# 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](/docs/getting-started/leadership/map/) -- install Map, migrate the activity schema, load your roster, and sync GetDX data. - [Getting Started: Landmark for Leadership](/docs/getting-started/leadership/landmark/) -- install Landmark and confirm you can run `npx fit-landmark org show`. - [Authoring Agent-Aligned Engineering Standards](/docs/products/authoring-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](/docs/getting-started/leadership/map/#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: ```sh npx fit-map validate ``` ```text 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](/docs/products/authoring-standards/). Confirm that your roster is loaded and the team hierarchy is visible: ```sh npx fit-landmark org team --manager alice@example.com ``` ```text 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: ```sh npx fit-landmark snapshot list ``` ```text 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: ```sh npx fit-landmark snapshot trend --item quality --manager alice@example.com ``` ```text 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: ```sh npx fit-landmark snapshot compare --snapshot MjUyNbaY --manager alice@example.com # ID from 'snapshot list' ``` ```text 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: ```sh npx fit-landmark health --manager alice@example.com ``` ```text 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: ```sh npx fit-landmark voice --manager alice@example.com ``` ```text 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: ```sh npx fit-landmark practice --manager alice@example.com ``` ```text 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: ```sh 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: ```sh npx fit-landmark practiced --manager alice@example.com ``` ```text 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: ```sh npx fit-landmark initiative list --manager alice@example.com ``` ```text 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: ```sh npx fit-landmark initiative impact --manager alice@example.com ``` ```text 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: 1. **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. 2. **Trends show direction.** `npx fit-landmark snapshot trend --item quality --manager alice@example.com` shows scores across multiple snapshots, making the trajectory visible. 3. **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. 4. **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](/docs/products/engineering-outcomes/culture-investments/) for that workflow. For the full command reference and audience model, see the [Landmark product page](/landmark/).