DORA Metrics for Banking: Boost Velocity

Introduction

Engineering velocity isn't about working harder or adding headcount. For banks, it's about removing friction from the path between idea and customer value, while maintaining the stability and security that financial services demand. Yet most banks struggle to measure what actually drives velocity, relying on vanity metrics like story points or lines of code that tell you nothing about whether software is reaching customers faster and more reliably.

DORA metrics, deployment frequency, lead time for changes, change failure rate, and mean time to restore give you a precise, evidence-based view of software delivery performance. Developed through years of research into high-performing technology organizations, these four metrics correlate strongly with both speed and stability. Banks that improve DORA metrics ship features faster, experience fewer production incidents, and recover from problems more quickly.

For CTOs and engineering leaders in banking, this matters because regulatory scrutiny, complex vendor landscapes, and legacy system constraints create unique challenges that generic productivity advice can't address. This guide shows you how to baseline DORA metrics in your environment, identify and eliminate the bottlenecks slowing you down, and implement a 90-day plan that boosts velocity without compromising control or hiring new teams. Whether you're new to DORA metrics or refining your approach, these strategies apply to banks of any size.

Why DORA Metrics Matter for Banks

Traditional software development metrics often mislead. Tracking velocity in story points assumes all stories carry equal complexity and value when they don't. Measuring individual developer productivity through commits or lines of code incentivizes the wrong behavior of ‘more code, not better outcomes’. Counting hours worked tells you nothing about whether software is actually shipping to customers.

DORA metrics focus on what matters: the flow of changes from ideation to customer impact, and how safely that flow operates. The four metrics work together to paint a complete picture.

Deployment frequency measures how often you release changes to production. High performers deploy multiple times per day. Low performers deploy monthly or less. More frequent deployments correlate with smaller batch sizes, faster feedback loops, and reduced risk per change.

Lead time for changes measures elapsed time from commit to production deployment. This reveals process bottlenecks: slow builds, long-running tests, manual approval queues, and environment provisioning delays. Shortening lead time means features reach customers faster and feedback cycles tighten.

Change failure rate measures the percentage of deployments that cause incidents requiring hotfixes, rollbacks, or patches. This is your quality signal. High performers keep the change failure rate below 15%, proving you can ship fast without breaking things. Low performers see 40-60% failure rates, indicating inadequate testing or poor release practices.

Mean time to restore (MTTR) measures how long it takes to recover from production incidents. Fast recovery requires automated monitoring, documented runbooks, and rollback capabilities. High performers restore service in under an hour. Low performers take days.

Research published in Accelerate: The Science of Lean Software and DevOps demonstrates that organizations improving these four metrics achieve measurably better business outcomes: higher profitability, faster time to market, and greater customer satisfaction. For banks, DORA metrics also support operational resilience requirements under frameworks like the Digital Operational Resilience Act (DORA), which mandates evidence-based ICT risk management.

Good to know: DORA (the regulation) and DORA metrics (the engineering performance framework) share an acronym but come from different origins. However, they complement each other perfectly; DORA metrics give you the measurement foundation to demonstrate operational resilience that DORA (the regulation) requires.

How to Baseline DORA Metrics in Your Bank

You can't improve what you don't measure. Start by instrumenting your delivery pipeline to capture the data needed for each metric.

Deployment Frequency

Pull deployment logs from your CI/CD platform (Jenkins, GitLab CI, GitHub Actions, Azure DevOps) for the past 90 days. Count successful production deployments per application or service. Calculate deployments per day or per week, depending on your baseline.

For banks with multiple environments (dev, staging, UAT, production), ensure you're tracking production deployments specifically. Pre-production deployments don't count; customers can't see them.

If you lack centralized deployment tracking, start logging deployments to a shared system (e.g., database table, metrics platform like Datadog, or even a structured spreadsheet) and instrument going forward. You need at least 30 days of data to establish a meaningful baseline.

Lead Time for Changes

Lead time starts when a developer commits code and ends when that code deploys to production. To measure accurately, you need commit timestamps from version control and deployment timestamps from your CI/CD system.

For a sample of recent deployments, trace individual commits backward to their PR merge time and forward to production deployment. Calculate elapsed hours or days. Group by application, team, or system criticality to identify patterns.

Many banks discover their lead time is measured in weeks, not hours or days. The longest delays typically cluster around: waiting for PR reviews, slow or flaky test suites, manual approval gates, and environment availability.

Change Failure Rate

A change fails if it requires an unplanned hotfix, rollback, or incident response after reaching production. Pull incident records for the past 90 days and correlate them with deployments. What percentage of deployments caused incidents?

This metric requires honest classification. Not every minor bug constitutes a failure; focus on incidents that degraded service availability, caused data errors, or triggered customer complaints. If your organization already tracks severity levels (P0, P1, P2), consider only P0 and P1 incidents as change failures.

If you don't have a clear incident-to-deployment correlation, implement it now. Tag incidents with the deployment that introduced them. This creates accountability and enables root cause analysis.

Mean Time to Restore (MTTR)

For each production incident in your baseline period, calculate the time from detection to full restoration of service. Detection starts when monitoring alerts or customers report the problem. Restoration completes when service returns to normal operation, not when a hotfix deploys (unless that hotfix immediately restores service).

Average MTTR across incidents to get your baseline. Also, examine the distribution; one 48-hour outage can skew your average. Look at the median and 90th percentile MTTR for a fuller picture.

Banks often discover that MTTR varies wildly by system and team. Core banking platforms with runbooks and automated rollback recover in minutes. Ancillary systems without monitoring or documentation take days.

Pro tip: Don't aim for perfection in your baseline. Rough directional data is better than paralysis. If you can track these metrics for just 2-3 critical systems initially, that's enough to start improvement experiments and prove ROI before expanding measurement across the organization.

Five High-Impact Levers to Boost Velocity

Once you've baselined DORA metrics, you can systematically improve them by pulling five levers. Each addresses common bottlenecks banks face.

Lever 1: Automate Repetitive Work

Manual toil kills velocity. Every task humans repeat, such as generating PR descriptions, writing changelog entries, backporting fixes to release branches, and updating dependency versions, consumes time that could go toward building features.

Introduce automation and AI agents for repetitive delivery tasks. AI-powered tools can generate PR descriptions from code diffs, create unit tests from function signatures, and summarize incident root causes from logs and traces. These aren't luxuries; they're force multipliers that elevate engineers from busywork to decision-making.

For example, automate dependency updates using tools like Renovate or Dependabot. Instead of engineers manually checking for updates weekly, automated PRs propose dependency bumps with test results attached. Engineers review and merge in minutes, keeping systems current without dedicated effort.

Similarly, automate deployment runbook generation. When teams deploy new services, capture configuration, dependencies, rollback procedures, and monitoring queries as code. The next time an incident occurs, on-call engineers have complete context immediately.

Lever 2: Optimize Development Flow

Velocity suffers when work queues, pull requests wait for review, builds wait for available agents, and deployments wait for approval meetings. Each queue adds lead time and increases batch size, making changes riskier.

Adopt trunk-based development to reduce integration delays. Instead of long-lived feature branches that diverge from main for weeks, engineers commit small changes directly to trunk (protected by feature flags if needed). This eliminates merge conflicts, reduces batch size, and allows continuous integration to catch issues early.

Parallelize CI/CD pipelines where possible. If your test suite takes 45 minutes to run, split tests across multiple agents and run them concurrently. Cut build time to 10-15 minutes, and lead time drops proportionally.

Visualize work in progress (WIP) limits. Use kanban boards or pipeline dashboards to show how many PRs, builds, and deployments are in flight at any moment. When queues form, address them immediately, reassign reviewers, allocate more CI agents, or defer lower-priority work.

Lever 3: Improve Quality Earlier

Catching defects in production is expensive. They trigger incidents, consume on-call engineer time, erode customer trust, and require emergency hotfixes that bypass the normal process. Shifting quality left, finding issues before code merges, prevents these cascading costs.

Implement comprehensive automated testing at multiple levels. Unit tests validate individual functions. Integration tests verify interactions between components. Contract tests ensure APIs match consumer expectations. End-to-end tests simulate user journeys. Each layer catches different defect types.

Add static analysis to your merge gates. SAST tools (SonarQube, Checkmarx, Semgrep) detect security vulnerabilities and code quality issues. SCA tools (Snyk, WhiteSource) flag vulnerable dependencies. Run these checks on every PR and block merges when critical findings appear.

Invest a sprint in deflaking tests. Flaky tests, otherwise known as tests that fail intermittently without code changes, destroy trust in your CI pipeline. Engineers start ignoring test failures, assuming they're false positives. Systematically identify the top 20 flaky tests, fix or quarantine them, and restore confidence that failing tests signal real problems.

NedBank improved system reliability with Scrums.com by implementing enhanced monitoring and observability across their AWS infrastructure. Using tools like Datadog for performance tracking and BugSnag for error detection, the organization caught issues earlier in the development cycle, reducing production incidents and improving overall data quality. The approach demonstrates that investing in upstream quality controls pays downstream dividends in stability and velocity.

Important: Quality isn't a tradeoff with speed; it's an enabler. High-performing organizations improve both simultaneously by automating quality checks and integrating them into fast feedback loops.

Lever 4: Encode Guardrails as Policy-as-Code

Banks need controls, but manual controls slow delivery. The solution isn't fewer controls; it's automated enforcement that runs instantly and consistently.

Define your minimum quality bar for merging and deploying code, like test coverage thresholds, security scan results, required approvals, and documentation updates. Encode these requirements as pipeline gates enforced by your CI/CD platform. If any check fails, the pipeline stops and notifies the responsible team.

For infrastructure changes, use policy-as-code frameworks (Open Policy Agent, HashiCorp Sentinel, AWS Config Rules) to validate compliance before applying changes. For example, require that all S3 buckets have encryption enabled, all IAM roles follow least-privilege principles, and all EC2 instances use approved AMIs. These checks execute in seconds, giving engineers immediate feedback without waiting for security review meetings.

This approach creates psychological safety. Engineers can move quickly knowing guardrails prevent dangerous mistakes. Controls become invisible enablers rather than visible bottlenecks.

Lever 5: Focus Your Roadmap

Even with perfect delivery mechanics, teams achieve low velocity when they build the wrong things. Feature bloat, gold-plating, and speculative work waste capacity that could go toward high-ROI outcomes.

Ruthlessly prioritize. Use discovery workshops to clarify outcomes, validate assumptions, and cut scope before committing engineering resources. Build rapid prototypes or clickable mockups to test concepts with stakeholders, avoiding expensive rework after development begins.

Measure feature usage post-launch. If features you spent months building go unused, learn from it and adjust prioritization. Focus your roadmap on initiatives that measurably move business metrics: customer acquisition, retention, transaction volume, and cost reduction.

When prioritization discipline exists, engineers spend less time on low-value work and more on initiatives that matter. Deployment frequency might stay constant, but customer impact per deployment increases dramatically, the ultimate velocity multiplier.

Real-World Velocity Gains: What Banks Achieve

Banks implementing these levers consistently see measurable DORA metric improvements within 60-90 days.

Deployment frequency doubles or triples. Teams that deployed monthly start deploying weekly. Teams that deployed weekly move to daily. Smaller batch sizes reduce risk and tighten feedback loops.

Lead time drops 30-50%. Automated tests, parallel builds, and reduced approval queues cut elapsed time from commit to production. Features reach customers in days instead of weeks.

Change failure rate decreases 20-40%. Better testing, static analysis, and incremental rollouts catch defects before customers see them. When failures do occur, they're smaller and easier to diagnose.

MTTR improves 40-60%. Automated monitoring, runbooks, and rollback capabilities accelerate recovery. Incidents that once took hours to resolve now close in minutes.

Investec scaled their engineering capacity with Scrums.com by over 20 seats in under 60 days, supporting multiple high-priority product initiatives simultaneously. The rapid onboarding and structured delivery model enabled the organization to maintain velocity even as headcount grew, demonstrating that process and tooling improvements amplify the impact of additional capacity rather than being replaced by it. This five-year partnership proves that velocity improvements compound over time when supported by disciplined practices.

90-Day Plan: From Baseline to High Performance

Transform delivery performance with a structured 90-day plan focused on quick wins and sustainable habits.

Days 1-30: Measure and Identify Bottlenecks

Instrument DORA metrics for 2-3 pilot systems. Pull 90 days of historical data to establish baselines: current deployment frequency, median lead time, change failure rate, and MTTR.

Map your value stream. For a typical change, trace every step from idea to production, like ticket creation, backlog prioritization, development, PR creation, code review, automated tests, security scans, approvals, deployment, and monitoring. Identify queues and delays at each stage.

Select high-impact intervention points. Where does work wait the longest? Where do changes fail most often? Where do incidents take the longest to resolve? Prioritize fixes based on impact and feasibility.

Define success criteria. Set ambitious but realistic improvement targets for your 90-day window. For example: reduce median lead time by 30%, increase deployment frequency by 50%, decrease change failure rate by 20%, cut MTTR in half.

Days 31-60: Implement Improvements

Roll out trunk-based development and small batch commits. Train engineers on frequent integration patterns and feature flag usage to decouple deployment from release.

Parallelize CI/CD pipelines. Split long-running test suites across multiple agents. Implement caching for dependencies and build artifacts to reduce redundant work.

Add or improve automated tests. Identify gaps in test coverage, particularly for critical user paths, and write integration or contract tests to close them. Deflake the 10-20 most problematic tests.

Introduce AI-powered automation for PR descriptions, test generation, and changelog creation. Measure time saved and engineer satisfaction with the tools.

Establish policy-as-code gates. Configure pipelines to enforce test coverage minimums, block merges with critical security findings, and require reviewers before merging to protected branches.

Pro tip: Celebrate small wins publicly. When deployment frequency increases or lead time drops, share the data with leadership and recognize the teams driving improvement. Momentum builds when progress is visible.

Days 61-90: Expand and Sustain

Extend successful practices to 2-3 additional teams or systems. Use templates and documentation from pilot teams to accelerate adoption.

Implement progressive delivery patterns. Use feature flags to control rollout scope, deploy with canary analysis and monitoring key metrics, and configure automatic rollback on degradation.

Establish an operating rhythm. Schedule weekly delivery health reviews where teams examine DORA metrics, discuss blockers, and commit to experiments for the next week. Monthly reliability reviews assess incident trends, runbook quality, and MTTR improvements.

Conduct a retrospective with stakeholders. Present before-and-after DORA metrics, highlight velocity gains and stability improvements, and outline next steps for expanding the model across the organization.

Integrate DORA metrics into leadership dashboards. Make delivery performance visible at the executive level to maintain focus and investment in continuous improvement.

When Engineering Capacity Becomes the Constraint

Process improvements and automation dramatically boost velocity, but eventually you hit capacity limits. Your teams are moving efficiently, but there's simply more work than available engineers can handle. At this point, adding capacity becomes the highest-leverage intervention.

Traditional hiring takes months. Posting roles, screening candidates, conducting interviews, making offers, and onboarding new hires consume 3-6 months before new team members contribute meaningfully. For banks facing tight product roadmaps or regulatory deadlines, this timeline is unacceptable.

Software development companies specializing in banking and financial services offer a faster alternative. A strategic partner should provide:

• Rapid team scaling with engineers who ramp in weeks, not months, because they've worked in similar banking environments before
• Flexible engagement models that let you scale capacity up or down as priorities shift, without long-term employment commitments
• Cross-functional squads combining engineers, QA, DevOps, and platform expertise so you get complete delivery capacity, not just individual contributors
• Delivery visibility through real-time metrics and dashboards that integrate with your existing DORA measurement, ensuring new capacity maintains performance standards

This approach particularly suits banks pursuing digital transformation initiatives, migrating to cloud infrastructure, or building new customer-facing products where internal teams are fully allocated to maintenance and operations.

Scrums.com's Software Engineering Orchestration Platform (SEOP) enables exactly this model. Our dedicated development teams integrate with your existing processes while bringing embedded DORA metric tracking, automated quality gates, and proven delivery practices from working with institutions like NedBank, PayInc, and Investec. Whether you need 5 engineers or 50, teams ramp quickly and maintain the velocity improvements you've worked hard to achieve.

Conclusion

Engineering velocity in banking isn't a people problem; it's a systems problem. The path to faster delivery runs through measurement, automation, and continuous improvement of the practices that govern how code flows from idea to customer value. DORA metrics give you the measurement foundation. The five levers of automate toil, optimize flow, improve quality early, encode guardrails, and focus your roadmap give you the intervention points.

Within 90 days, you can baseline where you are, implement targeted improvements, and demonstrate measurable velocity gains without hiring new teams or sacrificing stability. The banks that excel in the next decade won't be the ones with the largest engineering headcount—they'll be the ones that remove friction from delivery and compound small improvements into sustained competitive advantage.

Your tooling, processes, and people are ready. What's needed is deliberate focus on the metrics that matter and systematic elimination of the bottlenecks holding you back.

Ready to accelerate your engineering velocity? Explore how Scrums.com's financial software development services help banks implement DORA metrics in their software development teams and boost delivery performance in 90 days, without adding permanent headcount, or read more about DORA compliance in banking.

External Resources

• Accelerate: The Science of Lean Software and DevOps - Research foundation for DORA metrics and high-performing organizations
• DORA Metrics Guide - Google Cloud - Practical implementation guide for measuring the four key metrics
• State of DevOps Report - DORA - Annual research on software delivery performance and organizational outcomes
• Trunk Based Development - Paul Hammant - Definitive guide to trunk-based development practices
• BCBS 239 Risk Data Principles - Basel Committee guidance on risk data aggregation relevant to metrics and reporting