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Tag: dora metrics

Why DORA Metrics Look Different When AI Is Part of Your Development Workflow
DORA metrics have been a reliable compass for engineering teams for over a decade. Deployment frequency, lead time for changes, change failure rate, mean time to recovery, and reliability give teams a shared language for talking about delivery performance. The research behind them is solid, the benchmarks are well-established, and most engineering leaders know what good looks like for each metric.

What is less discussed is how AI-assisted development changes the baseline assumptions those metrics were built on. Not whether DORA metrics are still relevant — they are — but how the same numbers can mean something different when a significant portion of your codebase is being generated by AI coding tools.

Deployment Frequency Goes Up. Sometimes for the Wrong Reasons.

AI coding assistants accelerate code production. Developers who use them ship features faster, close tickets quicker, and generate pull requests at a higher rate than before. For teams tracking deployment frequency, this looks like progress. The number goes up. Executives see the dashboard and interpret it as improved engineering velocity.

The problem is that deployment frequency measures how often you ship, not whether what you ship is correct. When AI generates code at a pace that outstrips a team’s ability to review it carefully, deployment frequency can increase while code quality quietly degrades. The metric looks better. The system becomes more fragile.

This is not an argument against AI tooling. It is an argument for being careful about what deployment frequency is actually telling you when AI is involved. An increase in deployment frequency driven by genuine process improvement looks different in production from an increase driven by shipping AI-generated code faster than the review and testing process can handle.

Lead Time Compresses in the Middle and Expands at the Edges

Lead time for changes measures the time between a code commit and that code running in production. AI coding assistants directly reduce one part of this: the time it takes to write the code in the first place. Implementation time drops. For many teams, this is where lead time was already short, so the improvement is real but modest.

Where lead time tends to expand is at the review stage. AI-generated code has a particular failure pattern that experienced reviewers describe the same way: the diff looks clean, the logic seems sound, and the problem only shows up two days later when something else breaks. Nobody flags it in review because there is nothing obviously wrong to flag. The issue lives in how the new code behaves when it is running next to three other services under production load, not in what the code itself says.

That kind of review is slower. It has to be. And when it is not slow enough, the cost shifts to the investigation that happens after deployment rather than before it.

Integration testing carries the same pattern. AI-generated code tends to produce more interaction edge cases than code written by someone who has been staring at the same codebase for six months. Those edge cases do not always surface in the test suite that was written alongside the code. They surface in staging or production, which means they show up as lead time measured in days rather than hours.

Change Failure Rate Is the Metric Most Disrupted by AI

Of the four DORA metrics, change failure rate is the one that AI-assisted development affects most directly and most unpredictably.

Change failure rate measures the percentage of deployments that cause a production failure. For teams with mature testing practices and stable codebases, this metric tends to be low and relatively stable. AI-assisted development disrupts this in a specific way.

AI coding tools generate code that handles the described scenario competently. What they consistently miss are the undescribed scenarios: the edge cases that experienced developers know about from working with the system over time, the integration behaviors that are not documented anywhere but are understood by the team, the data patterns that only appear in production traffic.

Tests written alongside AI-generated code have the same blind spots, because those tests are also generated from the same limited understanding of the system. The result is a change failure rate that can spike unpredictably after AI-assisted development is adopted, because failures are concentrated in the categories that neither the AI nor the tests were aware needed to be covered.

Addressing this requires testing approaches grounded in real system behavior rather than in what the AI understood the system to be. When tests are derived from actual production traffic rather than from specifications, they cover the scenarios that real users produce rather than the scenarios that developers anticipated. Understanding how each of the five DORA metrics connects to testing strategy is worth revisiting as AI becomes a larger part of the development workflow.

Mean Time to Recovery Depends on What You Can See

Mean time to recovery measures how long it takes to restore service after a production failure. AI-assisted development affects this metric indirectly through the nature of the failures it tends to produce.

Failures caused by integration issues in AI-generated code are often harder to diagnose than logic errors in human-written code. The code looks correct in isolation. The test suite passed. The failure only appears when the code interacts with the rest of the system under real conditions. Tracing this category of failure back to its source requires good observability tooling and a clear understanding of service interaction patterns, both of which need to be in place before AI-assisted development is adopted at scale rather than after.

Teams that invest in observability before expanding AI-assisted development tend to maintain stable mean time to recovery as deployment velocity increases. Teams that treat observability as something to add later find that AI-assisted development compounds the diagnostic difficulty of production failures rather than reducing it.

Reliability Is the Fifth Metric AI-Assisted Development Puts Under New Pressure

The fifth DORA metric, reliability, measures whether services meet their availability and performance targets in production. It is the newest addition to the framework and, in some ways, the most directly affected by AI-assisted development at scale.

When AI generates code faster than testing infrastructure can validate it, reliability degrades in a specific pattern. Individual deployments pass their gates. The cumulative effect of many AI-generated changes shipping in quick succession creates instability that no single deployment would have caused alone. The system becomes progressively less predictable, not because any one change was wrong, but because the aggregate of changes exceeded what the testing and review process could meaningfully absorb.

Reliability as a metric captures this cumulative effect in a way that the other four metrics do not. Deployment frequency can look healthy. Change failure rate can stay within acceptable bounds on a per-deployment basis. Mean time to recovery can remain stable. And yet reliability degrades because the system is absorbing more change than it can maintain consistent behavior through.

For teams adopting AI-assisted development at scale, reliability is the leading indicator worth watching most closely. A decline in reliability that is not explained by individual deployment failures is often the first signal that the pace of AI-generated change has outstripped the team’s ability to validate it.

Reading the Dashboard Differently

DORA metrics remain the right framework for measuring engineering delivery performance. The research behind them is not invalidated by AI-assisted development. What changes is the interpretive layer.
- A deployment frequency increase deserves a question: is this driven by genuine process improvement or by shipping AI-generated code faster than the system can absorb it safely?
- A stable change failure rate deserves a question: is this because the testing infrastructure covers the edge cases that AI generation misses, or because those failures have not appeared yet?
- A lead time reduction deserves a question: where in the pipeline did time come out, and did it create pressure anywhere else?
- A reliability decline that does not trace to specific deployment failures deserves a question: is the cumulative pace of AI-generated change exceeding what the testing and validation infrastructure can absorb?
DORA metrics are still the right compass. AI-assisted development means reading them with more context than the numbers alone provide.
May 26, 2026
How AI is Shaping Modern DevOps and DevSecOps
AI is no longer a side experiment in software delivery. Gartner estimates that by 2028, three-quarters of enterprise software engineers will use AI code assistants, up from less than 10% in early 2023. That scale matters because it shifts day-to-day work across the entire software development lifecycle — from what makes it into the backlog to how we release and learn after incidents.

From SDLC to Flow: What Really Changes

Across planning and design, AI reduces noise. Backlogs get de-duplicated, related items are grouped, and dependency-heavy work is surfaced earlier, so sprints start clearer. During build and test, assistants suggest edge cases, flag risky changes, and help teams focus on the small number of issues that truly threaten stability. In release and operations, AI connects the dots between recent deploys, logs and user impact, so responders get to the first safe action faster. None of this is magic; it’s shorter feedback loops and better signals, stitched into the work leaders already manage.

Where AI Transforms DevOps

DevOps has always been judged by a handful of simple outcomes – how often we deploy, how long changes take, how often they fail, and how quickly we recover. These are the well-known DORA metrics, and they are a practical way to separate AI promise from AI reality.
- Planning gets quieter: When duplicates and old tickets are cleared, teams ship smaller, steadier changes.
- Reviews get crisper: AI can highlight suspicious diffs or missing tests, while humans have the final say.
- Testing gets quicker: Patterns behind flaky tests are easier to spot, so pipelines give trustworthy results sooner.
- Releases get right-sized: Not every change needs a ceremony. Transparent, risk-aware routing often helps keep throughput up without gambling on quality.
The point is not about adding more tools. It is more about removing friction where teams already work and watch whether deployment frequency, lead time, failure rate, and recovery actually improve.

Where AI Benefits DevSecOps

Security shifts left when it is part of the developer experience, not an after-the-fact gate. AI helps in three human-friendly ways:
- Explain, do not just alert: Translating a policy finding into plain language and a suggested fix turns a blocker into a quick edit.
- Prioritize what matters: Vulnerabilities are not equal; looking at exploitability and blast radius avoids “fix everything” fatigue.
- Keep receipts: Automatically capturing what changed, why it was safe, and who approved it gives leaders confidence without adding meetings.
This is DevSecOps as most teams want it, less scolding and more shared context.

Guardrails That Earn Trust

Leaders don’t have to be experts in models to set good rules:
- Provenance and privacy: Limit AI inputs to approved code and data; log which model and version influenced what change.
- Human accountability: Keep humans responsible for merges and releases; use AI as an advisor, not an authorizer.
- Clarity over cleverness: Document where AI is in your toolchain and how people can challenge it. If a control adds friction without moving outcomes, remove or reshape it.
How to Start AI in DevOps and DevSecOps
- Pick one product or service line
- Set baselines for the four delivery measures
- Run a time-boxed pilot of six to eight weeks
- Keep the few AI-assisted steps that make work feel simpler and make the metrics better and drop the rest
- Share the before/after in a single page (cover what improved, where risk decreased, and what you learned)
How to Choose the Right AI Tool for DevSecOps

With dozens of AI-enhanced DevOps and DevSecOps platforms in the market (such as GitHub, GitLab, Harness, Atlassian, JFrog, Snyk, Checkmarx, and more) most teams struggle with the same question: Which AI tool is the right one for us?

Choosing the right AI tool is less about features and more about fit with how your teams already work.
- Match your workflow: Pick tools that plug directly into your existing repos, pipelines, and collaboration channels.
- Prioritize signal quality: The best AI tools reduce noise, leading to fewer alerts, clearer explanations, and actionable suggestions.
- Check governance and transparency: Choose platforms that show what model made a recommendation, what data it used, and keep an auditable trail.
- Validate security boundaries: Ensure code and data stay within approved environments and that the vendor supports enterprise‑grade security controls.
- Measure impact on DORA and security KPIs: Pilot quickly and track deployment frequency, lead time, MTTR, failure rate, and vulnerability remediation.
- Prefer developer-first security: Inline fixes, clear reasoning, and prioritized vulnerabilities matter more than broad scanning.
- Look for consolidation opportunities: Platforms that integrate source control, CI/CD, security, and operations provide AI with more context and reduce tool fatigue.
AI is shaping modern DevOps and DevSecOps by simplifying the work, not by replacing it. It allows fewer distractions in planning, clearer reviews, faster tests, steadier releases, and calmer incident response. With modest guardrails and a focus on the outcomes you already track, you can harness the change without getting lost in the hype. The goal is not to be “AI-driven.” It’s to be purpose-driven, with AI helping you ship faster and safer, and with evidence your stakeholders can trust.
April 2, 2026
GitOps Implementation at Enterprise Scale — Moving Beyond Traditional CI/CD

Most engineering organizations running traditional CI/CD pipelines eventually hit the ceiling. Deployments work until they don’t, and when they break, the fixes are manual, inconsistent and hard to trace. For example, we recently reached that point after our third deployment incident in two months, each caused by configuration drift between environments. Our pipelines had grown organically over several years, and teams had developed habits of running manual fixes when deployments failed — deepening inconsistencies across staging and production. Rollbacks meant remembering which scripts to run and in what order. A compliance audit then flagged our lack of change traceability, and that became the final push toward evaluating GitOps.

GitOps positions Git as the single source of truth for system configurations, with automated agents continuously reconciling live environments against declared state. Adoption has accelerated quickly. Nearly 91% of respondents already use GitOps, with another 67% planning adoption within a year. For organizations at scale, the question has shifted from whether to adopt GitOps to how to execute the migration without disrupting active development.

We evaluated several tools against our environment: Jenkins for legacy pipeline compatibility, GitHub Actions for repository-native automation, Harness for enterprise deployment orchestration and ArgoCD for Kubernetes-native continuous delivery. ArgoCD became our primary choice because of its pull-based synchronization model, built-in drift detection and clear visualization of application state across clusters. We kept Jenkins and GitHub Actions in the stack for build and test stages where they already worked well. Harness remained an option for teams requiring more sophisticated approval workflows and governance controls. We ruled out purely script-based push deployment approaches because they offered poor drift control and scaled badly.

Security advantages became tangible during implementation. Declarative infrastructure means every change flows through pull requests with full audit trails. Policy-as-code enforcement allows teams to define security requirements that apply automatically across all deployments. Role-based access through Git permissions eliminates separate credential management systems. We integrated SAST scanning directly into the GitOps workflow, catching issues before they reached production clusters.

Performance metrics tracked against DORA benchmarks told the story of impact. Deployment frequency increased from weekly to multiple times daily, as merges now triggered automatic reconciliation to clusters. Lead time for changes dropped from days to hours. Both change failure rate and mean time to recovery improved significantly because rollbacks became Git reverts followed by automatic re-sync rather than manual intervention.

Organizational resistance proved more challenging to address than the technical work. Teams feared the new approach would introduce additional bureaucracy. Engineers accustomed to quick kubectl fixes worried about losing agility. We ran hands-on workshops demonstrating that GitOps actually produced faster deployments, easier rollbacks and better visibility into what was running where. We created golden templates for common deployment patterns, so teams did not have to start from scratch. Early adopter wins helped convert skeptics, and support from compliance and security teams gave the initiative organizational weight.

The rollout required careful sequencing. We started with a pilot team, standardized repository layout and templates based on what we learned, then added security gates. Low-risk stateless services migrated first. As confidence grew, we moved to higher-criticality applications. Some legacy services could not migrate because they depended on imperative configurations, lacked adequate health checks or had tight coupling that GitOps declarative models could not accommodate cleanly. Those remain on the roadmap for refactoring.

Unexpected benefits emerged after full adoption. Onboarding improved as deployment knowledge now lived in Git history and manifests rather than in senior engineers’ heads. Incident response accelerated because traceability let teams pinpoint exactly what changed and when, and rollback became a consistent, reliable operation. The shift from push-based to pull-based operations improved security posture by limiting direct cluster access.

Looking back, we would have invested earlier in three areas: Training, so teams understood the mental shift before tools landed in their laps; templates, so adoption friction stayed low; secrets and environment strategy, which proved more complex than anticipated when bolted on later. GitOps delivered on its promise of observable, auditable, reproducible infrastructure, but the path there required patience, sequencing and sustained attention to the people side of the change.

February 24, 2026
An Experience-Based Guide to Choosing the Right DevOps Provider in 2026
To stay competitive, companies need to deliver software updates and new features rapidly without compromising reliability and security. However, maintaining a specialized in-house team can be costly due to talent shortages, high salaries and fluctuating workloads. Whether through outsourcing, outstaffing or managed service models, partnering with a trusted DevOps provider offers a flexible way to fill talent gaps and scale expertise without the burden of maintaining a large in-house team.

This article outlines a four-phase approach for selecting and evaluating a DevOps partner to ensure alignment with your business objectives.

PHASE 1: The ‘Why’ (Internal Preparation)

Begin your search by evaluating your project needs. Assess the current state of your software systems, delivery processes and areas for improvement. This will help you define clear requirements and avoid misunderstandings as you align your project vision with that of the service provider.

1.1 Define Your Needs and Goals

Identify the challenges you want to solve through DevOps adoption and create a clear, organized list of goals. This list will become the foundation for selecting and evaluating potential partners. Here are a few examples of such goals:
- Increase developer productivity by establishing or optimizing a continuous integration/continuous deployment (CI/CD) pipeline.
- Implement infrastructure as code (IaC) to automate and standardize cloud environment management.
- Reduce deployment and release times while minimizing errors from manual processes.
- Improve system monitoring, alerting and logging to enhance visibility.
- Strengthen infrastructure security and compliance controls.
Define measurable success criteria (for example, reduce deployment time by 40% in six months or achieve 99.9% uptime on production). Providers perform better when expectations are transparent and quantifiable.

1.2 Align Stakeholders Early

Before engaging external DevOps partners, make sure all key stakeholders in domains such as engineering, operations, finance and security agree on priorities and success metrics. Misalignment often causes friction later when the provider’s goals do not fully match business expectations.

Max Petrichenko, team lead at IT Craft DevOps development company, notes:

“A clear understanding of your long-term goals and challenges is the foundation of a successful DevOps implementation. If your team is overloaded or lacks a comprehensive understanding of existing bottlenecks, a third-party system audit can provide valuable insights. It provides an outside perspective on your infrastructure, highlights hidden risks and helps refine your requirements before engaging an external DevOps partner.”

1.3 Assess Your Current Capabilities

Review your available resources and evaluate your in-house development team’s skills and capacities, as they closely collaborate with the outsourced DevOps team. Focus on identifying:
- Gaps in workflows that DevOps can help resolve.
- How much workload do your developers already carry?
- Existing DevOps-related skills and knowledge within the team.
- How satisfied are developers with current processes?
1.4 Determine Project Scope and Duration

Evaluate the scope and expected duration of your collaboration. Depending on your goals, you might need a short-term engagement to handle specific tasks, such as a cloud migration or a long-term strategic partner who acts as an extension of your in-house team, managing infrastructure maintenance and monitoring.

Understanding your project scope, key outcomes and timeline will help you determine how many DevOps engineers you need and what type of engagement model best fits your goals.

PHASE 2: The ‘Who’ (Initial Vetting)

Once your internal preparations are complete, proceed to evaluate potential service providers. Review the available candidates carefully and create a shortlist of those who meet your key requirements.

2.1 Experience and Expertise

A solid track record is the clearest indicator of a service provider’s expertise. Look for a partner that specializes in DevOps within your industry, not a general software development company. Their familiarity with your industry’s unique challenges can be a significant advantage.

Review case studies and client testimonials from similar projects, paying attention to experience with businesses of your size. Additionally, don’t hesitate to ask for references to verify their claims.

Ask how the provider measures DevOps’s success internally. Mature vendors often track metrics such as deployment frequency, change failure rate, lead time to change and mean time to recovery (MTTR)—known as DORA metrics. A team that is fluent in DORA indicates a metrics-driven mindset, not just tool familiarity.

2.2 Technical Proficiency and Tooling

Review the service provider’s technical stack. They should have proven expertise with core DevOps tools such as Docker, Kubernetes, Jenkins, Terraform or Grafana, ensuring they can support automation, orchestration and monitoring across your environment.

However, a reliable service provider shouldn’t be limited to a fixed set of tools. Instead, they should be skilled in a wide range of DevOps technologies and flexible enough to choose the ones that best fit your project’s specific needs and challenges.

Check whether the provider supports multi-cloud and hybrid environments. Various companies face tool sprawl across AWS, Azure and on-prem infrastructure. A capable partner should demonstrate how they unify observability and automation across different platforms. Ask for a real example of how they used automation to reduce operational costs or deployment errors in a similar environment.

2.3 Communication and Cultural Fit

The outsourced DevOps team should function as a seamless extension of your in-house team. Prioritize providers that promote transparency, proactive communication and flexibility in adapting their workflows to align with your existing processes.

A reliable service provider should ensure smooth communication and consistent project updates. They need to integrate with the project management tools used by your internal team and hold regular touchpoints such as daily check-ins and weekly planning meetings to keep everyone aligned.

Beyond English fluency, evaluate time-zone overlap, responsiveness during emergencies and clarity in reporting. A mature vendor provides structured communication rituals such as weekly status summaries, quarterly retrospectives and incident reports, following the DevOps blameless post-mortem model.

PHASE: 3 The ‘How’ (Deeper Evaluation)

After shortlisting and vetting potential providers, proceed to the technical and operational evaluation stage. The goal here is to verify their claims and gain a clear understanding of how they apply DevOps practices in real projects.

3.1 Technical Assessment and Methodology

Take a close look at how the provider applies DevOps in real projects. Ask them to walk you through a typical CI/CD pipeline and explain how they handle quality, security and process automation. Ideally, the provider can walk you through an end-to-end workflow, from estimation to production, explaining each step and the reasoning behind every decision.

Ask whether the provider performs infrastructure security reviews as part of CI/CD implementation. DevSecOps readiness, including vulnerability scanning, compliance checks and secret management, has become mandatory. Moreover, evaluate if they use automated testing frameworks and observability tools to detect performance regressions early.

3.2 Team Structure and Skill Set

Request to review the curriculum vitae (CVs) of the specialists assigned to your project and schedule interviews to discuss their experience, previous roles and upcoming responsibilities. Additionally, assess the provider’s replacement procedures and resource backup plans to ensure business continuity in the event of a key DevOps expert leaving the team.

3.3 Test the Partnership with a Trial Project

Begin with a small paid project to evaluate the DevOps team in a real-world setting. This allows you to observe their technical skills, communication style and overall approach to collaboration. Pay attention to how they respond to feedback and manage revisions, as this will reveal their flexibility and commitment to delivering quality results.

If possible, structure your trial project to simulate a real bottleneck or risk scenario, such as a sudden traffic spike or a failed deployment. Measure how the team collaborates under pressure. A strong DevOps partner demonstrates not only technical skill but also composure and clear communication when issues occur.

PHASE 4: The ‘What’ (Finalizing the Agreement)

After vetting and selecting a particular service provider from your list, focus on preparing a clear and comprehensive contract.

4.1 Pricing Models and Transparency

Whatever model you choose, whether fixed price or a dedicated team, ensure the pricing structure is fully transparent. Ask about potential risks of cost overruns and how they are handled. Confirm that the provider clearly outlines all expenses from the start to avoid unexpected charges later.

Request a detailed cost breakdown for each deliverable or sprint to prevent hidden charges. Clarify ownership of cloud accounts, licenses and infrastructure expenses. Moreover, establish a clear exit strategy—what happens to your infrastructure scripts, credentials and data if the contract concludes.

4.2 Clarify Terms and Protect Your Interests

Clearly document all deliverables in the contract and include key performance metrics in the service level agreement. These may cover uptime, mean time to recovery, lead time for changes and other relevant indicators. Review carefully the section outlining what happens if the provider fails to meet commitments and how accountability is ensured.

Beyond the SLA, include a continuous improvement clause. This ensures the provider regularly proposes process enhancements (for example, improving build time or deployment frequency) rather than just maintaining the status quo.

Review all contract terms with your legal team to ensure your interests are fully protected. The agreement should clearly define intellectual property rights, roles and responsibilities, and include provisions that help prevent misunderstandings or future disputes.

Final Thoughts: Continuous Partnership Evaluation

Even after signing, vendor management doesn’t end. Review your DevOps provider’s performance quarterly using key performance indicators (KPIs) such as:
- Deployment frequency and speed improvements
- Incident response efficiency
- Infrastructure cost optimization results
- Innovation contributions (for example, automation improvements or new tool adoption)
This ongoing evaluation keeps the relationship strategic, not transactional.
October 29, 2025
Database Observability Extends DORA Metrics and More to Database DevOps

Extending DevOps to the database helps teams keep up with application deployments by shifting left and streamlining previously slow, manual stages in the workﬂow. Implementing change management automation and governance represents a major step in completing the “last mile” of your CI/CD pipeline. But to fully embrace DevOps and its beneﬁts, you not only need to automate and integrate but continuously optimize your processes.

DORA’s four key metrics provide a compass to application and DevOps teams for continuous optimization of the development pipeline. Measured and represented in analytics and monitoring platforms, these insights drive a faster, more eﬃcient and more innovative application development process.

How can the same spirit and quality of optimization extend to the database deployment workﬂow?

Organizations are leveraging database observability to apply DORA metrics to change management and use those insights to:

● Accelerate the pipeline

● Improve security

● Reduce errors and remediation

● Anticipate and prevent issues

● Detect and avoid database drift

Much like other elements of DevOps, it’s possible to bring visibility to the database change process thanks to the components that power workﬂow automation. Database observability unlocks DORA metrics along with other indicators that matter to your DevOps, application, database and IT teams.

The Role of Database Observability in DevOps

Database observability is the capacity to understand a system’s current state and optimize CI/CD pipeline performance, security, and eﬃciency by analyzing its outputs, logs and workﬂow data. These measurements inform insights for continuous improvement and proactive problem management, closing the gap between application development and database management in traditional DevOps practices.

Just like a DevOps observability dashboard provides visibility into the performance of application development workﬂows, a database observability dashboard showcases change management workﬂow measurements. It can answer questions like, “What is the current state of our environments?” or, more precisely, “Which changes have been deployed? Which of those succeeded or failed? What is the context surrounding those changes – and what can we learn from this information that can help make the pipeline better?”

Teams embrace database observability so they can:

● Streamline database operations by identifying bottlenecks and improving efficiency

● Enhance auditing capabilities through transparent and traceable database change logs

● Foster a culture of continuous improvement by leveraging data-driven insights for database management

● Align with application pipeline acceleration efforts to achieve overall DevOps agility and maturity

● Reduce the frequency and risk of deployment failures and operational downtime

Database observability enables you to shift left, proactively detecting issues and improving integrity earlier, before errors occur in production or further down the pipeline. It supports a high-performing data infrastructure with the ability to detect problems before they endanger your production system and identify opportunities for process improvement that make way for the team to focus on progress and innovation.

DORA Metrics for Database Change Management Optimization

With visibility into your systems, you can use actionable insights to inform decisions for the continuous improvement of your CI/CD pipeline. Think of DORA’s metrics (deployment frequency, lead time for changes, change failure rate, and time to restore service) in the database context, then expand to answer your optimization questions.

Identifying metrics is a critical step in the database observability initiative. Observability insights will help answer, “How do we improve these metrics?” and “What does improvement look like?” By capturing and analyzing database change logs over longer periods, teams can see trends and patterns previously obscured to them by lack of visibility.

Breaking down deployment frequency, for instance, can help highlight the metrics by database, application, and team to ﬁnd high and low performers or discover ineﬃciencies in data connections. Similarly, ﬁltering and slicing observability data to create baselines, comparisons and aspirations enables teams to use the data they’re already collecting to look inward and optimize the process itself. This can answer questions like:

● Which deployment pipelines are least eﬃcient and why? How can these be optimized, unblocked and accelerated?

● How often are database rollbacks occurring – and how is that affecting reliability?

● What kinds of errors are most common and costly – and how can they be avoided?

● Which users and systems are accessing which databases – and how can they be more secure?

In addition, database observability provides security and compliance enhancements by enabling trend analysis, reducing remediation time and anticipating potential issues before they occur. It can also identify drift in the intended state of your environment with detailed reports to ﬁnd the problem fast.

Achieving database observability has typically been an uphill battle – as one of the last areas of the overall application pipeline to be automated, database change management often relies on manual processes that take hours or days, not seconds. However, automated database change management workﬂow data provides all the ingredients for a successful database observability dashboard, not to mention the capability for self-service deployments and built-in policy checks.

In the context of full-pipeline visibility and continuous optimization, database observability brings much-needed clarity and granularity to teams embracing DevOps at every stage of their workﬂows.

March 7, 2024
The Ultimate DORA DevOps Guide: Boost Your Dev Efficiency With DORA Metrics

Imagine having a powerful tool that measures your software team’s efficiency, identifies areas for improvement and unlocks the secrets to achieving speed and stability in software development – that tool is DORA metrics.

DORA metrics offer valuable insights into the effectiveness and productivity of your team. By implementing these metrics, you can enhance your dev practices and improve outcomes.

In this blog, we will delve into the importance of DORA metrics for your team and explore how they can positively impact your software team’s processes. Join us as we navigate the significance of these metrics and uncover their potential to drive success in your team’s endeavors.

What are DORA Metrics?

Software teams use DORA metrics in an organization to help improve their efficiency and, as a result, enhance the effectiveness of company deliverables. It is the industry standard for evaluating dev teams and allows them to scale.

The metrics include deployment frequency, lead time for changes, mean time to recovery, and change failure rate. They have been identified after six years of research and surveys by the DORA (DevOps Research and Assessments) team.

To achieve success with DORA metrics, it is crucial to understand them and learn the importance of each metric. Here are the four key DORA metrics

The Four DORA Metrics

1. Deployment Frequency: Boosting Agility

Organizations need to prioritize code deployment frequency to achieve success and deliver value to end users. However, it’s worth noting that what constitutes a successful deployment frequency may vary from organization to organization.

Teams that underperform may only deploy monthly or once every few months, whereas high-performing teams deploy more frequently. It’s crucial to continuously develop and improve to ensure faster delivery and consistent feedback. If a team needs to catch up, implementing more automated processes to test and validate new code can help reduce recovery time from errors.

Why is deployment frequency important?

● Continuous delivery enables faster software changes and quicker response to market demands.
● Frequent deployments provide valuable user feedback for improving software efficiently.
● Deploy smaller releases frequently to minimize risk. This approach reduces the impact of potential failures and makes it easier to isolate issues. Taking small steps ensures better control and avoids risking everything.
● Frequent deployments support agile development by enabling quick adaptation to market changes and facilitating continuous learning for faster innovation.
● Frequent deployments promote collaboration between teams, leading to better outcomes and more successful projects.

Use Case:

In a dynamic market, agility is paramount. Deployment frequency measures how frequently code is deployed. Infrequent deployments can cause you to lag behind competitors. Increasing deployment frequency facilitates more frequent rollouts, hence, meeting customer demands effectively.

2. Lead Time for Changes: Streamline Development

The time it takes to implement changes and deploy them to production directly impacts their experience, and this is the lead time for changes.

If we notice longer lead times, which can take weeks, it may indicate that you need to improve the development or deployment pipeline. However, if you can achieve lead times of around 15 minutes, you can be sure of an efficient process. It’s essential to monitor delivery cycles closely and continuously work towards streamlining the process to deliver the best experience for customers.

Why is the lead time for changes important?

● Short lead times in software development are crucial for success in today’s business environment. By delivering changes rapidly, organizations can seize new opportunities, stay ahead of competitors and generate more revenue.
● Short lead times help organizations gather feedback and validate assumptions quickly, leading to informed decision-making and aligning software development with customer needs. Being customer-centric is critical for success in today’s competitive world, and feedback loops play a vital role in achieving this.
● By reducing lead time, organizations gain agility and adaptability, allowing them to swiftly respond to market changes, embrace new technologies and meet evolving business needs. Shorter lead times enable experimentation, learning, and continuous improvement, empowering organizations to stay competitive in dynamic environments.
● Reducing lead time demands collaborative teamwork, breaking silos, fostering shared ownership and improving communication, coordination and efficiency.

Use Case:

Picture your software development team tasked with a critical security patch. Measuring lead time for changes helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipeline or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.

3. Change Failure Rate: Ensuring stability

The change failure rate measures the code quality released to production during software deployments. Achieving a lower failure rate than 0-15% for high-performing dev teams is a compelling goal that drives continuous improvement in skills and processes. Establishing failure boundaries tailored to your organization’s needs and committing to reducing the failure rate is essential. By doing so, you enhance your software solutions and deliver exceptional user experiences.

Why is change failure rate important?

● It enhances user experience and builds trust by reducing failures; we elevate satisfaction and cultivate lasting positive relationships.
● It protects your business from financial risks, and you avoid revenue loss, customer churn, and brand damage by reducing failures.
● Reduce change failures to allocate resources effectively and focus on delivering new features.

Use Case:

Stability is pivotal in software deployment. The change failure rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing or insufficient quality control. Enhancing testing protocols, refining code reviews and ensuring thorough documentation can reduce the failure rate, enhancing overall stability.

4. Mean Time to Recover (MTTR): Minimizing Downtime

Mean time to recover (MTTR) measures the time to recover a system or service after an incident or failure in production. It evaluates the efficiency of incident response and recovery processes. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes. The goal is to build robust systems that can detect, diagnose, and rectify problems. Organizations ensure minimal disruption and work towards continuous improvement in incident resolution.

Why is mean time to recover important?

● Minimizing MTTR enhances user satisfaction by reducing downtime and resolution times.
● Reducing MTTR mitigates the negative impacts of downtime on business operations, including financial losses, missed opportunities and reputational damage.
● Helps meet service level agreements (SLAs) that are vital for upholding client trust and fulfilling contractual commitments.

Use Case:

Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.

Key Use Cases

Development Cycle Efficiency:

Metrics: Lead time for changes and deployment frequency, high deployment frequency, swift lead time:

Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.

Low deployment frequency despite swift lead time:

A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.

Code review excellence:

Metrics: Comments per PR and change failure rate, few comments per PR, low change failure rate:

Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.

Abundant comments per PR, minimal change failure rate:

Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.

Developer Responsiveness:

Metrics: Commits after PR review and deployment frequency, frequent commits after PR review, high deployment frequency:

Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.

Sparse commits after PR review, high deployment frequency:

Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.

Quality Deployments:

Metrics: Change failure rate and mean time to recovery (MTTR), low change failure rate, swift MTTR:

Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.

High change failure rate, rapid MTTR:

A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.

Code Collaboration Efficiency:

Metrics: comments per PR and commits after PR is raised for review

In collaborative software development, optimizing code collaboration efficiency is paramount. By analyzing comments per PR (reflecting review depth) alongside commits.

After PR is raised for review, teams gain crucial insights into their code review processes.

High comments per PR, low post-review commits:

Thorough reviews with limited code revisions post-feedback indicate a need for iterative development. Encouraging developers to iterate fosters a culture of continuous improvement, driving efficiency and learning.

Low comments per PR, high post-review commits:

Few comments during reviews paired with significant post-review commits highlight the necessity for robust initial reviews. Proactive engagement during the initial phase reduces revisions later, expediting the development cycle.

Impact of PR Size on Deployment:

Metrics: Large PR size and deployment frequency

The size of pull requests (PRs) profoundly influences deployment timelines. Correlating large PR size with deployment frequency enables teams to gauge the effect of extensive code changes on release cycles.

High deployment frequency despite large PR size:

Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.

Low deployment frequency with large PR size:

Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.

PR Size and Code Quality:

Metrics: Large PR size and change failure rate

PR size significantly influences code quality and stability. Analyzing Large PR size alongside change failure rate allows engineering leaders to assess the link between PR complexity and deployment stability.

High change failure rate with large PR size:

Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.

Low change failure rate despite large PR size:

A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment.

Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality and agility in software development endeavors.

Help Your Team With DORA Metrics!

In the ever-evolving world of software development, harnessing the power of DORA metrics is a game-changer. By leveraging them, your software team can achieve remarkable results. These metrics are vital to enhancing user satisfaction, mitigating financial risks, meeting service-level agreements and delivering exceptional software solutions.

March 7, 2024
LinearB to Provide Free DORA Metrics Dashboard

LinearB is making available a free dashboard through which they can visualize DevOps Assessment and Research (DORA) metrics from within the company’s software delivery management platform.

DORA metrics enable DevOps teams to track deployment frequency, mean lead time for changes, mean time to recover and change failure rates. The dashboard is currently available via an early access program and is scheduled to be generally available in late fall.

LinearB CEO Ori Keren said the challenge is that not enough DevOps teams track these metrics because there previously was a fee associated with running them. LinearB is now absorbing those costs to help DevOps teams that have adopted the company’s namesake software delivery platform to be more efficient.

In addition to the trailing DORA indicators of DevOps performance, LinearB’s dashboard makes it possible to track leading indicators such as merge frequency and pull request size. In effect, the ability to track DORA metrics should be table stakes for any DevOps team, noted Keren.

LinearB’s 2023 Software Engineering Benchmarks Report based on analysis of more than 3.7 million pull requests (PRs) from more than 2,000 DevOps teams found code reviews are the biggest bottleneck in a DevOps workflow. Best practices for improving code reviews include automating reviewer assignments, setting up automated alerts for changes to be made to deprecated application programming interfaces (APIs) and using automated labels to prioritize workflows. Not surprisingly, the report also noted that startups and smaller companies are much more efficient when it comes to managing DevOps workflows than the average enterprise.

In the meantime, the one issue that DevOps teams are already encountering with the rise of generative AI tools is that the amount of code that needs to be reviewed is exponentially increasing. Unfortunately, the pace at which that code is generated is faster than the pace at which AI is being applied to accelerate DevOps workflows. As a result, DevOps bottlenecks are likely to increase.

However, generative AI will soon be applied to surface recommendations for where DevOps teams can improve, for example, cycle times, noted Keren. Organizations that can effectively apply AI across the entire application life cycle will have a substantial competitive advantage as the pace at which software can be developed and deployed continues to accelerate, he added.

The most important DevOps metric, of course, remains developer productivity. While it’s important for DevOps teams to measure their own efficiency, all that effort is for naught if developers are inefficient. Unfortunately, most of them spend a lot more time managing development environments than they do writing code. The goal for many organizations is to enable DevOps workflows that result in developers being able to deploy higher-quality applications faster.

Each organization will naturally need to determine what level of DevOps maturity makes the most sense to strive for based on how critical software development is to their organization. Regardless, however, the one thing that is certain when it comes to DevOps is there is always room for improvement.

October 4, 2023
Why Enterprises Should Embrace Data-Driven Software Management

Many Fortune 500 companies have recently undergone extensive layoffs and now find themselves having to achieve more with fewer people—while grappling with how to integrate generative AI into their products. They must build while economizing, streamlining and increasing efficiency in every part of their company. Yet one of the most vital cogs of the machinery has struggled to evolve over the past 20 years.

In fact, until now, managing high-tech engineering teams at the world’s biggest companies has not been all that high-tech.

Back in the 1970s, the “waterfall” model was introduced: An inflexible process in which each phase of software development depended on the previous one’s completion. Then, around the 2000s, companies started moving towards “agile” models that tracked progress in two-week sprints.

Now, a third era of software development management is emerging. It’s not waterfall or agile; it’s data-driven.

Current management systems cannot give managers real-time, granular insights so they can make decisions on the spot and run projects in the most efficient way possible. However, data-driven models generate live data on how teams are progressing which has far-reaching effects on businesses’ productivity and future survival.

The emergence of DORA, a framework for tracking development metrics makes this possible. For the first time, new tools enable us to capture the software development life cycle automatically, without any manual input from the engineer, leading to fewer roadblocks, a 20% faster cycle time and greater ROI.

Despite this, only a handful of Fortune 500 companies have embraced data-driven metrics. Here are the reasons that shift should happen now.

Real-time Insights are Game-Changers

Until now, companies have been using inaccurate methods to track their software development teams. Project management reports on engineers’ output are reviewed every two weeks, monthly, or even quarterly. This means that impactful changes are made at a delayed point in time rather than when they can make the greatest difference to cycle time.

In contrast, DORA measures specific real-time performance indicators: Deployment frequency (DF), lead time for changes (LT), mean time to recovery (MTTR) and change failure rate (CFR) (For more information, see here). These far more data-driven metrics measure efficiency and velocity at every key point of day-to-day software development, with feedback to management happening continually.

Often, individual engineers won’t have enough capacity or oversight to inform managers of specific issues during a cycle. Using DORA metrics is like being able to detect that a team has an abnormally high BPM though they are in a rest phase. It’s a signal of illness; that more or different resources are needed. That then requires you to go in and inspect the micro factors that can be adapted.

For any Fortune 500 company, this quick turnaround is vital. Any small issue in a product cycle can easily escalate and become a critical problem in a matter of days. Instead, using DORA can accelerate software development and, ultimately, time to market.

DORA Gives you Internal Benchmarks

But, how can you tell if something is wrong while using DORA? In the first few months of adoption, it’s likely that companies will compare their internal data to industry benchmarks. Unprecedented data has recently been published that offers general performance benchmarks for each DORA metric based on industry research.

But the end game is to be able to compare your DORA metrics to your own internal benchmarks. With time, each company will develop its own personal baselines that better represent internal capabilities and limitations based on innumerable variables.

The purpose of DORA benchmarks, however, is not to draw a fixed red line. Think of it more like a speed limit that ensures that people are working at a steady pace while allowing for flexibility.

A clear sign that your team may be burnt out is a really low cycle time. At times it can be a technical bottleneck, but the beauty of DORA is that it can also point you towards well-being issues.

DORA Needs to be Coupled With Granular Insights

DORA is unparalleled when it comes to flagging managers that something is wrong. DORA metrics can flag that a team has a high cycle time – for example, an abnormally long deployment frequency suggests that engineers are working more slowly or that there may be a build-up of code that isn’t being uploaded to the repository quickly enough during the review process. At that point, you need to zoom in to get more information. There are multiple metrics that provide insights into the exact pain points in a team’s development, and you can develop these internally or via tools or engineering management software that provides more holistic oversight.

In this particular case, you can use a granular metric called “time to merge from first review” to see how fast engineers incorporate feedback from their peers during code reviews. If it is taking a week for the first review to be carried out, integrated into the code and finally uploaded to the general platform, then perhaps there’s a bottleneck occurring during the review process.

Perhaps the most important benefit of Engineering Leadership 3.0 is that it no longer has to be a guessing game. If they sense something’s wrong, they can check their DORA dashboard, receive confirmation of the problem, hone in with granular metrics, and take corrective actions immediately.

Teams that adapt their behavior as a result of these data-driven metrics will add incredible ROI to the company. But these changes can’t happen overnight. It will start with getting buy-in from engineering managers and the executive team. For Fortune 500 companies, that often requires elaborating action plans, integrating external tools to operate the tracking process and training engineering managers (in our case, this takes up to five weeks). But once they’re a part of the business, they will become indispensable in running smoothly without stacking up issues.

September 15, 2023
GitLab Strengthens Remote DevOps Management

GitLab this week moved to provide remote developers with access to a more secure continuous integration/continuous delivery (CI/CD) platform while at the same time adding a dashboard that makes it simpler to track the overall status of application development projects.

David DeSanto, chief product officer for GitLab, said GitLab Remote Development takes advantage of an instance of the GitLab CI/CD platform running on a Kubernetes cluster. That cluster is accessed via a Web-based integrated development environment (IDE) to eliminate the need for developers to create development environments on their local desktops.

That capability reduces developers’ time and effort on tasks other than code and gives organizations more centralized control over the intellectual property being created, noted DeSanto. Organizations will no longer have to worry, for example, about theft of a developer’s laptop that has sensitive data on it, he added. In addition, organizations will be able to more easily create “golden” images that can be consistently accessed by developers residing in multiple locations.

In addition to making it easier to centrally manage a distributed team of developers, GitLab is also adding a GitLab Value Streams Dashboard that provides senior executives with access to metrics that assess the overall health of an application development project. While GitLab has always provided metrics to DevOps teams, DeSanto said this dashboard, available as a private beta, makes it possible to identify, for example, bottlenecks that might result in delayed delivery of an application.

Value stream management (VSM) is, of course, not a new idea. The issue that DevOps teams are trying to come to terms with is whether they need to adopt a dedicated VSM platform. The other option is to simply provide a set of dashboards that enable senior IT and business executives to track projects’ progress without deciphering a set of much lower key performance indicators (KPIs). Previously, the GitLab platform surfaced a lot of nitty-gritty details that don’t provide the context senior executives required, noted DeSanto. The Value Streams Dashboard metric surfaces KPIs based on DevOps Readiness Assessments (DORA) metrics from Google alongside other flow metrics.

These latest offerings are part of a larger case being made for an integrated DevOps platform that reduces the total cost of application development by eliminating the need for organizations to integrate various tools themselves. Each DevOps team can employ as many of the modules GitLab makes available as they need. Once invoked, they take advantage of the same data store as every other element of the GitLab platform. That capability is becoming more critical as organizations embrace platform engineering to reduce the cost of application development using DevOps best practices, said DeSanto.

It’s not clear how much interest there is in replacing legacy CI/CD platforms that, in some instances, have been in place for nearly a decade. However, as the pace at which applications are being built and deployed continues to accelerate, there is no doubt that organizations of all sizes will need to modernize application development processes.

January 25, 2023
How a Mature DevOps Practice Can Strengthen Competitive Advantage

Saman Pourkermani, practice lead, operations effectiveness, Inspirant Group, co-authored this article.

Software development and delivery has become an integral part of every aspect of a business and is no longer simply a support function. Nowadays, most of us, including those in business operations, know the anxieties of having to wait for specific environment release dates to deploy code for urgent fixes or updates to Dev, QA, UAT or production, which slows down productivity. Or worse, an environment has gone down with no backup environment. We all know systems have to be in place to ensure quality and security, but what makes some companies more successful than others when it comes to their DevOps operations?

Any organization that builds and delivers software applications and digital services can benefit fromDevOps. The main difference is the maturity of their DevOps culture and practices. Companies with mature DevOps practices and cultural philosophies are not only able to build and deliver applications faster than those with traditional DevOps practices, but also learn and innovate faster. Mature DevOps capabilities, tooling, and cultural philosophies enable faster innovation by automating and streamlining development, testing, deployment, and infrastructure management processes. The ability to outpace others from both a delivery and learning standpoint is a huge competitive advantage.

Most organizations understand this and as a result, have been heavily investing in maturing their DevOps capabilities and practices. However, most struggle to make meaningful progress to catch up to leading companies.

Why is That?

The most obvious reasons are resource constraints–budgetary, human and/or systems. Many companies have fixed budgets and must balance the tradeoffs between setting up an expensive infrastructure (equipped with yearly upgrades) with other business needs for short-term/long-term benefit gains.

We believe a company’s DevOps maturity is directly associated with and significantly impacted by how well and disciplined they are at making business prioritization and tradeoff decisions, picking what they will focus/work on and what they will not – ideally leaning out how much work they commit to completing.

It goes without saying that most companies that want to but don’t yet have mature DevOps practices have more work than they can handle, from setting up and documenting CI/CD processes to creating business continuity and disaster recovery plans to ensuring they have the right people for execution. These critical elements take time, effort, expertise, and budget to put together. While everyone wishes their teams could get it all done in a timely manner, the reality is that everyone has a finite amount of budget and capacity.

Quite often, progress on DevOps capabilities and automation gets sidelined in favor of ongoing customer delivery work. The trick is to establish a mechanism for balancing teams’ capacity allocation so that it includes a healthy mix of work focused on becoming more effective and efficient at delivery (e.g., maturing DevOps capabilities, improving architecture and design, etc.) and work to fulfill customer needs. In this way, a percentage of a team’s capacity can always be devoted to advancing DevOps delivery capabilities while the rest can be used to address customer requests.

How Can This be Accomplished?

Start With Culture. Culture is a critical factor when it comes to having a mature DevOps practice. Team members must fulfill their individual roles but also operate as a cohesive unit. The mindset of each team member must be focused on the greater good of the whole – not individuals. Has the team established a way of working together that fosters accountability along with encompassing growth and innovation?

Focus on Process. Progress cannot be made without identifying a clear, sensible process. For example, having a well-documented and executed CI/CD process is paramount to DevOps maturity to address all developer pain points and ensure a smooth deployment process. The key is having a documented process that establishes the team’s way of working and addresses the following: Are the right team members engaged? Are there special circumstances that need to be accounted for during the development/configuration/deployment processes? Does everyone have the same understanding of the process at each stage?

Automation and Tools. Have your processes been automated in a way that allows the DevOps practices to happen in the same way each time, without manual intervention? Do you have and know how to use the fundamental tools that all successful DevOps practices need (e.g., build and test code continuously, source control (e.g., GitLab), environment configuration management, performance measurement (e.g., load balancing))? Are deployment pain points adequately identified and addressed to prevent coding/release bottlenecks?

Business Prioritization and Trade-off Decisions. We believe this is the most important aspect of becoming a mature DevOps practice because it allows for progress over time. Balancing the teams’ capacity to include advancing DevOps delivery capabilities is critical for establishing and sustaining a competitive advantage. Optimizing your DevOps capabilities allows you to free up and capitalize on the time, talent and performance of your team members for learning and innovation.

As Jack Welch once stated, “The only sustainable competitive advantage is to learn faster than your competition and to be able to act on what you have learned.” When a software organization becomes too rigid without an adaptive DevOps edge, it will be left in the dust.

November 18, 2022