66% of Developers Don't Trust Their Metrics - Here's How We Rebuilt Credibility

[vp_eng_keisha]

Recent research shows that 66% of developers don’t believe their productivity metrics reflect their actual contributions. This isn’t just a measurement problem - it’s a trust crisis that undermines everything we’re trying to accomplish.

I want to share how we addressed this at my organization, and what worked.

Why Developers Don’t Trust Metrics

When I interviewed engineers about their distrust, the themes were consistent:

1. “Metrics are used against us” - They’ve seen peers punished for low numbers
2. “The numbers don’t match reality” - Their best work often doesn’t show up in metrics
3. “Nobody asked us” - Metrics were imposed, not co-created
4. “Gaming is rewarded” - They’ve watched colleagues game metrics and get promoted

The 18-Month Journey to Rebuild Trust

Here’s what we did:

Phase 1: Audit and Remove (Months 1-3)

• Removed all metrics from performance reviews
• Eliminated team comparison dashboards
• Stopped manager bonuses tied to velocity
• Publicly acknowledged past metric misuse

Phase 2: Co-Create (Months 4-8)

• Formed a Metric Council with engineers, managers, and product
• Asked: “What would you WANT to measure to improve?”
• Engineers chose metrics they trusted
• Made all metric definitions public and debatable

Phase 3: Implement Holistically (Months 9-14)

• Combined DORA with SPACE framework
• Added developer sentiment surveys
• Included qualitative reviews alongside quantitative
• Built “metric health” checks to detect gaming

Phase 4: Sustain (Ongoing)

• Regular reviews of whether metrics still serve us
• Open invitation to challenge any metric
• No metric becomes a target for compensation

What We Measure Now

Our measurement approach includes:

Quantitative (DORA + Platform Metrics)

• Deployment frequency, lead time, CFR, MTTR
• Developer wait time (builds, deploys, environments)
• Toil ratio (time on repetitive tasks)

Qualitative (SPACE-Inspired)

• Satisfaction and well-being surveys (quarterly)
• Developer NPS (“Would you recommend working here?”)
• Pride in work assessments

Outcome Correlation

• Business impact per engineering effort
• Customer satisfaction tied to specific releases
• Revenue influence

Results After 18 Months

Metric	Before	After
Engineers who trust metrics	34%	78%
Voluntary metric gaming	High	Minimal
Manager-engineer trust scores	3.2/5	4.4/5
Retention (senior engineers)	72%	89%

Key Lessons

1. Trust is rebuilt through actions, not announcements
2. Engineers must co-own the metrics
3. Qualitative + Quantitative is non-negotiable
4. Gaming detection should be built in from day one

The 66% distrust number should be a wake-up call for every engineering leader. How are you addressing metric trust in your organization?

[cto_michelle]

Keisha, your 18-month journey is exactly the kind of organizational change leadership that I want to see more of. Let me add the executive perspective on getting buy-in for this transformation.

The Executive Buy-In Challenge

When I proposed similar changes to my board and CEO, I faced resistance:

• “But how will we know if engineering is improving?”
• “Won’t removing metrics from reviews make performance subjective?”
• “Other companies use these metrics in their benchmarks”

How I Made the Case

1. Show the Cost of Distrust

I calculated what metric gaming and distrust was costing us:

• Attrition of senior engineers: $150K+ per departure in replacement costs
• Hidden quality issues: Customer escalations, technical debt
• Time spent gaming: Estimated 10% of engineering capacity

The total was over $2M annually. That got their attention.

2. Propose Business Outcome Metrics

Instead of DORA, I suggested the board track:

• Revenue per engineering hour (are we building valuable things?)
• Customer satisfaction by release (are customers happy with what we ship?)
• Engineer retention by tenure (are we sustainable?)

These metrics connected to things the board already cared about.

3. Commit to Transparency

I promised quarterly reports that would include:

• Quantitative metrics (DORA, business outcomes)
• Qualitative signals (sentiment surveys, team health)
• Correlation analysis (do better numbers mean better outcomes?)

The combination made leadership comfortable that we weren’t abandoning measurement - we were improving it.

The Leadership Lesson

Your point about trust being rebuilt through actions is crucial. At the executive level, this means:

1. Be willing to look bad in the short term - Removing metrics before replacements are in place takes courage
2. Model the behavior - I stopped asking about DORA numbers in my skip-levels
3. Celebrate the right things - I publicly praised teams for sustainable practices, not metric improvements

Your 34% to 78% trust improvement is remarkable. That’s a cultural shift that will pay dividends for years.

[data_rachel]

Keisha, your Metric Council approach is exactly what I advocate for. Let me add some statistical perspective on building trustworthy measurement systems.

Why Traditional Metrics Fail the Trust Test

From a measurement theory standpoint, most engineering metrics violate basic principles:

1. Validity: Do they measure what they claim to measure?

   • Lines of code doesn’t measure productivity
   • Deployment frequency doesn’t measure value delivered

2. Reliability: Are they consistent across contexts?

   • A “deploy” means different things to different teams
   • “Incidents” get classified differently by different people

3. Sensitivity: Do they respond to real changes?

   • Metrics often stay flat despite real improvements
   • Or change dramatically due to classification shifts, not actual change

Building Metrics Developers Will Trust

Here’s my framework for trustworthy measurement:

1. Multi-source triangulation

Never rely on a single metric. Cross-validate:

• Quantitative signals (DORA, platform metrics)
• Qualitative signals (surveys, interviews)
• Outcome signals (customer behavior, business results)

If all three agree, you probably have real signal. If they diverge, dig deeper.

2. Transparent definitions

Every metric should have a public “spec” that includes:

• Exact calculation methodology
• Known limitations
• What it does NOT capture
• When it was last updated

Engineers trust what they can verify.

3. Gaming-resistance by design

Design metrics with counter-metrics:

• High deployment frequency + stable change failure rate
• Fast lead time + sustained quality perception
• High throughput + maintained developer satisfaction

Gaming one should naturally hurt another.

4. Statistical process control

Treat metric movements statistically:

• Is this change within normal variation?
• Is there a detectable trend?
• Did something structural change?

Celebrating noise as signal destroys trust.

The SPACE Framework Validation

Research behind SPACE (Satisfaction, Performance, Activity, Collaboration, Efficiency) shows that combining qualitative and quantitative measures produces more valid assessments than either alone.

Your 78% trust score validates that approach empirically.

[eng_director_luis]

Keisha, implementing complementary metrics in regulated environments has its own unique challenges. Let me share what worked in financial services.

The Regulatory Complication

In banking, metrics aren’t just internal tools - they’re often part of regulatory submissions. This creates additional constraints:

1. Auditors want consistency - Changing definitions mid-year raises red flags
2. Comparison expectations - Regulators benchmark us against peers
3. Documentation requirements - Every metric needs a paper trail

This makes the “remove and rebuild” approach harder. We can’t just stop measuring.

How We Implemented Parallel Measurement

Instead of replacing metrics, we ran parallel systems:

Official Metrics (Regulatory)

• DORA metrics with fixed definitions
• Incident counts and severity
• Change success rates

Internal Metrics (Engineering)

• Team health surveys
• Developer experience scores
• Business outcome correlation

Alignment Reviews (Quarterly)

• Where do official and internal metrics diverge?
• What explains the gap?
• Which should we trust for this decision?

The Trust-Building Timeline

In regulated environments, trust rebuilding takes longer:

Phase	Banking Timeline	Keisha’s Timeline
Audit current state	6 months	3 months
Parallel measurement	12 months	4 months
Gradual shift	18 months	6 months
Full implementation	24+ months	14 months

We’re now 30 months in and at about 65% trust (compared to your 78%). Slower, but sustainable in our regulatory context.

What Made the Difference

The single biggest factor: making internal metrics visible to compliance teams.

When auditors understood that we track developer satisfaction because it predicts incidents, they became advocates for the approach. They’d rather see leading indicators than just lagging outcomes.

Your Metric Council concept would work well here - I’d add a compliance representative to ensure new metrics can survive regulatory scrutiny.