM is pronounced enough that major analyst firms have taken notice. In May of this year, Gartner analyst Sanjit Ganguli published a research note titled, “Network Performance Monitoring Tools Leave Gaps in Cloud Monitoring.” It’s a fairly biting critique of the NPM space that says, essentially, that the vast majority of current NPM approaches were largely built for a pre-cloud era, and are unable to because the new complexities brought by decentralization and full-stack virtualization. As a result, network managers are left in the lurch when trying to adapt to the realities of digital operations.

What is needed to cloudify NPM and make it DevOps-friendly? First of all, with distributed, hybrid cloud applications, there need to be options to embed NPM metric collection agents in cloud server deployments, either directly on app servers or load balancers such as NGINX or HAProxy. With container management and configuration automation tools, deploying lightweight NPM agents to more distributed points is very doable. By collecting NPM metrics on or very close to application servers, you get the most accurate read on “is it the network?” when app performance issues arise.

Another key aspect of cloudifying NPM is where the analytics happens. Appliances have severe compute and storage constraints. In the era of big data and cloud-scale compute economics, relying on appliances for NPM analytics is beyond outmoded. There are a plethora of open-source big data platforms as well as a growing number of emerging commercial SaaS options for NPM analytics. Today, big data network analytics platforms allow engineers to perform deep ad-hoc analyses on billion-row datasets and get answers in seconds.

DevOps’ mandate for amplifying the feedback loop plus the availability of copious cloud compute capacity means that there is no excuse to not aggressively measure everything, including network performance. Networking teams can and must go beyond config automation to change the way and the scale they gather and share metrics to server and app dev teams. Cloud-friendly NPM is a needed step to bring about Net DevOps as a lived reality in IT.

About the Author / Alex Henthorn-Iwane

Alex Henthorn-Iwane is the vice president of Marketing at Kentik. He has more than 20 years of experience bringing new technologies in networking, security and software to global markets. Henthorn-Iwane leads the global marketing strategy for Kentik and helps the company bring its story and solutions to network-dependent organizations around the world. Connect with him on LinkedIn, Twitter or SlideShare.

November 10, 2016
My picks for opening day of DevOpsDays Austin

As we wrote earlier, we are happy to be attending and sponsoring DevOpsDays Austin 2015. The opening day morning is chocked full of great sessions with the afternoon dedicated to openspaces as is the norm. While everything from breakfast to Damon Edwards keynote through JJ Ashgar’s session on Chef before lunch look tasty. Here are three sessions that I will be attending that I am really looking forward to:

The Coming Donkey Apocalypse by Michael Cote’ – Cote’ formally of 451 Research and now with Pivotal is going to talk about the “state of the union” of Devops. From the abstract:

DevOps is growing in popularity and even usage in “the real world.” It seems like we’re slowly getting past unicorns only and seeing many “horses” do DevOps. Soon we’ll see the mainstream market – the “donkeys” – start to pick it up and bend DevOps to its will. This talk will go over “the state of the union” of DevOps and provide some guidance for how to prepare for the donkey apocalypse.

I don’t know what to make of this title but it peaked my curiosity. I don’t think DevOps adoption moving mainstream is equal to moving from horses to donkeys and so it is not a very flattering title. On top of this not only don’t I think main stream adoption of DevOps means it is Donkeys adopting it, I do not think it means an apocalypse for DevOps. In fact I think it is a good thing. On top of this, I think the idea of calling mainstream adoption, adoption of the donkeys and them causing an apocalypse smacks of elitism and is against what DevOps is about. I don’t think it is a bad thing that more organizations will get involved and that they will “bend DevOps”. I don’t know Michael well and have only spoken to him once briefly. Everything I hear though is that he is a very bright guy and dynamic speaker and writer. So i am interested to hear what Michael has to say in this session and whether he thinks it is a good thing, bad thing or how we can help move it along. You can’t judge a book by its cover and you sometimes you can’t judge a session by its title and abstract.

Fixing your Organization by Continually Breaking It by J Paul Reed – Reed is a consultant and has over a decade of experience in the trenches as a build/release and tools engineer, working with such organizations as VMware, Mozilla, and Symantec. In 2012, he founded Release Engineering Approaches, a consultancy incorporating a host of tools and techniques to help organizations “Simply Ship. Every time.” From the abstract:

When we discuss DevOps, we often find ourselves mired in the “tools vs. culture” debate. A point often forgotten is the “amplify feedback loops” of DevOps “3 Ways” and how to build that into our cultures. In this talk, we’ll look at the importance of continually breaking your organization with experiments, some surprising examples, and how to do it in such a way that you aren’t left with a broken organization.

I love this concept going back to Netflix and the Simian Army and have heard similar talks before on it. Looking forward to what Reed has to say and learning. Amplifying feedback loops is something I would like to see more written about as well. I am looking forward to this one!

Continuous Acceleration: Why Continuous Everything Needs a Supply Chain Approach by Josh Corman – Josh is of course the CTO of Sonatype and a former 451 Research analyst too. Besides all that Josh is a friend from the security world that I know a long time. This talk sounds like a continuation of what Josh and Gene Kim presented at DevOps Connect at RSA Conference a few weeks ago. Also Ericka Chickowski wrote a good article on her interview with Josh on this last week. Here is the abstract:

Today we write less code & consume more re-usable open source code. Innovation accelerates & complexity grows. Complexity is the enemy of quality. Poor quality creates break-fixes & slows dev. It’s a continuous loop.
Learn about software supply chain management strategies to accelerate delivery: scrutinize your ‘suppliers,’ minimize risk & bloat, and improve traceability, visibility & response times.

Josh is a great presenter. I have seen him present many topics over the years at several events, I am sure this one won’t disappoint.

There are other good presentations including some stuff on Dockers and containers (what would a conference be without them?). Additionally the I am sure the open spaces will spawn some good stuff as well.

I will try to update throughout the day and give those who can’t attend a sense of what is going on.

I realize many of you can’t make Austin tomorrow, but if you are going and see me please introduce yourself and say hi.

May 4, 2015
DevOps and faster feedback: fewer problems, better features (part 2)

As the experts established in part one of this series , the tightening of feedback loops through DevOps gives developers a better chance to learn how well their coding choices perform in the context of production environments. This makes for higher quality code in the long-run and constantly improved skillsets among development teams.

But fast feedback loops don’t just improve the line-by-line coding work the development team puts out. It can also push through a huge boost in the value of the features they’re producing for end users.

“Feedback loops are important not just for technical feedback or feedback about things not working,” says Aater Suleman, CEO of Flux7 Labs and a professor in computer systems design and architecture at the University of Texas. “There’s also the feedback around what customers are looking form in terms of particular features and whether or not those features are doing well in the market or not.”

The tighter the feedback loop is about feature sets—and the more quickly the team can release responses to that business-related feedback–the faster the pivot time to account for market conditions. The quicker assessment of how well features are doing gives development teams and the business units they answer to a more accurate barometric reading to decide where to direct programming time. As a result, it isn’t just the quality of work that goes up, but also the business value.

“With a DevOps flow that allows developers to start seeing feedback from end customers right away, it helps them be more agile on a business level so they can be more strategic about where to spend their actual time,” Suleman says, explaining that this exposes developers to a big-picture view of their work. “You may have the most productive development environment in the world, but if a feature developers have worked on for two months is not going to be liked by the end users, it doesn’t matter how productive the developers were during that time period.”

Not only that, but it eliminates the hamster-wheel of playing catch-up with competitors’ features.
“I know of many companies which still hold to quarterly release cycles. Did you competitor just release an awesome new feature? Have fun waiting three months to catch up,” says Curtis Poe, a consultant and Agile development coach for All Around The World. ” In today’s competitive world and fast-paced business environments, do you really want to playing catch up all the time? Switch to continuous delivery and never worry about a lengthy release process blocking you from offering what your competitors offer.”
Poe says that the best DevOps teams can get the most differentiation from the crowd by understanding that they’re not just monitoring technology and code, they’re monitoring people. Namely, end users.

“Do you sell shoes online and you have a typical conversion rate of 3.2 percent? What happens if you make a release and the conversion rate drops to 2.5 percent?” he says. “Assuming that nothing technological is going wrong , monitoring business value, when appropriate, can instantly tell your designers, ‘Hey, moving that button from the left to the right side impacted our sales.’”

Additionally, that kind of business monitoring makes it more easy to understand the dependencies between technical and business functional aspects of software updates. Then the team can more easily explain to business leaders what technical obstacles stand in the way of functional improvements, and how to overcome them.

“You always want to deliver the best experience for your customers, and you want to iterate on both the product and the non-functional aspects as often and quickly as possible,” says Arnold Goldberg, vice president of customer engineering for PayPal. “Integrating new features might be the wrong thing to do if, for example, there are challenges with performance, stability or availability.”

May 12, 2014
DevOps and faster feedback: fewer problems, better features (part 1)

As organizations begin to institute DevOps patterns and continuous delivery into their IT shops, one of the most dramatic long-term returns on their investment is that it bolsters the class and consistency of work produced by existing development teams.

Organizations that evolve to DevOps don’t necessarily need to hire rock star programmers to boost code and feature quality. That’s because, in the long-run, continuous delivery actually fosters an environment that makes it possible for the developers already in place to learn more efficiently and better improve the day-to-day quality of their work. The secret ingredient to this? Faster feedback loops.

“Shorter time to feedback is the key difference between DevOps and the traditional waterfall approach,” says Aater Suleman, CEO of Flux7 Labs and a professor in computer systems design and architecture at the University of Texas. “The main challenge with waterfall was the fact that there was a very long loop for feedback, which removed the agility from the picture. It could be a month or longer before developers would actually get feedback about what they’d done.”

The longer the cycle, the more code to sift through to find problems and the longer it takes to fix issues, says Curtis Poe, a consultant and Agile development coach for All Around The World. Shorter feedback cycles make it easier to find issues quickly and attend to them as soon as possible.

“Having continuous delivery means that typically very little code needs to be reviewed for failure and as a developer, if I get a bug report for code I wrote in the last hour, I’m going to be far more efficient in fixing in than for a bug report for code I wrote last month,” Poe says.

This is true not just for out-and-out failure, but also tweaking for improved performance.

“When frequent deployments are coupled with well-instrumented monitoring metrics on all aspects of your application, small nuances in performance can be detected and teased out for relatively small amounts of code change,” says Jeff Behl, chief network architect at Logic Monitor. “Developers want quick feedback to know changing this particular small part of code can affect this aspect of the application. The sooner you learn how to optimize the application code, the better.”

Even more important, fast feedback loops are not only critical for fixing past problems, but making sure they don’t happen again in the future. The shorter the time between when a developer writes code and receives information about how well it did out in the real world, the easier it is for them to learn from those lessons and apply them to future work.

With longer release cycles, be they quarterly or monthly, there’s often a lot of energy expended trying to find the person who created an offending line of code that broke something, says BLANK. The top triage person will go through the ringer to figure out who broke the code, until they might pinpoint something like, this person made this code commit six months ago that broke the application.

“Does anyone remember what they were doing six weeks ago? Of course not. But if you were writing code at ten o’ clock and I came by at one o’ clock and told you exactly what was broken and I sent you a zip file that had everything that you needed to do to reproduce that on your desktop do you think you’re going t o be a better programmer?,” Gary Gruver, vice president of quality engineering release and IT operations at Macy’s said last fall at FlowCon, explaining that every developer wants to do a good job and is typically trying to do the best job that he or she can. “If you can’t provide them that rapid feedback , real-time in an efficiency manner, how do you expect people to get better?”

In the scenario above, the process of developing the offending code is long gone in a developer’s memory, which means it takes more work for the coder to review their work, remember the project and then mentally link that process to the infrastructure dependencies that were affected by it. With shorter cycles, devs spend less time remembering what they did and more cognitive work on understanding dependencies and the context in which the code operated. Long-term, that’s going to make for better code from the get-go later on.

“Developers who can understand how their applications work under additional loads, abnormal conditions, and degraded circumstances, will end up designing better applications and systems, and in the end play a more strategic role at the company,” says Arnold Goldberg, vice president of customer engineering for PayPal.

According to Goldberg, context in development is critical and the faster, richer feedback provided by DevOps allows developers to better understand how application improvements work in production context. And that is huge for informing best practices.

“Much of it is specific to the fabric of the existing organic infrastructure that a company has, so it needs to be a pragmatic application of it in the context of the company’s current situation,” he says.

Faster feedback loops aren’t just about making technical coding improvements, either. These more rapid assessment cycles can also greatly improve features and business value offered by developers. We’ll explain more in part two of this series.

May 6, 2014