Demonstrate Engineering Progress

The quarterly review is due and the only data available is ticket counts -- which single out individuals rather than illuminate the system. This guide walks you through preparing a quarterly presentation with Landmark: system-level trends, marker evidence, and engineer voice that show direction without naming individuals.

Prerequisites

Getting Started: Map for Leadership -- install Map, migrate the activity schema, load your roster, and sync GetDX data.
Getting Started: Landmark for Leadership -- install Landmark and confirm you can run npx fit-landmark org show.
Authoring Agent-Aligned Engineering Standards -- define drivers and markers in your standard data. Landmark's health, evidence, and readiness views require drivers in drivers.yaml and markers in your capability YAML files.

The rest of this guide assumes Map's activity layer is running and populated. If you want to explore with synthetic data first, see Trying the activity layer with synthetic data in the Map guide.

Confirm your data is ready

Before building views for a quarterly review, confirm that the standard data, roster, and snapshots are in place.

Validate your standard data against the schema:

npx fit-map validate

Validating standard data...
  disciplines/software_engineering.yaml  ✓
  capabilities/task_completion.yaml      ✓
  behaviours/ownership.yaml             ✓
  drivers.yaml                          ✓

Validation passed. 0 errors, 0 warnings.

If any errors appear, resolve them using the guidance in Authoring Agent-Aligned Engineering Standards.

Confirm that your roster is loaded and the team hierarchy is visible:

npx fit-landmark org team --manager alice@example.com

  Team: Alice Smith (alice@example.com)

    Bob Chen         bob@example.com        Software Engineering  J060
    Carol Davis      carol@example.com      Software Engineering  J070
    Dan Park         dan@example.com        Data Engineering      J060

If the output is empty, re-run npx fit-map people push roster.csv with your current roster file.

Confirm that snapshot data is available:

npx fit-landmark snapshot list

  Snapshots

    MjUyNbaY   2025-03-15   Q1 2025
    NzE4MmRk   2025-06-14   Q2 2025

If the output is empty, run npx fit-map getdx sync followed by npx fit-map activity transform to ingest the latest GetDX data.

See system-level trends across snapshots

Quarterly reviews need context: is a score improving, declining, or flat? Before diving into the health view, check how a specific driver has moved over time.

Track a driver's trend across snapshots, scoped to your team:

npx fit-landmark snapshot trend --item quality --manager alice@example.com

  Trend: quality (Alice Smith's team)

    2025-03-15   72
    2025-06-14   78
    2025-09-13   81

The output shows the driver's score at each snapshot date, making the direction visible. Replace quality with any driver ID from your drivers.yaml -- the starter data includes quality, reliability, and cognitive_load.

Compare the latest snapshot against organizational benchmarks:

npx fit-landmark snapshot compare --snapshot MjUyNbaY --manager alice@example.com  # ID from 'snapshot list'

  Snapshot comparison: MjUyNbaY (Alice Smith's team vs organization)

    Driver          Team   p50   p75   p90
    quality           78    70    80    88
    reliability       65    68    76    84
    cognitive_load    82    72    81    90

Use the snapshot ID from npx fit-landmark snapshot list.

Build the health view

The health view is the centerpiece of Landmark's quarterly presentation. It joins driver scores, contributing-skill evidence, engineer voice comments, and growth recommendations into a single picture scoped to a manager's team.

Run the health view for your team:

npx fit-landmark health --manager alice@example.com

  Health: Alice Smith's team

    quality (78, 72nd percentile, vs_org: +5)
      Contributing skills: task_completion (12 artifacts), planning (8 artifacts)
      "We've been catching more issues in review lately" — latest snapshot
      "Design docs are getting better but still inconsistent" — latest snapshot
      Recommendation: Carol Davis could develop planning (currently working)
      Initiative: Code Review Standards (65% complete)

    reliability (65, 48th percentile, vs_org: -3)
      Contributing skills: incident_response (4 artifacts), sre_practices (2 artifacts)
      "On-call handoffs are still rough" — latest snapshot

The output is organized by driver. For each driver you will see:

Score and percentile -- the team's GetDX score with its position relative to the organization (e.g. "72nd percentile, vs_org: +5").
Contributing skills -- the skills from your standard that map to this driver, listed by ID.
Evidence counts -- how many marker-matched artifacts exist for each contributing skill.
GetDX comments -- up to two engineer comments related to the driver's contributing skills, surfaced from the latest snapshot.
Growth recommendations -- if Summit is installed, specific individuals who could develop a contributing skill, with their current level noted.
Active initiatives -- any organizational initiatives linked to this driver, with completion percentage.

Understanding what the health view shows

The health view is designed for conversations about the system, not about individuals. Driver scores are team-level aggregates from GetDX. Evidence counts show how many artifacts across the team match a skill's markers -- not which individual produced them. Comments are surfaced by keyword relevance to the driver, not attributed to specific respondents.

When presenting health data in a quarterly review, the narrative is: "Here is where the system is strong, here is where it is trending, and here is what engineers are saying about it." The data supports that narrative without requiring anyone to name names.

Hear what engineers are saying

GetDX snapshot comments contain direct engineer feedback. Landmark surfaces these comments in two modes, both useful for quarterly preparation.

See comments themed by topic across your team:

npx fit-landmark voice --manager alice@example.com

  Voice: Alice Smith's team (latest snapshot)

    incident    3 comments
      "On-call handoffs are still rough"
      "Runbook coverage is improving but gaps remain"
      "Incident review meetings have been helpful"

    planning    2 comments
      "Sprint planning feels more realistic this quarter"
      "Design docs are getting better but still inconsistent"

    testing     1 comment
      "Integration tests saved us twice this month"

    Below-50th driver alignment:
      reliability (48th percentile) — 3 incident comments

The manager view buckets comments by theme and counts how many mention each. It also highlights drivers scoring below the 50th percentile where engineer comments align -- showing where sentiment matches the quantitative data.

This is valuable for quarterly reviews because it grounds numerical scores in the team's own words. A low reliability score paired with three comments about incident response tells a clearer story than the score alone.

Check where evidence supports the standard

Evidence coverage shows whether the team's actual work produces artifacts that match your standard's markers. Two views help here: practice patterns across the team and the gap between derived and evidenced capability.

See practice patterns for your team:

npx fit-landmark practice --manager alice@example.com

  Practice patterns: Alice Smith's team

    task_completion     12 artifacts   strong
    planning             8 artifacts   moderate
    incident_response    4 artifacts   developing
    sre_practices        2 artifacts   minimal

Practice patterns show which skills have strong marker-matched evidence and which have little or none. Filter to a specific skill for detail:

npx fit-landmark practice --skill task_completion --manager alice@example.com

Compare what the standard predicts the team should be capable of against what evidence actually shows:

npx fit-landmark practiced --manager alice@example.com

  Practiced vs derived: Alice Smith's team

    Bob Chen
      task_completion    practitioner   evidenced
      planning           working        evidenced
      sre_practices      working        on paper only

    Carol Davis
      task_completion    practitioner   evidenced
      architecture       practitioner   on paper only

Skills flagged "on paper only" have derived capability (the team member's role implies the skill) but no marker evidence. This can mean the evidence pipeline has a gap, or it can highlight a coaching opportunity. Either way, it is information worth surfacing in a quarterly review -- it shows where the organization's definitions and actual practice diverge.

Connect initiatives to outcomes

If your organization tracks initiatives in GetDX, Landmark can show whether completed initiatives moved the needle on their target drivers.

List active initiatives:

npx fit-landmark initiative list --manager alice@example.com

  Initiatives: Alice Smith's team

    Code Review Standards    quality        65% complete
    Runbook Coverage         reliability    30% complete

See whether completed initiatives correlated with driver score changes:

npx fit-landmark initiative impact --manager alice@example.com

  Initiative impact: Alice Smith's team

    Code Review Standards (quality)
      Before: 72 (2025-03-15)
      After:  78 (2025-06-14)
      Delta:  +6

    Runbook Coverage (reliability)
      Before: 65 (2025-06-14)
      After:  — (in progress)

Initiative impact shows before-and-after scores for each initiative's target driver. This is particularly useful for quarterly reviews because it answers the question "did the investment work?" with data rather than narrative.

Verify

You have demonstrated engineering progress without surveillance when:

Health view renders with data. npx fit-landmark health --manager alice@example.com shows at least one driver with a score, contributing skills, and evidence counts. No "No GetDX snapshot data available" messages.
Trends show direction. npx fit-landmark snapshot trend --item quality --manager alice@example.com shows scores across multiple snapshots, making the trajectory visible.
Engineer voice is surfaced. npx fit-landmark voice --manager alice@example.com shows themed comments with counts. Comments align to drivers without attributing them to specific individuals.
Evidence backs the story. npx fit-landmark practiced --manager alice@example.com shows where the team's actual work matches the standard and where it does not -- system-level insight, not individual performance data.

All commands accept --format text, --format json, or --format markdown. Use --format markdown to produce output suitable for sharing in documents and presentations.

What's next

Once you can demonstrate overall progress, a natural follow-up is checking whether specific culture investments -- mentorship programs, tooling changes, process improvements -- are producing measurable results before the next budget cycle. See Tell Whether Culture Investments Are Working for that workflow.

For the full command reference and audience model, see the Landmark product page.