Intelvision
Book a diagnostic
Book a diagnostic

Engineering Metrics Consulting

You are measuring engineering. You are not measuring what matters.

We design and implement the engineering metrics that give leadership a real view of delivery health — cycle time, lead time, deployment frequency, and change failure rate — replacing vanity metrics with evidence that drives decisions.

The situation

Engineering reports velocity. Leadership cannot tell if the project is on track.

Story points, sprint completion rates, and burn-down charts tell you about effort. They do not tell you about delivery — when the next release ships, how much debt is accumulating, or whether the team is accelerating or slowing down over time.

You are dealing with

Metrics dashboards that show lots of data but do not surface the information leadership needs to make decisions. Engineers gaming metrics inadvertently — story points inflated, sprint completion rates maintained by descoping — because the metric has become a goal rather than a signal.

What you need

A small set of metrics that tell the truth about delivery health — where the team is fast, where it is slow, and whether it is improving or degrading over time. Visible to leadership without requiring translation from engineering.

What you cannot afford

Continuing to make investment decisions about engineering based on metrics that measure activity rather than outcomes. A metrics system that engineering manages in its own interest rather than in the interest of business visibility.

What this engagement delivers

An engineering metrics framework grounded in DORA research and configured for your specific delivery system — implemented in your existing tooling, visible to the right people, and producing signal rather than noise.

Approach

Three phases. Audit first, design second, implementation only when the framework is right.

We do not install metrics before understanding what the current measurement is producing. The audit identifies what is missing. The design specifies the right framework. Implementation puts it in your existing tooling with dashboards the right people actually use.

Step 1
Step 2
Step 3

Phase 01

Metrics audit

Review of what is currently measured — what the metrics are, who uses them, and whether they are influencing decisions or just filling dashboards. We identify the information gaps that are causing leadership to rely on judgment or escalation rather than data.

2–3 days · Fixed scope

Phase 02

Metrics design

A framework of four to eight metrics covering delivery velocity (cycle time, lead time), reliability (deployment frequency, change failure rate, MTTR), and business alignment (feature vs debt ratio, backlog health). Each metric defined with its source, calculation, and the decision it informs.

1–2 weeks · Collaborative

Phase 03

Implementation

Metrics configured in your existing tooling — Jira, GitHub, or equivalent. Dashboards built for engineering leadership and business leadership separately — the same data, presented at the right level of detail for each audience. Baseline established so improvement is measurable.

1–2 weeks · Technical implementation

What you receive

Concrete outputs, not a slide deck.

  1. 01 / 04

    Metrics audit report

    A written assessment of current measurement — what the metrics are showing, what they are hiding, and the specific information gaps causing leadership to make decisions without data.

  2. 02 / 04

    Metrics framework

    A defined set of metrics — each with source, calculation, target range, and the decision it informs. Documented well enough to be maintained without ongoing external support.

  3. 03 / 04

    Dashboards

    Configured dashboards for engineering leadership (operational detail) and business leadership (delivery health summary) — built in your existing tooling. Baseline data captured so trend is measurable from day one.

  4. 04 / 04

    Delivery review cadence

    A structured monthly delivery review — agenda, participants, and the data pack — so the metrics produce regular decisions rather than sitting in a dashboard no one looks at.

What about DORA metrics?

DORA (Deployment Frequency, Lead Time for Changes, Change Failure Rate, Time to Restore) is the most research-backed framework for engineering performance measurement. We implement DORA metrics where the delivery system supports them — and are honest about the data quality and process maturity needed to make them meaningful rather than misleading.

Case studies

Case 1

Identified up to $220k/month in delivery-related revenue leakage

A software client had active development, ongoing releases, and a long-term product relationship, but delivery problems were starting to threaten the commercial value of the account.

Critical bugs, piecemeal releases, and work not tied to business objectives were creating a growing cost of poor quality. We built a business case around what poor delivery was costing and what fixing it could unlock.

Impact:

  • Estimated delivery-related revenue leakage: $50k-$220k/month
  • Broader potential monthly impact modeled around $315k
Read the full case study
Case 2

Turned a small efficiency request into a $4.19M self-funding roadmap

A modest time-saving ask uncovered a far larger structural constraint sitting underneath the day-to-day work.

We mapped the real opportunity and built a phased roadmap that pays for itself as it is delivered, rather than a single large bet.

Impact:

  • Modeled value of the phased roadmap: $4.19M
  • Each phase funds the next
Read the full case study
Case 3

Turned a routine review into a €1.05M resilience business case

A German funding intermediary with a well-built stack and a lean 24-person team had one systemic constraint: no tested recovery layer across eight interconnected systems — one WordPress database as the single source of truth for all of them.

A modest environment review uncovered €508K–€1.05M in modelled annual risk, unpriced and invisible until the numbers landed on paper. Reliability had quietly become the product.

Impact:

  • Modelled expected annual loss, quantified and phased out: €508K–€1.05M
  • Designed recovery time, from no tested restore: < 4 hrs
Read the full case study

Common questions

What executives ask before engaging.

What are the key engineering metrics to track?+

The most useful engineering metrics are DORA's four: Deployment Frequency (how often you ship), Lead Time (idea to production), Change Failure Rate (how often a release causes a problem), and Mean Time to Recover (how fast you fix it). These four metrics predict delivery performance and organisational health better than any individual measure.

What are DORA metrics and why do they matter?+

DORA metrics — Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recover — were developed by the DevOps Research and Assessment programme. Research across thousands of organisations confirms that high DORA performers ship more reliably, recover faster, and have lower change failure rates. They predict organisational health, not just technical output.

How do you measure engineering team productivity?+

Productivity is best measured at the system level, not the individual level. Useful system-level measures include lead time from commit to production, deployment frequency, incident rate, and the ratio of completed work to work delivered with measurable business impact. Individual lines-of-code or ticket-count metrics damage quality and collaboration without improving throughput.

What is delivery visibility in software development?+

Delivery visibility is the ability of engineering leadership and stakeholders to see — in real time — what is in flight, what is blocked, what the risk profile of the next release is, and whether the team is on track. Poor delivery visibility is why projects appear fine until they are not.

We have tried metrics before and nobody looked at the dashboards. How is this different?+

Metrics dashboards fail when they are not connected to a decision cadence. The delivery review cadence we establish creates a recurring reason to look at the data — and a structured format for turning the data into decisions. Without the cadence, any dashboard becomes shelfware.

Should engineering leadership own the metrics or should it be a shared responsibility?+

Engineering leadership owns the operational metrics. Business leadership reads the delivery health summary. The separation matters — engineering needs detail to manage the system, business needs summary to make investment decisions. Conflating the two produces either data overload for business leaders or insufficient detail for engineers.

Fixed-scope first step

Metrics Audit

Two to three business days. A written assessment of what you are currently measuring — what the metrics show, what they hide, and what framework will produce the delivery visibility leadership needs.

The audit covers

  • Current metrics inventory — what is measured, who uses it, what decisions it informs
  • Information gap analysis — what leadership cannot see about engineering delivery
  • DORA readiness — whether the delivery system supports meaningful DORA measurement
  • Tooling assessment — what data is available from existing tools
  • Written metrics framework recommendation with implementation approach

2–3 business days · Remote · Fixed fee

Also consider

Engineering Diagnostic→
A structured assessment of your engineering function — delivery, process, team, and architecture.
Engineering Operating Model→
A delivery system designed for predictability — planning cadence, team structure, and process that scales.
Software Delivery→
End-to-end delivery process redesign for teams that have stopped being predictable.

Rescue the project before delivery problems become revenue problems