Metric Content long posts

Month 1 LinkedIn Content Calendar - Complete Posts

Week 1 - Post 1: DORA Metrics Are Just the Beginning

DORA Metrics Are Just the Beginning: Why Most Teams Measure the Wrong Things

Your deployment frequency is up 300%. Lead time dropped to 2 hours. Change failure rate hit an all-time low.

Congratulations—you’re now shipping faster than ever.

But are you shipping the right things?

After 20+ years scaling engineering teams and implementing DORA frameworks across dozens of organizations, I’ve seen a troubling pattern: Teams optimize for delivery speed while completely ignoring feature adoption and business value.

The DORA Blind Spot

DORA metrics answer “How fast can we ship?” but ignore the critical question: “Should we have shipped this at all?”

I’ve worked with teams that achieved stellar DORA scores while building features that: • Less than 15% of users actually adopted • Generated zero measurable business impact
• Solved problems customers didn’t have • Required significant support overhead

Their engineering metrics looked fantastic. Their business outcomes were mediocre at best.

Beyond the Foundation

DORA metrics are essential—but they’re the foundation, not the finish line. The organizations that truly transform their engineering impact expand their measurement framework to include:

✅ Feature Adoption Rate - What percentage of users engage with new capabilities within 30/60/90 days?

✅ Value Realization Time - How long before features deliver measurable business outcomes?

✅ Engineering ROI - Can you connect development investments to revenue, retention, or cost reduction?

✅ Customer Problem Resolution - Are you building solutions to validated customer pain points?

The Strategic Shift

The most successful engineering leaders I work with make this mental shift:

❌ “We need to ship features faster” ✅ “We need to ship valuable features faster”

❌ “Our deployment frequency is industry-leading”
✅ “Our feature adoption proves we’re solving real problems”

❌ “Engineering productivity is up 40%” ✅ “Engineering investments drove $2M in additional ARR”

What This Means for Your Team

Start with DORA metrics—they’re non-negotiable for modern engineering organizations. But don’t stop there.

Ask your teams: • How do we measure if features create customer value? • What’s our process for validating problem/solution fit before development? • Can we connect engineering work to business outcomes?

The future belongs to engineering organizations that measure both delivery performance AND value creation.

What metrics beyond DORA does your team track? Share your approach in the comments.

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Ready to expand your measurement framework beyond basic DORA metrics? Let’s discuss how to align engineering performance with business value creation.

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Week 1 - Post 2: Platform Engineering’s Dirty Secret

Platform Engineering’s Dirty Secret: Tools Don’t Transform Teams

$500KinvestedinBackstage.Another$300K on custom tooling. Developer portal looking pristine.

Yet your platform adoption is stuck at 30%.

Welcome to platform engineering’s dirty secret: The best tools in the world can’t fix organizational dysfunction.

After building platforms at organizations from 100 to 5,000+ engineers, I’ve learned this uncomfortable truth: Platform success isn’t about the technology stack—it’s about whether teams understand why and how to use what you’ve built.

The Platform Paradox

Most platform engineering initiatives follow this pattern:

1. “We need better developer experience”
2. Build/buy sophisticated tooling (Backstage, custom IDP, etc.)
3. Launch with fanfare and training sessions
4. Watch adoption plateau at disappointing levels
5. Blame “cultural resistance” or “change management”

But here’s what actually happened: You solved the wrong problem.

The Real Platform Problem

Teams don’t adopt platforms because of missing features or poor UX (though those matter). They fail to adopt because:

❌ They don’t understand how platform capabilities connect to their daily pain points ❌ No one showed them the “why” behind the “what”
❌ Platform benefits aren’t obvious in their specific context ❌ They lack confidence to experiment with new approaches ❌ Success metrics focus on platform usage, not team outcomes

The Integration Imperative

Here’s my contrarian take: Platform + Team transformation must happen simultaneously.

The most successful platform rollouts I’ve led include: ✅ Cohort-based team training while platform features are being built ✅ Real-time adoption coaching as teams encounter new capabilities ✅ Team-specific use case development rather than generic documentation ✅ Shared learning sessions where teams teach each other platform wins ✅ Metrics that measure team outcomes, not just platform usage

What This Looks Like

Instead of: “Here’s our new deployment pipeline, please use it” Try: “Let’s solve your Friday afternoon deployment anxiety together”

Instead of: “Platform adoption is at 40%“
Ask: “Are the teams using our platform shipping more confidently?”

Instead of: “We built what engineering asked for” Consider: “We’re transforming how teams think about their delivery capabilities”

The Strategic Shift

Platform engineering isn’t a technical problem—it’s an organizational transformation problem that happens to involve technology.

The platform teams that succeed treat adoption as a change management challenge, not a feature development challenge.

Your platform’s success isn’t measured by how many teams log into your portal. It’s measured by how many teams can’t imagine working without it.

Platform engineers: What’s been your biggest adoption challenge? What worked (or didn’t) for driving team transformation alongside tool rollout?

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Building a platform that teams actually want to use? Let’s discuss integration strategies that drive real adoption.

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Week 1 - Post 3: The Micro-Metrics Trap

The Micro-Metrics Trap: Why Department-Level KPIs Kill Global Performance

Your frontend team’s velocity is up 40%. Backend team reduced bug count by 60%.
QA team cut testing time in half. Infrastructure team improved uptime to 99.97%.

So why is your overall product delivery slower than last quarter?

Welcome to the micro-metrics trap.

After two decades of scaling engineering organizations, I’ve watched countless teams optimize their departmental KPIs while accidentally destroying end-to-end flow.

The Optimization Illusion

Here’s the pattern I see repeatedly:

Frontend optimizes for story points completed → Creates integration bottlenecks Backend optimizes for code quality → Increases review cycle time
QA optimizes for defect detection → Extends testing phases Infrastructure optimizes for stability → Slows deployment frequency

Each team looks great on their individual dashboard. The customer experience suffers.

Why Local Optimization Fails

Systems thinking teaches us that optimizing individual components often degrades overall system performance.

In software delivery, this manifests as: ❌ Handoff delays between optimized silos ❌ Queue buildup as teams optimize different metrics ❌ Integration debt from independently optimized components
❌ Conflicting priorities that cancel out local improvements ❌ Invisible waste in the spaces between teams

The DORA Antidote

This is why DORA metrics matter—they measure end-to-end flow, not departmental efficiency.

When teams optimize for: ✅ Lead Time (idea to production) instead of individual velocity ✅ Deployment Frequency (system-wide) instead of local throughput ✅ Mean Time to Recovery (organizational) instead of team uptime ✅ Change Failure Rate (holistic) instead of department defect rates

…the entire delivery system improves together.

The Strategic Question

Ask yourself: “If every team hit their individual goals, would our customers get better outcomes faster?”

If the answer isn’t an obvious “yes,” you’re measuring the wrong things.

Real-World Impact

I recently worked with a team where:

• Individual team metrics showed 35% improvement across the board
• Global DORA metrics showed 15% degradation in lead time
• Root cause: Teams were optimizing for local efficiency at the expense of system flow

The fix? Align all team metrics with global flow metrics.

Beyond the Trap

Successful engineering organizations I work with:

1. Set global metrics first (DORA + business outcomes)
2. Derive team metrics that support global goals
3. Reward collaboration over local optimization
4. Measure system flow more than component efficiency
5. Review end-to-end impact of all local improvements

Your micro-metrics should accelerate macro-outcomes, not compete with them.

Engineering leaders: What local optimizations have accidentally hurt your global performance? How do you balance team autonomy with system-wide flow?

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Struggling to align team metrics with delivery outcomes? Let’s discuss measurement strategies that improve both local and global performance.

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Week 2 - Post 1: Developer Workspace as Platform Component

Developer Workspace as Platform Component: The Missing Piece

Your platform has everything: CI/CD pipelines, observability, deployment automation, service catalogs, security scanning.

But developers still spend 2 hours setting up local environments for new services.

You’re missing the most critical platform component: the developer workspace itself.

After building internal developer platforms across dozens of organizations, I’ve learned that workspace integration is the difference between platform adoption and platform abandonment.

The Workspace Blind Spot

Most platform engineering efforts focus on: ✅ Production infrastructure automation ✅ Deployment pipeline standardization
✅ Service discovery and networking ✅ Monitoring and alerting integration ✅ Security and compliance tooling

But they ignore: ❌ Local development environment consistency ❌ Workspace-to-platform connectivity ❌ Developer onboarding automation ❌ Local testing with platform services ❌ Development workflow integration

Why This Kills Adoption

When developers can’t seamlessly connect their workspace to platform capabilities:

• Platform benefits feel disconnected from daily work
• Context switching creates friction and resistance
• Onboarding new team members becomes painful
• Platform value proposition becomes abstract
• Teams build workarounds that bypass your platform

The Integration Imperative

Successful platforms treat developer workspace as a first-class platform component:

🔧 Standardized Development Environments

• Consistent tooling, dependencies, and configurations
• One-command environment setup for any service
• Automatic platform service connectivity

⚡ Workspace-Platform Bridge

• Local development that mirrors platform behavior
• Easy testing against platform-managed services
• Real-time platform integration feedback

🚀 Onboarding Automation

• New developer can contribute to any service within hours
• Automatic workspace provisioning with platform access
• Context-aware guidance for platform capabilities

Strategic Implementation

The platform teams I work with approach workspace integration strategically:

1. Audit current developer pain - Where do teams waste time on environment issues?
2. Standardize incrementally - Start with highest-impact services
3. Integrate platform services - Make local development feel like production
4. Automate onboarding - Measure time-to-first-commit for new developers
5. Measure adoption through usage - Platform success = daily developer workflow integration

Real-World Impact

One platform team I worked with saw:

• 75% reduction in “it works on my machine” issues
• 80% faster onboarding for new developers
• 40% increase in platform service adoption
• Developer satisfaction scores increased from 6.2 to 8.4

The difference? They made platform capabilities feel native to daily development work.

The Strategic Question

Your platform isn’t just about production infrastructure—it’s about the entire developer experience from workspace to deployment.

Platform engineers: How integrated is your developer workspace with platform capabilities? What’s your biggest onboarding or local development friction point?

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Building a platform that developers actually love using? Let’s discuss workspace integration strategies that drive real adoption.

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Week 2 - Post 2: Why Your Deployment Frequency Doesn’t Matter

Why Your Deployment Frequency Doesn’t Matter (If You’re Building the Wrong Features)

Deployment frequency: 47 times per day Lead time: 23 minutes
Change failure rate: 0.8% MTTR: 4 minutes

Impressive DORA metrics. Terrible business outcomes.

This team was a DevOps success story and a product failure simultaneously.

After implementing DORA frameworks across 50+ engineering teams, I’ve discovered an uncomfortable truth: Operational excellence without product-market alignment is just expensive waste.

The Velocity Trap

The conversation usually goes like this:

Engineering: “We’re deploying 10x more frequently than last year!”
Product: “But feature adoption is down 30%…” Business: “Where’s the ROI on all this DevOps investment?”

Here’s what happened: The team optimized their delivery machine without optimizing what they were delivering.

Beyond the DORA Foundation

DORA metrics are essential—they measure your ability to respond to market needs quickly. But they don’t measure whether you’re responding to the RIGHT market needs.

The most successful engineering organizations I work with expand their measurement framework:

📊 DORA Foundation Metrics (How fast can we respond?)

• Deployment Frequency
• Lead Time for Changes
• Change Failure Rate
• Mean Time to Recovery

📈 Value Creation Metrics (Are we responding to the right things?)

• Feature Adoption Rate (% users engaging within 30/60/90 days)
• Time to Value (How quickly features drive business outcomes)
• Customer Problem Resolution (Are we solving validated pain points?)
• Engineering ROI (Revenue/retention/cost impact per development investment)

The Strategic Shift

❌ “We need to ship features faster” ✅ “We need to ship valuable features faster”

❌ “Our deployment frequency is industry-leading” ✅ “Our rapid deployment enables quick customer feedback loops”

❌ “Look at our operational efficiency gains”
✅ “Look at how operational efficiency drives product experimentation”

Real-World Application

One team I worked with had stellar DORA scores but struggled with business impact. We implemented this framework:

1. Pre-development validation - Feature requests required customer problem evidence
2. Post-deployment measurement - Every feature tracked adoption and business metrics
3. Learning integration - Fast deployment enabled rapid iteration based on actual usage
4. Value-driven prioritization - Backlog prioritized by both development effort AND expected impact

Result: Same deployment frequency, 300% improvement in feature adoption, measurable business impact.

The Platform Connection

This is why platform engineering matters: Great platforms enable both fast delivery AND fast learning.

Your platform should support:

• Rapid feature experimentation
• Easy feature flagging and rollback
• Built-in adoption tracking
• Quick iteration based on customer feedback

Your DORA metrics should serve your learning velocity, not just your shipping velocity.

Engineering leaders: How do you balance delivery speed with delivery value? What works (or doesn’t) for connecting deployment frequency to business outcomes?

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Ready to align engineering velocity with business value? Let’s discuss measurement frameworks that matter to both technical and business leaders.