DORA Metrics LinkedIn Content Strategy: 60-Day Posting Plan
This strategic posting sequence is designed to establish you as the go-to authority on DORA metrics while engaging potential clients, including your Fortune 400 contact. The plan includes 1-2 posts daily, alternating between educational content, engagement prompts, and subtle promotion of your consulting services.
Content Categories
Each post is labeled with one of these categories:
- [EDUCATION] - Teaching fundamental concepts about DORA metrics
- [INSIGHT] - Sharing expert analysis and perspective
- [ENGAGEMENT] - Prompting conversation and interaction
- [CASE STUDY] - Sharing anonymized success stories
- [PROMOTION] - Subtle promotion of your services
- [TECHNICAL] - Deep dives into implementation details
- [TOOL] - Focused on Apache DevLake or specific implementation tools
Week 1: DORA Metrics Fundamentals
Day 1
Morning Post [EDUCATION]
After 20+ years in DevOps, I've found that organizations consistently struggle with one question:
"How do we KNOW if our software delivery is actually improving?"
The answer lies in the scientifically-validated DORA metrics:
• Deployment Frequency
• Lead Time for Changes
• Mean Time to Recovery
• Change Failure Rate
Over the next few weeks, I'll be sharing insights on implementing these metrics to transform software delivery.
What's your biggest challenge in measuring DevOps performance?
#DORAMetrics #DevOpsTransformation #SoftwareDelivery
Afternoon Post [ENGAGEMENT]
Quick poll for my network:
How often does your organization typically deploy code to production?
• Multiple times per day
• Weekly to monthly
• Monthly to quarterly
• Quarterly or less frequently
This first DORA metric—Deployment Frequency—is a powerful indicator of your delivery pipeline health.
Drop your answer in the comments, and I'll share how your team compares to industry benchmarks.
#DevOpsPoll #DeploymentFrequency #DORAMetrics
Day 2
Morning Post [EDUCATION]
📊 DORA Metric #1: Deployment Frequency
The first DORA metric measures how often you successfully release to production.
Elite teams: Multiple times per day
High performers: Between once per day and once per week
Medium performers: Between once per week and once per month
Low performers: Less than once per month
Why it matters: Higher frequency = smaller batches = lower risk
The most common obstacle I see with clients? Insufficient automation and manual approval gates.
What's your current deployment frequency?
#DORAMetrics #DeploymentFrequency #DevOpsMetrics
Afternoon Post [TECHNICAL]
Want to start tracking Deployment Frequency but not sure how?
Here's the simplest approach I recommend to clients:
1. Query your deployment tool's API for successful deployments
2. Count deployments per environment per time period
3. Visualize as a simple trend line
For teams starting out, even a manual spreadsheet tracking weekly counts will provide valuable insights.
Pro tip: Apache DevLake can automate this collection across Jenkins, GitLab, GitHub Actions, and more.
#DORAMetrics #DevOpsImplementation #MetricsCollection
Day 3
Morning Post [EDUCATION]
📊 DORA Metric #2: Lead Time for Changes
This measures how long it takes for a commit to reach production.
Elite teams: Less than one hour
High performers: Less than one day
Medium performers: Between one day and one week
Low performers: Between one week and one month
Why it matters: Shorter lead times = faster innovation = competitive advantage
I've helped teams reduce this from weeks to hours by focusing on pipeline efficiency.
#DORAMetrics #LeadTime #DevOpsPerformance
Afternoon Post [INSIGHT]
A common misconception about Lead Time for Changes:
"Our releases are naturally complex and take time - we can't possibly achieve elite performance levels."
The reality: Lead time isn't about rushing complex changes. It's about your delivery system's capability to respond when needed.
Elite performers don't rush every change - they have the CAPABILITY to deliver quickly when required.
This subtle distinction transforms how organizations approach pipeline optimization.
#DORAMetrics #DevOpsTransformation #LeadTimeForChanges
Day 4
Morning Post [EDUCATION]
📊 DORA Metric #3: Mean Time to Recovery (MTTR)
How quickly can you restore service after an incident?
Elite teams: Less than one hour
High performers: Less than one day
Medium performers: Less than one week
Low performers: More than one week
Why it matters: Failures happen—recovery speed determines real-world impact.
The biggest improvement opportunity I see? Automated rollback capabilities.
#DORAMetrics #MTTR #IncidentResponse
Afternoon Post [CASE STUDY]
MTTR Success Story:
A client was averaging 6+ hours to recover from production incidents. We identified that their investigation process was largely manual.
The solution? Implementing enhanced observability and a standard "incident playbook."
Result: MTTR dropped to under 45 minutes within 90 days, reducing business impact by 87%.
Sometimes the biggest improvements come from process changes, not just technology.
#DORAMetrics #MTTR #DevOpsSuccess
Day 5
Morning Post [EDUCATION]
📊 DORA Metric #4: Change Failure Rate
What percentage of your deployments cause incidents or outages?
Elite teams: 0-15%
High performers: 16-30%
Medium performers: 16-30%
Low performers: 16-30%
Wait... the middle tiers have the same percentages? Yes! The research shows that medium and low performers have similar failure rates but drastically different recovery times.
The key difference: Effective testing and fast remediation.
#DORAMetrics #ChangeFailureRate #DevOpsQuality
Afternoon Post [TOOL]
If you're struggling to track Change Failure Rate, you need to connect your deployment data with your incident data.
The tool I recommend to clients is Apache DevLake - it can correlate deployments from CI/CD systems with incidents from PagerDuty, Jira, or ServiceNow.
This correlation is essential to understand which changes are causing problems and why.
#ApacheDevLake #DORAMetrics #DevOpsTools
Day 6
Morning Post [INSIGHT]
The most common question I get about DORA metrics:
"Which metric should we focus on improving first?"
My approach:
1. Measure all four metrics to establish your baseline
2. Identify which is furthest from industry benchmarks
3. Look for the "constraint" in your system (per Theory of Constraints)
4. Focus intensely on that ONE metric initially
For most organizations migrating to cloud, I find Lead Time is often the primary constraint.
What's your experience?
#DORAMetrics #DevOpsStrategy #ContinuousImprovement
Afternoon Post [ENGAGEMENT]
What's your team's biggest obstacle to improving DORA metrics?
• Lack of tooling/automation
• Organizational resistance
• Insufficient technical practices
• Unclear implementation approach
• Something else (comment below)
I'm curious to hear what challenges you're facing in your DevOps measurement journey.
#DORAMetrics #DevOpsChallenges #ContinuousImprovement
Day 7
Morning Post [TECHNICAL]
DORA Metrics Implementation Tip:
Start with simple, directional measures rather than perfect metrics.
For example, if you can't easily measure actual production deployment frequency, start by tracking:
- Merge request frequency to main branch
- QA environment deployment frequency
- Release approvals
These proxy metrics aren't perfect but provide immediate visibility while you build more robust measurement.
Progress over perfection.
#DORAMetrics #DevOpsImplementation #MetricsStrategy
Afternoon Post [PROMOTION]
As I shift into semi-retirement, I'm selectively accepting a limited number of clients (5-10 hours weekly) for DORA metrics consulting and Apache DevLake implementation.
Ideal fit: Organizations with existing DevOps teams who need expert guidance on metrics implementation and improvement strategies.
Interested? Send me a DM for more details on my micro-consulting offerings.
#DORAMetrics #DevOpsConsulting #MetricsImplementation
Week 2: Metrics Collection & Tools
Day 8
Morning Post [TOOL]
Why I recommend Apache DevLake for DORA metrics collection:
• Open source = no vendor lock-in
• Connects to 20+ common DevOps tools
• Flexible dashboard configuration
• Pre-built DORA metrics templates
• Active community support
• Enterprise-grade security options
After evaluating dozens of tools, it offers the best balance of simplicity and power for teams getting serious about metrics.
#ApacheDevLake #DORAMetrics #DevOpsTools
Afternoon Post [TECHNICAL]
Setting up Apache DevLake for DORA metrics in 5 steps:
1. Deploy DevLake (Docker containers or Kubernetes)
2. Connect your data sources (GitHub, Jenkins, Jira, etc.)
3. Configure data collection frequency
4. Select the DORA metrics blueprint
5. Customize dashboards for different stakeholders
The entire setup typically takes less than a day for a DevOps engineer familiar with your toolchain.
#ApacheDevLake #DORAMetrics #DevOpsImplementation
Day 9
Morning Post [INSIGHT]
The biggest mistake I see in DORA metrics implementation?
Starting with the tool instead of the people.
Successful implementations follow this sequence:
1. Educate teams on why metrics matter
2. Agree on definitions and boundaries
3. Start with manual measurement if needed
4. THEN implement the tooling
Tools without buy-in create resistance. Education before automation.
#DORAMetrics #DevOpsTransformation #OrganizationalChange
Afternoon Post [EDUCATION]
DORA Metrics aren't just for application teams!
For data engineering teams:
• Deployment Frequency = How often data pipeline changes deploy
• Lead Time = Time from data model change to production
• MTTR = How quickly data pipeline failures are resolved
• Change Failure Rate = % of pipeline changes causing issues
As more organizations move to cloud-based data platforms, these metrics become increasingly relevant for data teams.
#DataEngineering #DORAMetrics #DataDevOps
Day 10
Morning Post [CASE STUDY]
DORA metrics case study: Cloud Migration
A financial services client was migrating from on-premises to GCP but had no way to measure if the migration was actually improving delivery performance.
We implemented Apache DevLake to track DORA metrics across 12 teams:
• Established pre-migration baseline
• Tracked metrics throughout the transition
• Created executive dashboards showing improvement
Result: Clear evidence that migration improved all 4 metrics, with deployment frequency showing the most dramatic improvement (7x increase).
#CloudMigration #DORAMetrics #GCP
Afternoon Post [TECHNICAL]
Technical tip for measuring Lead Time accurately:
You need to track FOUR timestamps:
1. Initial commit time
2. PR/MR creation time
3. PR/MR merge time
4. Deployment completion time
Many teams only measure from merge to deployment, missing significant upstream delays.
Apache DevLake can track all four phases automatically by connecting your Git and CI/CD systems.
#DORAMetrics #LeadTime #DevOpsMeasurement
Day 11
Morning Post [EDUCATION]
DORA metrics reveal a counterintuitive truth:
The teams that deploy MOST frequently have the LOWEST failure rates.
This contradicts the common belief that "rushing changes to production causes problems."
The reality: Smaller, more frequent changes are less risky than large, infrequent batches.
This is why improving deployment frequency often improves stability metrics too.
#DORAMetrics #DevOpsParadox #ContinuousDelivery
Afternoon Post [ENGAGEMENT]
I'd love to hear from my network:
Has your team successfully improved any DORA metrics? What specific changes made the biggest difference?
Share your success story in the comments!
#DORAMetrics #DevOpsSuccess #ContinuousImprovement
Day 12
Morning Post [TOOL]
Beyond Apache DevLake: Other tools for tracking DORA metrics
• GitHub Actions + GitHub Insights (for GitHub-centric teams)
• GitLab Value Stream Analytics (native GitLab metrics)
• Azure DevOps Analytics (for Microsoft shops)
• CloudBees DevOptics (Jenkins-focused)
• Sleuth.io (emerging player with good UX)
Each has strengths and limitations. The right choice depends on your existing toolchain and specific needs.
#DORAMetrics #DevOpsTools #MetricsImplementation
Afternoon Post [TECHNICAL]
Mapping DORA metrics to GCP-specific tools:
• Cloud Build logs → Deployment Frequency
• Cloud Source Repositories + Cloud Deploy → Lead Time
• Cloud Monitoring alerts → MTTR calculation
• Error Reporting + Deployment Markers → Change Failure Rate
If you're all-in on Google Cloud, you can assemble these native services and export the data to Data Studio/Looker for visualization.
Apache DevLake can also connect to these GCP services directly.
#GCP #DORAMetrics #GoogleCloud
Day 13
Morning Post [INSIGHT]
"You can't improve what you don't measure" is only half the truth.
The full reality: "You improve what you measure, so measure carefully."
Teams optimize for what's measured, sometimes at the expense of what's not measured.
That's why DORA metrics are balanced between speed (frequency, lead time) and stability (MTTR, failure rate).
Measure them together or risk unintended consequences.
#DORAMetrics #MeasurementStrategy #DevOpsPhilosophy
Afternoon Post [CASE STUDY]
Case Study: Addressing Resistance to Metrics
A client team was resistant to implementing DORA metrics, fearing they'd be used as a performance evaluation tool.
Our approach:
• Started with team-level (not individual) metrics
• Made dashboards accessible to everyone
• Celebrated improvements, never criticized declines
• Used metrics to remove obstacles, not assign blame
Result: Within 2 months, the team was actively using metrics to drive their own improvement initiatives.
Culture eats metrics for breakfast.
#DevOpsCulture #DORAMetrics #OrganizationalChange
Day 14
Morning Post [PROMOTION]
I'm excited to announce a limited number of openings for my DORA Metrics Assessment service.
This 3-hour engagement includes:
• Evaluation of your current measurement capabilities
• Baseline metrics establishment
• Tool selection recommendations
• Custom implementation roadmap
Perfect for organizations starting their metrics journey or looking to optimize existing approaches.
DM me if you're interested in securing one of the available slots.
#DORAMetrics #DevOpsConsulting #MetricsImplementation
Afternoon Post [EDUCATION]
DORA metrics aren't just about technology – they reveal organizational dynamics too.
High lead times often indicate:
• Excessive approval processes
• Team dependencies
• Knowledge silos
• Unclear requirements
High change failure rates typically signal:
• Insufficient testing
• Technical debt
• Deployment process issues
• Feature pressure overriding quality
The metrics are technological, but the root causes are often organizational.
#DORAMetrics #DevOpsTransformation #OrganizationalDynamics
Week 3: Metrics in Practice & Improvement Strategies
Day 15
Morning Post [EDUCATION]
Deep dive: Deployment Frequency improvement strategies
Top approaches I've seen work:
1. Feature flags to separate deployment from release
2. Trunk-based development (vs. long-lived branches)
3. Automated testing to enable confident deployment
4. Smaller user stories to enable incremental delivery
5. Infrastructure as Code for consistent environments
Which of these has worked best for your team?
#DORAMetrics #DeploymentFrequency #ContinuousDelivery
Afternoon Post [TECHNICAL]
Technical tip: Use percentiles, not averages, for Lead Time measurement.
Lead time distributions are typically right-skewed:
• P50 (median) represents typical changes
• P90 shows your worst regular performance
• P99 reveals your extreme outliers
A team with a median lead time of 2 days might have a P90 of 15 days, revealing significant inconsistency.
Apache DevLake can calculate these percentiles automatically.
#DORAMetrics #LeadTime #MetricsImplementation
Day 16
Morning Post [EDUCATION]
Deep dive: Lead Time improvement strategies
Most effective approaches I've implemented:
1. Work-in-progress (WIP) limits to reduce multitasking
2. Automated code review tools to accelerate reviews
3. "Swarm programming" on blocked items
4. Standardized environments to reduce integration issues
5. Clear definition of ready to prevent mid-development blocks
The biggest gains often come from process changes, not technical changes.
#DORAMetrics #LeadTime #DevOpsImprovement
Afternoon Post [CASE STUDY]
Lead Time Case Study:
A client's lead time averaged 12 days from commit to production. Analysis showed:
• Code sat in review queues for 3+ days
• Test environment instability added 2+ days
• Manual approvals added 4+ days
We implemented:
• Required review SLAs
• Ephemeral test environments
• Automated policy checks
Result: Lead time reduced to 3 days within one quarter, unlocking 4x more feature delivery.
#DORAMetrics #LeadTime #DevOpsTransformation
Day 17
Morning Post [EDUCATION]
Deep dive: Mean Time to Recovery (MTTR) improvement strategies
Most effective approaches:
1. Automated rollback capabilities
2. Enhanced observability and monitoring
3. Standardized incident response playbooks
4. Post-incident blameless reviews
5. Chaos engineering to practice recovery
The common thread? Preparation and practice before incidents occur.
#DORAMetrics #MTTR #IncidentManagement
Afternoon Post [INSIGHT]
MTTR insight: Recovery time isn't just about technical speed—it's about detection time too.
The MTTR formula that matters:
Time to detect + Time to remediate = MTTR
Elite teams often focus more on reducing detection time through:
• Comprehensive monitoring
• Well-defined alerting thresholds
• Customer-focused SLIs/SLOs
You can't recover from a problem you don't know exists.
#DORAMetrics #MTTR #ObservabilityEngineering
Day 18
Morning Post [EDUCATION]
Deep dive: Change Failure Rate improvement strategies
Most effective approaches:
1. Automated testing at multiple levels
2. Progressive delivery patterns (canary, blue/green)
3. Static code analysis in CI/CD pipelines
4. Production-like testing environments
5. Code review guidelines and practices
Elite teams focus on building quality in rather than testing it in later.
#DORAMetrics #ChangeFailureRate #DevQuality
Afternoon Post [TECHNICAL]
Technical tip: Looking for a quick win on Change Failure Rate?
Implement automated rollbacks based on error rate thresholds.
When a deployment causes error rates to spike above normal, trigger an automatic rollback while you investigate.
This doesn't technically reduce your failure rate immediately, but it dramatically reduces the impact of each failure.
A good interim step while building more robust testing.
#DORAMetrics #ChangeFailureRate #DevOpsTips
Day 19
Morning Post [INSIGHT]
The connection between DORA metrics and business outcomes is now scientifically validated:
• Organizations with elite performance are 2x more likely to meet or exceed business objectives
• Elite performers are 1.8x more likely to grow market share
• Publicly traded elite performers outperform the S&P 500
These aren't just technical metrics—they're leading indicators of business success.
This is why executives should care about DORA metrics.
#DORAMetrics #DevOpsBusiness #DigitalTransformation
Afternoon Post [ENGAGEMENT]
Let's discuss a common debate:
Should DORA metrics be used for team comparisons?
Some argue they provide healthy competition and benchmarking.
Others say team comparisons create perverse incentives.
My view: Compare teams to their own historical trends, not to each other. Teams have different contexts, technologies, and starting points.
What's your perspective on this?
#DORAMetrics #DevOpsLeadership #PerformanceMeasurement
Day 20
Morning Post [TECHNICAL]
Advanced Apache DevLake configuration tip:
Connect your incident management system (like PagerDuty) to properly calculate MTTR and Change Failure Rate.
Three key pieces to configure:
1. Incident creation timestamp
2. Incident resolution timestamp
3. Incident-to-deployment correlation (usually by date/time)
Without this integration, you're missing half the DORA metrics picture.
Sample configuration available on request.
#ApacheDevLake #DORAMetrics #IncidentManagement
Afternoon Post [TOOL]
Quick tip: If you're just starting with DORA metrics and not ready for a full tool implementation, here's a simple Google Sheet template I created for manual tracking:
[Link to downloadable template]
It's not scalable long-term, but it's a great way to start building the measurement habit while you implement more robust solutions.
#DORAMetrics #StartingSmall #DevOpsMeasurement
Day 21
Morning Post [INSIGHT]
The most underrated aspect of DORA metrics implementation:
Executive education and buy-in.
Without leadership understanding, metrics become:
• Just another dashboard nobody looks at
• Used incorrectly for performance evaluation
• Ignored when making resource decisions
• Abandoned when showing uncomfortable truths
Technical implementation is the easy part. Leadership alignment is where most efforts succeed or fail.
#DORAMetrics #ExecutiveAlignment #DevOpsLeadership
Afternoon Post [PROMOTION]
I'm excited to share that I'll be releasing a compact guide on "Implementing DORA Metrics with Apache DevLake" next month.
This step-by-step implementation guide will cover:
• Tool configuration
• Integration with common DevOps tools
• Dashboard creation
• Common pitfalls to avoid
DM me if you'd like early access when it's ready.
#DORAMetrics #ApacheDevLake #DevOpsGuide
Week 4: Advanced Topics & Cloud Migration
Day 22
Morning Post [EDUCATION]
DORA metrics in the context of cloud migration:
One of the primary benefits of cloud adoption should be improved delivery performance—but many organizations don't see the gains they expect.
DORA metrics provide the objective evidence to:
• Establish pre-migration baseline
• Track improvement during migration
• Validate ROI after completion
• Identify teams needing additional support
Don't migrate without measuring!
#CloudMigration #DORAMetrics #CloudAdoption
Afternoon Post [TECHNICAL]
Technical implementation challenge: Tracking DORA metrics across hybrid environments.
When migrating from on-prem to cloud, you'll need to:
1. Collect metrics from both environments
2. Tag deployments by environment type
3. Create comparison views to track progress
4. Account for different toolchains during transition
Apache DevLake handles this well with its multi-source capability and tagging features.
#HybridCloud #DORAMetrics #CloudMigration
Day 23
Morning Post [INSIGHT]
The fifth (unofficial) DORA metric worth tracking: Deployment Pain.
This qualitative metric captures:
• How stressful deployments feel
• Whether people avoid deploying on Fridays
• How much manual intervention is required
• After-hours deployment requirements
Teams with high deployment pain scores typically have poor DORA metrics—but sometimes the pain is visible before the metrics deteriorate.
A simple 1-5 survey can reveal valuable insights.
#DORAMetrics #DeploymentPain #DevOpsHealth
Afternoon Post [CASE STUDY]
GCP Migration Case Study:
A client migrating a monolithic application to microservices on GCP was struggling to determine if the effort was worthwhile.
We implemented DORA metrics tracking for:
• Legacy components (pre-migration)
• Newly migrated services
• Components in transition
This provided clear evidence that migrated services achieved:
• 8x higher deployment frequency
• 74% reduction in lead time
• 45% reduction in change failure rate
The metrics justified additional investment in accelerating migration.
#GCPMigration #DORAMetrics #CloudTransformation
Day 24
Morning Post [EDUCATION]
Advanced topic: DORA metrics across the application lifecycle
Different expectations apply at different stages:
• New product development (high frequency, tolerance for failures)
• Growth phase (balanced metrics approach)
• Maintenance mode (stability prioritized over frequency)
Single benchmark targets don't make sense across these stages.
The key is setting appropriate target bands for each application based on its lifecycle stage.
#DORAMetrics #ApplicationLifecycle #DevOpsStrategy
Afternoon Post [TOOL]
For those implementing Apache DevLake, here's a quick reference architecture:
[Simple architecture diagram showing DevLake connected to various tools]
Key components:
• Core collection engine
• API connectors to data sources
• Transformation layer
• Metrics computation engine
• Visualization layer (Grafana)
Deployment options include Docker compose (simplest) or Kubernetes (most scalable).
#ApacheDevLake #DORAMetrics #TechnicalArchitecture
Day 25
Morning Post [INSIGHT]
Counter-intuitive finding from DORA research:
Elite performing teams spend 33% LESS time on operational work and unplanned work than low performers.
This challenges the belief that "moving fast breaks things."
The reality: Good technical practices and automation create both speed AND stability, creating a virtuous cycle of improvement.
How much of your team's time goes to unplanned work?
#DORAMetrics #DevOpsParadox #TechnicalExcellence
Afternoon Post [TECHNICAL]
Data pipeline-specific metrics that complement DORA:
1. Pipeline Deployment Frequency
2. Data Freshness (time from source change to availability)
3. Pipeline Recovery Time
4. Pipeline Failure Rate
5. *NEW* Data Quality Change Failure Rate
For data teams, the last metric is crucial: what % of pipeline changes cause data quality issues?
This adapted framework works well for data engineering teams.
#DataEngineering #DORAMetrics #DataOps
Day 26
Morning Post [EDUCATION]
Beyond the four DORA metrics: The capabilities that drive performance.
The DORA research identified 24 technical capabilities that enable better metrics, including:
• Continuous testing
• Trunk-based development
• Deployment automation
• Proactive monitoring
• Integration of security into development
Measuring the metrics tells you where you stand. Implementing these capabilities is how you improve.
#DORACapabilities #DevOpsTransformation #ContinuousImprovement
Afternoon Post [ENGAGEMENT]
I've been sharing a lot about DORA metrics implementation.
What specific aspect would you like me to dive deeper on next week?
• Tool selection and configuration
• Improvement strategies for specific metrics
• Executive communication approaches
• Adoption strategies and overcoming resistance
• Something else (comment below)
Your input helps me share the most valuable content!
#DORAMetrics #CommunityFeedback #DevOpsContent
Day 27
Morning Post [CASE STUDY]
Real-world improvement example:
A client's most problematic service had:
• Monthly deployments (at best)
• 3-week lead times
• 22% change failure rate
After implementing targeted improvements:
• Weekly deployments
• 4-day lead time
• 8% change failure rate
The key intervention? Breaking down large user stories into smaller, more manageable pieces, which enabled everything else to improve.
Sometimes the simplest changes have the biggest impact.
#DORAMetrics #DevOpsSuccess #ContinuousImprovement
Afternoon Post [PROMOTION]
I'm looking for 2-3 organizations interested in a complementary DORA metrics assessment.
In exchange for allowing me to use anonymized findings in my upcoming guide, I'll provide:
• Current metrics baseline assessment
• Comparison to industry benchmarks
• Specific improvement recommendations
• Implementation guidance for Apache DevLake
If your organization is interested, DM me for details.
#DORAMetrics #DevOpsAssessment #FreeCounseling
Day 28
Morning Post [TECHNICAL]
Advanced implementation scenario: Measuring DORA metrics for microservices architectures.
The challenge: 100+ services with independent deployment cycles.
Solution approach:
1. Group services into "products" or "value streams"
2. Create roll-up metrics at the group level
3. Maintain service-level detail for troubleshooting
4. Tag services by criticality tier
This provides meaningful high-level metrics without losing granular insights.
#Microservices #DORAMetrics #DevOpsMeasurement
Afternoon Post [INSIGHT]
"But our industry is different" - addressing the most common objection to DORA metrics.
The research shows that industry vertical has surprisingly little correlation with performance. High and low performers exist in every industry.
Regulated industries face additional constraints but can still achieve elite performance through:
• Automated compliance controls
• Compliance-as-code approaches
• Risk-based deployment strategies
Don't let your industry be an excuse for poor metrics.
#DORAMetrics #RegulatedIndustries #DevOpsTransformation
Week 5-8: Content Strategy (Abbreviated Examples)
Week 5: Success Patterns & Anti-Patterns
Example Posts:
The most common anti-pattern I see in DORA metrics implementation: measuring without acting.
Teams collect data, create dashboards, and then... nothing changes.
Effective implementation requires:
1. Regular review cadences
2. Clear accountability for improvements
3. Resources allocated for addressing findings
4. Celebration of progress
Measurement without action is just vanity metrics.
#DORAMetrics #DevOpsTransformation #MeasurementAntiPatterns
Success pattern: The "Metrics Champion"
Organizations that successfully implement DORA metrics typically have a dedicated champion who:
• Deeply understands the metrics and their context
• Has credibility with both technical teams and leadership
• Can translate metrics into improvement actions
• Persistently advocates for data-driven decisions
Without this champion role, metrics initiatives often fade away within 6 months.
#DORAMetrics #DevOpsLeadership #MetricsChampion
Week 6: Apache DevLake Deep Dive
Example Posts:
Apache DevLake configuration tip:
When setting up the GitHub connector, use these specific settings to ensure accurate lead time measurement:
[Configuration screenshot]
This ensures DevLake captures the entire development lifecycle rather than just the deployment phase.
For a full configuration guide, DM me for my setup documentation.
#ApacheDevLake #DORAMetrics #DevOpsTools
Common Apache DevLake troubleshooting scenario:
If your deployment frequency metrics seem unusually low, check these common issues:
1. Deployment tool connector configuration
2. Deployment recognition regex patterns
3. Environment tagging configuration
95% of the time, missing deployments come down to one of these configuration issues.
#ApacheDevLake #DORAMetrics #Troubleshooting
Week 7: GCP-Specific DORA Implementation
Example Posts:
GCP-native DORA implementation architecture:
[Architecture diagram]
This reference architecture uses:
• Cloud Build for deployment tracking
• Cloud Monitoring for incident detection
• BigQuery for metrics storage
• Looker/Data Studio for visualization
Perfect for organizations fully committed to the Google Cloud ecosystem.
#GCP #DORAMetrics #CloudNative
When implementing DORA metrics for GCP migrations, measure these phases separately:
1. Pre-migration baseline (on-premises)
2. Migration phase (hybrid operation)
3. Post-migration stabilization
4. Optimization phase
Each phase has different expected metrics patterns, and comparing them provides powerful insights into your migration ROI.
#GCPMigration #DORAMetrics #CloudTransformation
Week 8: Industry Specific Applications & Final Promotion
Example Posts:
DORA metrics for financial services:
For regulated financial institutions, these adaptations have proven effective:
• Change approval boards → Automated policy checks
• Manual testing → Automated compliance verification
• Documentation requirements → Documentation-as-code
• Risk-based deployment approaches
One banking client increase