# VCL Metric Calculations — Database Reference

## Overview

This document describes how every percentage, total, and forecast number on the VCL Report and CCP Metrics pages is computed from the underlying database. It is the single reference for verifying that what you see on the page matches what is in the data.

Each section answers four questions:

- **What it shows** — the field name on screen and the data path
- **What feeds it** — the table(s) and columns the value is computed from
- **How it is calculated** — the exact SQL or formula, plus any rounding rules
- **Why it can drift** — known sources of inaccuracy and how the dashboard guards against them

---

## Table of Contents

- [Data Sources](#data-sources)
  - [compliance_items](#compliance_items)
  - [compliance_uploads](#compliance_uploads)
  - [compliance_snapshots](#compliance_snapshots)
  - [vcl_multi_vertical_summary](#vcl_multi_vertical_summary)
- [VCL Report Page (Single-Vertical / Legacy AEO)](#vcl-report-page-single-vertical--legacy-aeo)
  - [Stats Bar](#stats-bar)
  - [Donut Chart — Status of Non-Compliant Assets](#donut-chart--status-of-non-compliant-assets)
  - [Heavy Hitters Table](#heavy-hitters-table)
  - [Vertical Breakdown Table](#vertical-breakdown-table)
  - [Compliance Trend Chart](#compliance-trend-chart)
- [CCP Metrics Page (Multi-Vertical)](#ccp-metrics-page-multi-vertical)
  - [Aggregated Stats Bar](#aggregated-stats-bar)
  - [Donut — Blocked vs In-Progress](#donut--blocked-vs-in-progress)
  - [Trend Chart](#trend-chart)
  - [Aggregated Burndown Forecast](#aggregated-burndown-forecast)
  - [Metric Table (Cross-Vertical)](#metric-table-cross-vertical)
  - [Metric Detail View — Per-Vertical Breakdown](#metric-detail-view--per-vertical-breakdown)
  - [Per-Metric Forecast Burndown Chart](#per-metric-forecast-burndown-chart)
  - [Per-Vertical Detail and Burndown](#per-vertical-detail-and-burndown)
- [Forecast Algorithms](#forecast-algorithms)
  - [Linear Regression Forecast (Trend)](#linear-regression-forecast-trend)
  - [Resolution-Date Burndown Forecast](#resolution-date-burndown-forecast)
  - [Per-Metric Forecast (Historical + Projected)](#per-metric-forecast-historical--projected)
- [Cross-Cutting Correctness Rules](#cross-cutting-correctness-rules)
- [Verifying Values by Hand](#verifying-values-by-hand)
- [Worked Example — Vulns_Aging vs 7.1.1 Forecast Charts](#worked-example--vulns_aging-vs-711-forecast-charts)

---

## Data Sources

The VCL pages read from four tables. Knowing what each one stores is the prerequisite for understanding the calculations.

### compliance_items

One row per `(hostname, metric_id, vertical)` combination per upload. **Only non-compliant findings are stored here.** Compliant devices never appear in this table — they are inferred from the Summary sheet's totals minus what is in `compliance_items`.

| Column | Type | Notes |
|---|---|---|
| `hostname` | TEXT | Device hostname |
| `metric_id` | TEXT | Compliance metric identifier (e.g., `2.3.5`, `7.1.1`) |
| `team` | TEXT | Sub-team responsible (`STEAM`, `ACCESS-ENG`, etc.) |
| `vertical` | TEXT | Vertical code (`NTS_AEO`, `SDIT_CISO`, `TSI`); `NULL` for legacy AEO uploads |
| `status` | TEXT | `'active'` if currently failing, `'resolved'` once the device drops off the next upload |
| `resolution_date` | DATE | Target remediation date (manual entry) |
| `seen_count` | INTEGER | Number of consecutive uploads this finding has appeared on |
| `first_seen_upload_id` / `upload_id` / `resolved_upload_id` | INTEGER | Upload references for first appearance, latest, and resolution |

> A device is "compliant" when **no** active row exists for it in this table.

### compliance_uploads

One row per uploaded xlsx. A multi-vertical upload day produces multiple rows that share the same `report_date`.

| Column | Type | Notes |
|---|---|---|
| `report_date` | TEXT | The reporting period the file covers (`YYYY-MM-DD`) |
| `vertical` | TEXT | Same vertical code as `compliance_items.vertical`; `NULL` for legacy AEO |
| `new_count` / `recurring_count` / `resolved_count` | INTEGER | Per-upload deltas (vertical-scoped) |
| `summary_json` | TEXT | The raw parsed Summary sheet — used as a fallback by `/summary` |

### compliance_snapshots

Monthly aggregated snapshot keyed by `(snapshot_month, vertical)`. The trend chart reads exclusively from here. Snapshots are written automatically inside the upload commit transaction.

| Column | Type | Notes |
|---|---|---|
| `snapshot_month` | TEXT | `YYYY-MM` |
| `vertical` | TEXT | Vertical code or team name (legacy) |
| `total_devices` / `compliant` / `non_compliant` | INTEGER | Counts at month end |
| `compliance_pct` | NUMERIC(5,2) | Pre-computed for that month |

> The `(snapshot_month, vertical)` pair is `UNIQUE`. Re-uploading inside the same calendar month overwrites the row via `ON CONFLICT DO UPDATE`.

### vcl_multi_vertical_summary

One row per `(metric_id, team)` pair per upload, populated from the Summary sheet of a multi-vertical xlsx. This is the **source of truth for compliant counts** — `compliance_items` only has non-compliant rows.

| Column | Type | Notes |
|---|---|---|
| `upload_id` | INTEGER | FK → `compliance_uploads` |
| `vertical` | TEXT | Vertical code |
| `metric_id` | TEXT | Metric identifier |
| `team` | TEXT | Either an `ALL: <vertical>` rollup row or a sub-team row (`STEAM`, `ACCESS-ENG`) |
| `non_compliant` / `compliant` / `total` | INTEGER | From the Summary sheet |
| `compliance_pct` | NUMERIC(5,2) | From the Summary sheet (decimal — `0.95` = 95%) |
| `target` | NUMERIC(5,2) | Per-metric target from the spreadsheet |

> **Critical aggregation rule:** rows where `team LIKE 'ALL:%'` are vertical-level rollups that already include their sub-teams. **Aggregating both rollup and sub-team rows would double-count.** Every cross-vertical query in this codebase filters with `WHERE team LIKE 'ALL:%'`.

---

## VCL Report Page (Single-Vertical / Legacy AEO)

This is the original VCL Report at `/api/compliance/vcl/...`. It aggregates across whatever data exists in `compliance_items` regardless of vertical, and is primarily used for the AEO single-team view.

Source: `backend/routes/compliance.js` (`router.get('/vcl/stats', ...)` and `router.get('/vcl/trend', ...)`)

### Stats Bar

**What it shows:** Total Devices, In-Scope, Compliant, Non-Compliant, Remediations Required, Current %, Target %.

**What feeds it:** `compliance_items` — every distinct hostname.

**How it is calculated:**

```sql
SELECT
    COUNT(DISTINCT hostname) AS total_devices,
    COUNT(DISTINCT hostname) AS in_scope,
    COUNT(DISTINCT CASE
        WHEN hostname NOT IN (SELECT DISTINCT hostname FROM compliance_items WHERE status = 'active')
        THEN hostname END) AS compliant,
    COUNT(DISTINCT CASE
        WHEN hostname IN (SELECT DISTINCT hostname FROM compliance_items WHERE status = 'active')
        THEN hostname END) AS non_compliant
FROM compliance_items;
```

Then in JavaScript:

```javascript
compliance_pct = in_scope > 0 ? Math.round((compliant / in_scope) * 100) : 0
remediations_required = non_compliant
target_pct = process.env.VCL_TARGET_PCT || 95
```

**Field-by-field:**

| Field | Definition |
|---|---|
| Total Devices | Count of unique hostnames that have ever appeared in any upload |
| In-Scope | Same as Total Devices — every tracked device is in-scope by definition |
| Compliant | Hostnames with **zero** rows where `status = 'active'` |
| Non-Compliant | Hostnames with **at least one** row where `status = 'active'` |
| Remediations Required | Equals Non-Compliant — every non-compliant device needs at least one fix |
| Current % | `ROUND((Compliant / In-Scope) * 100)` — whole-number percent |
| Target % | `VCL_TARGET_PCT` env var on the backend, default 95 |

**Why it can drift:**

- If a hostname has both an `active` row in one vertical and a `resolved` row in another, the `IN`/`NOT IN` subqueries above already classify correctly — `active` wins because the `IN` subquery includes any active row.
- Compliant devices are inferred. If `compliance_items` is missing rows that the Summary sheet reported (e.g., truncated upload), the count silently undercounts.

### Donut Chart — Status of Non-Compliant Assets

**What it shows:** Two slices — Blocked (red) and In-Progress (amber) — with counts and percentages.

**What feeds it:** `compliance_items` rows where `status = 'active'`, deduplicated to one row per hostname using `MAX(resolution_date)`.

**How it is calculated:**

```sql
SELECT hostname, MAX(resolution_date) AS resolution_date
FROM compliance_items
WHERE status = 'active'
GROUP BY hostname;
```

Then `categorizeNonCompliant()` partitions:

```javascript
blocked    = items.filter(i => i.resolution_date == null)
in_progress = items.filter(i => i.resolution_date != null)
blocked.pct     = Math.round((blocked.count    / total) * 100)
in_progress.pct = Math.round((in_progress.count / total) * 100)
```

> A device with **any** resolution date set on **any** of its active findings is considered In-Progress. Only when every active finding lacks a date is the device counted as Blocked.

**Why it can drift:**

- The `MAX(resolution_date)` clause means a device with one dated finding and one undated finding is classified as In-Progress, not Blocked. This is intentional — once one team commits to a date, the device is no longer fully blocked.
- Rounding to whole numbers means `blocked.pct + in_progress.pct` may total 99 or 101 in edge cases. The chart still displays the correct underlying counts.

### Heavy Hitters Table

**What it shows:** One row per team, sorted by non-compliant device count descending. Columns: Vertical/Team, Non-Compliant, Compliance Date, Notes.

**What feeds it:** `compliance_items` deduplicated to one team per hostname, plus `vcl_vertical_metadata` for manual fields (Notes, Compliance Date, RAs).

**How it is calculated:**

```sql
WITH device_team AS (
    SELECT DISTINCT ON (hostname)
           hostname,
           COALESCE(team, 'Unknown') AS team,
           resolution_date
    FROM compliance_items
    WHERE status = 'active'
    ORDER BY hostname, seen_count DESC, upload_id DESC
)
SELECT team,
       COUNT(DISTINCT hostname)::int AS non_compliant,
       MAX(resolution_date) AS compliance_date
FROM device_team
GROUP BY team
ORDER BY COUNT(DISTINCT hostname) DESC;
```

The CTE picks one representative row per hostname using the `(seen_count DESC, upload_id DESC)` rule — the longest-running, most recently seen team assignment wins. This guarantees `SUM(heavy_hitters[*].non_compliant) == stats.non_compliant`.

**Why it can drift:**

- Before the fix tracked under spec `compliance-duplicate-failing-metrics`, a hostname that appeared with different `team` values across verticals was double-counted. The CTE above is the fix — confirmed by Property 3 of that spec.
- `compliance_date` here is the latest resolution date across the team's devices, used as a default. The team's manually entered Compliance Date in `vcl_vertical_metadata` overrides it when present.

### Vertical Breakdown Table

**What it shows:** Same teams as Heavy Hitters, plus per-team Compliance %, Forecast Burndown columns, Blockers count, RAs, Notes.

**What feeds it:**
- Per-team total devices: same `device_team` CTE as Heavy Hitters but without the `status = 'active'` filter.
- Forecast: `compliance_items` with non-null `resolution_date`.
- Manual fields: `vcl_vertical_metadata`.

**How it is calculated (per team):**

```sql
-- Total devices for the team
WITH device_team AS (
    SELECT DISTINCT ON (hostname)
           hostname,
           COALESCE(team, 'Unknown') AS team
    FROM compliance_items
    ORDER BY hostname, seen_count DESC, upload_id DESC
)
SELECT COUNT(*)::int AS total FROM device_team WHERE team = $1;

-- Forecast resolution dates for the team
SELECT DISTINCT ON (hostname, metric_id) resolution_date
FROM compliance_items
WHERE status = 'active'
  AND COALESCE(team, 'Unknown') = $1
  AND resolution_date IS NOT NULL
ORDER BY hostname, metric_id, seen_count DESC, upload_id DESC;
```

In JavaScript:

```javascript
team_compliant     = team_total - team_non_compliant
compliance_pct     = team_total > 0 ? Math.round((team_compliant / team_total) * 100) : 0
forecast_burndown  = computeForecastBurndown(forecastItems)   // YYYY-MM → count
blockers           = Math.max(team_non_compliant - forecastItems.length, 0)
```

`computeForecastBurndown` buckets each device's resolution date into a `YYYY-MM` key. The result is `{ "2026-06": 12, "2026-07": 8, ... }` — the count of devices expected to resolve each month.

**Why it can drift:**

- The `DISTINCT ON (hostname, metric_id)` in the forecast query was added by the duplicate-failing-metrics fix. Without it, a device failing the same metric in two verticals would have its resolution date counted twice and `blockers` would go negative (the `Math.max` clamp protects the UI but masks the inconsistency).
- The team total uses **all rows** in `compliance_items` (active and resolved), so a team's `total` here is "every device that has ever been part of this team," not just current devices.

### Compliance Trend Chart

**What it shows:** Bar chart of compliant device count per month, plus a solid line (actual %) and a dashed line (forecasted %) on a secondary axis. A horizontal reference line marks the target.

**What feeds it:** `compliance_snapshots`, aggregated across all verticals.

**How it is calculated:**

```sql
SELECT snapshot_month,
       SUM(compliant)::int AS compliant_count,
       CASE WHEN SUM(total_devices) > 0
            THEN ROUND((SUM(compliant)::numeric / SUM(total_devices)::numeric) * 100, 1)
            ELSE 0 END AS compliance_pct
FROM compliance_snapshots
GROUP BY snapshot_month
ORDER BY snapshot_month ASC;
```

The forecast logic is described in [Linear Regression Forecast](#linear-regression-forecast-trend). Snapshots are persisted in `persistUpload()` using the upload's `report_date` month so historical uploads land in the correct bucket. See [Cross-Cutting Correctness Rules](#cross-cutting-correctness-rules) for the snapshot upsert behavior.

**Why it can drift:**

- Snapshots are keyed `(snapshot_month, vertical)`, so re-uploading the same month overwrites — only the latest upload's totals are preserved per month.
- Pre-fix snapshots from before the duplicate-failing-metrics correction may have `compliant + non_compliant > total_devices` if a hostname had both active and resolved rows across verticals. The fix uses `MIN(status)` inside a CTE so each hostname is classified once. Older snapshots written before the fix should be regenerated by re-running the affected uploads.

---

## CCP Metrics Page (Multi-Vertical)

The CCP Metrics page is the executive cross-vertical view. It uses the multi-vertical Summary sheet data as the source of truth for totals (since `compliance_items` only contains non-compliant devices).

Source: `backend/routes/vclMultiVertical.js`. Mounted at `/api/compliance/vcl-multi/...`.

### Aggregated Stats Bar

**What it shows:** Total Devices, Compliant, Non-Compliant, Current %, Target % across **every** vertical's latest upload.

**What feeds it:** `vcl_multi_vertical_summary` filtered to ALL: rollup rows of the latest upload per vertical.

**How it is calculated:**

```sql
-- 1. Find the latest upload ID per vertical
SELECT DISTINCT ON (vertical) id, vertical
FROM compliance_uploads
WHERE vertical IS NOT NULL
ORDER BY vertical, id DESC;

-- 2. Sum totals from rollup rows only (avoids double-counting sub-teams)
SELECT vertical,
       SUM(total)::int AS total_devices,
       SUM(compliant)::int AS compliant,
       SUM(non_compliant)::int AS non_compliant
FROM vcl_multi_vertical_summary
WHERE upload_id = ANY($1) AND team LIKE 'ALL:%'
GROUP BY vertical;
```

In JavaScript:

```javascript
agg_total         = SUM(vertical.total_devices for each vertical)
agg_compliant     = SUM(vertical.compliant     for each vertical)
agg_non_compliant = SUM(vertical.non_compliant for each vertical)
compliance_pct    = agg_total > 0 ? Math.round((agg_compliant / agg_total) * 100) : 0
```

> **The `team LIKE 'ALL:%'` filter is the most important rule on this page.** Each Summary sheet contains one rollup row per metric (`ALL: NTS-AEO`) plus one row per sub-team (`STEAM`, `ACCESS-ENG`). Summing both rollup and sub-team rows would double the totals. Every cross-vertical query enforces this filter.

**Why it can drift:**

- If a Summary sheet ever omits the `ALL:` rollup row for a metric, that metric's totals will be missing from the aggregate. The Python parser does not fabricate rollup rows, so this is a function of what the upstream xlsx contains.
- Verticals with no rows in `vcl_multi_vertical_summary` (e.g., the legacy AEO data) do not contribute. Their data is visible only on the original VCL Report, not the CCP Metrics page.

### Donut — Blocked vs In-Progress

**What it shows:** Same as the legacy donut, scoped to multi-vertical data.

**What feeds it:** `compliance_items` where `vertical IS NOT NULL`, deduplicated by hostname.

**How it is calculated:** Identical formula to the [legacy donut](#donut-chart--status-of-non-compliant-assets), but the filter is `WHERE vertical IS NOT NULL AND status = 'active'`.

```sql
SELECT hostname, MAX(resolution_date) AS resolution_date
FROM compliance_items
WHERE vertical IS NOT NULL AND status = 'active'
GROUP BY hostname;
```

### Trend Chart

**What it shows:** Cross-vertical monthly trend of compliant device count and compliance percentage with a 3-month forecast.

**What feeds it:** `compliance_snapshots` where `vertical IS NOT NULL AND vertical != ''`.

**How it is calculated:**

```sql
SELECT snapshot_month,
       SUM(total_devices)::int AS total_devices,
       SUM(compliant)::int     AS compliant,
       SUM(non_compliant)::int AS non_compliant
FROM compliance_snapshots
WHERE vertical IS NOT NULL AND vertical != ''
GROUP BY snapshot_month
ORDER BY snapshot_month ASC;
```

Each row's `compliance_pct` is `ROUND((compliant / total_devices) * 100, 1)` — one decimal place. The forecast then uses the [Linear Regression Forecast](#linear-regression-forecast-trend) logic.

### Aggregated Burndown Forecast

**What it shows:** A bar chart of expected device remediations per month across all verticals, plus stat cards for In-Progress, Blockers, and Projected Clear date.

**What feeds it:** `compliance_items` where `vertical IS NOT NULL AND status = 'active'`.

**How it is calculated:**

```sql
SELECT hostname, resolution_date, vertical
FROM compliance_items
WHERE vertical IS NOT NULL AND status = 'active';
```

Then the rows pass through two pure helpers:

1. `deduplicateByHostname(rows)` — collapses each hostname to one entry, keeping the **earliest non-null** `resolution_date`. A device that fails three metrics with different planned dates is bucketed by its earliest commitment.

2. `computeAggregatedBurndown(devices)` — computes:

   ```javascript
   total       = devices.length
   blockers    = devices.filter(d => d.resolution_date == null).length
   with_dates  = total - blockers
   monthly[m]  = count of devices whose resolution_date falls in month m   // YYYY-MM
   projection[m] = { remediated: monthly[m], remaining: running_remainder }
   projected_clear_date = (blockers === 0 && monthly is non-empty)
       ? last_month_in_monthly_keys
       : null
   ```

> Projected Clear is **only computed when `blockers === 0`**. Any device without a resolution date prevents the projection from showing — the dashboard is honest about the fact that an open-ended commitment cannot be projected.

**Why it can drift:**

- Devices with multiple `compliance_items` rows (one per failing metric) are deduplicated by hostname before bucketing. Without deduplication, a device with three failing metrics and one resolution date would count three times.
- A resolution date in the **past** still buckets into its actual month — `computeAggregatedBurndown` does not roll past-due dates forward. The per-metric chart does roll them forward; see [Per-Metric Forecast](#per-metric-forecast-historical--projected) for that distinction.

### Metric Table (Cross-Vertical)

**What it shows:** One row per metric, with non-compliant, compliant, total, compliance %, and target % aggregated across **every** vertical's latest upload. Sorted by non-compliant descending.

**What feeds it:** `vcl_multi_vertical_summary` rollup rows from the latest upload per vertical.

**How it is calculated:**

```sql
SELECT metric_id,
       MAX(metric_desc) AS metric_desc,
       MAX(category)    AS category,
       SUM(non_compliant)::int   AS non_compliant,
       SUM(compliant)::int       AS compliant,
       SUM(total)::int           AS total,
       ROUND(AVG(target::numeric), 4) AS target
FROM vcl_multi_vertical_summary
WHERE upload_id = ANY($1) AND team LIKE 'ALL:%'
GROUP BY metric_id
ORDER BY non_compliant DESC;
```

Then for each row:

```javascript
compliance_pct = total > 0 ? compliant / total : 0   // stored as decimal
```

The frontend renders the percentage with one decimal: `(compliance_pct * 100).toFixed(1) + '%'`.

> **`target` is the arithmetic mean across verticals**, not the worst or best. If two verticals report a target of 0.90 and 0.95 for the same metric, the cross-vertical target is 0.925. This is a deliberate choice — the page shows a fleet-wide composite target, not the strictest individual one.

**Why it can drift:**

- `MAX(metric_desc)` and `MAX(category)` rely on every Summary sheet using the same description for the same `metric_id`. If two verticals describe the same metric differently, the alphabetically-last description wins.

### Metric Detail View — Per-Vertical Breakdown

**What it shows:** For a selected metric, one row per vertical with that metric's numbers, plus a `sub_teams` array per vertical.

**What feeds it:** `vcl_multi_vertical_summary` for the selected metric, latest upload per vertical, both rollup and sub-team rows.

**How it is calculated:**

```sql
SELECT vertical, metric_desc, category, team,
       non_compliant, compliant, total, compliance_pct, target
FROM vcl_multi_vertical_summary
WHERE upload_id = ANY($1) AND metric_id = $2
ORDER BY vertical, team;
```

The handler then separates rollup rows (`team LIKE 'ALL:%'`) from sub-team rows in JavaScript:

- The rollup row for each vertical becomes the primary entry.
- Each sub-team row is attached to its vertical's `sub_teams` array.
- Rows where `team = '(Other)'` are skipped — they are catch-all rows already counted in the rollup.

`compliance_pct` is read directly from the table (already a decimal — `0.95` = 95%).

**Why it can drift:**

- A sub-team named `(Other)` is used by the spreadsheet for unassignable devices — it is intentionally excluded from the sub-team breakdown to avoid duplication.
- The vertical-level `compliance_pct` is what was in the Summary sheet at upload time. It is not recomputed from `compliant / total`. If those numbers ever disagree (Summary rounded differently), the table shows the spreadsheet's number.

### Per-Metric Forecast Burndown Chart

**What it shows:** A combined chart with up to 4 historical monthly snapshots (left of the divider) and up to 12 forecast months (right of the divider). Each data point shows total assets, non-compliant count, and compliance %.

**What feeds it:** Three sources combined:
1. `compliance_snapshots` for historical totals (3 months back).
2. `vcl_multi_vertical_summary` for the metric's `total` (used as `total_assets`).
3. `compliance_items` for current devices and their resolution dates.

**How it is calculated:**

```sql
-- Active devices for this metric across every vertical it spans
SELECT hostname, resolution_date, vertical
FROM compliance_items
WHERE metric_id = $1 AND status = 'active' AND vertical IS NOT NULL;

-- Historical snapshots for those verticals (3 months back)
SELECT snapshot_month AS month,
       SUM(total_devices)::int AS total_assets,
       SUM(non_compliant)::int AS non_compliant,
       ROUND((SUM(compliant)::numeric / NULLIF(SUM(total_devices), 0)) * 100, 1) AS compliance_pct
FROM compliance_snapshots
WHERE vertical = ANY($1) AND snapshot_month >= $2 AND snapshot_month < $3
GROUP BY snapshot_month
ORDER BY snapshot_month ASC;

-- The metric's per-metric total assets (from latest summary)
SELECT SUM(total)::int AS total
FROM vcl_multi_vertical_summary
WHERE metric_id = $1 AND team LIKE 'ALL:%'
  AND upload_id IN (
    SELECT id FROM compliance_uploads
    WHERE vertical IS NOT NULL
    ORDER BY id DESC LIMIT 20
  );
```

#### Historical Computation — Ratio Method

The `compliance_snapshots` table stores **vertical-level** totals, not per-metric. To estimate this metric's historical non-compliant count for a past month, the handler uses a ratio:

```javascript
metric_nc_for_month = ROUND(
    snapshot.non_compliant_for_vertical * (current_metric_nc / current_vertical_total_nc)
)
```

In words: "the metric's share of the vertical's current non-compliant load is assumed constant — apply that ratio to historical snapshot non-compliant counts."

> The ratio method is an approximation. It assumes the metric's contribution to the vertical's non-compliance is steady over the last three months. If the metric load shifts dramatically month-to-month, the historical bars will not match the actual past.

The current month is **always** computed from live data (not the ratio):

```javascript
current_month_nc = number of distinct active hostnames for this metric
current_compliance_pct = ROUND((total_assets - current_month_nc) / total_assets * 1000) / 10   // 1 decimal
```

#### Forecast Computation — Resolution Date Bucketing

Forward projections are handled by `computeMetricForecastBurndown`. The algorithm:

1. Partition active devices into `blockers` (no resolution date) and `with_dates`.
2. Bucket each dated device by its `resolution_date` month (`YYYY-MM`).
3. **Past-due dates roll into the current month.** A device whose date is March when today is May counts as remediating in May, not March.
4. Walk forward up to 12 months, decrementing `remaining_non_compliant` by each month's bucket.
5. Stop early if `remaining_non_compliant <= blockers` — meaning every device with a date is projected to be done and only blockers remain.

The compliance percentage at each forecast point is:

```javascript
compliance_pct = ROUND((total_assets - remaining_non_compliant) / total_assets * 1000) / 10
```

#### Correctness Properties of the Forecast

These properties hold for any input (verified by property-based tests in `backend/__tests__/compliance-duplicate-chart-entries.property.test.js` and the forecast spec):

1. **Partition invariant:** `blockers + with_dates == non_compliant`.
2. **Compliance formula:** `compliance_pct == ROUND((total - nc) / total * 1000) / 10` (or 0 when total is 0).
3. **Monotonic non-increasing:** each month's `non_compliant` is less than or equal to the previous month's.
4. **Horizon bound:** at most 12 forecast points; terminates early when only blockers remain.
5. **Past-due treated as current month:** dates in already-passed months are bucketed into the current month for projection purposes.

**Why it can drift:**

- The ratio method for historical data is an estimate. Verify by hand using the actual upload's Summary sheet for that month if precise historical numbers matter.
- The fallback `totalAssets = metricNcCount` triggers when no Summary data exists for the metric. This produces a `compliance_pct` of 0 because every "asset" is non-compliant. This is correct for the data we have — the chart cannot show compliance percentages for metrics that have only been observed as failures.

### Per-Vertical Detail and Burndown

**What it shows:** Stats and burndown for a single vertical (e.g., NTS_AEO).

**What feeds it:**
- Stats: `vcl_multi_vertical_summary` for that vertical, latest upload, with sub-team breakouts.
- Burndown: `compliance_items` for that vertical, deduplicated by hostname.

**How it is calculated:**

The vertical-level burndown deduplicates per hostname using the **first non-null** resolution date (any one is enough to mark the device In-Progress):

```javascript
// In the route handler, after fetching compliance_items for the vertical:
const deviceMap = {};
for (const row of rows) {
    if (!deviceMap[row.hostname]) {
        deviceMap[row.hostname] = { hostname: row.hostname, resolution_date: row.resolution_date };
    } else if (row.resolution_date && !deviceMap[row.hostname].resolution_date) {
        // Promote a null entry to In-Progress when any other row has a date
        deviceMap[row.hostname].resolution_date = row.resolution_date;
    }
}
const devices = Object.values(deviceMap);
const burndown = computeVerticalBurndown(devices);
```

`computeVerticalBurndown` returns the same shape as `computeAggregatedBurndown` but scoped to one vertical.

---

## Forecast Algorithms

The dashboard uses three different forecasting approaches depending on the data being projected.

### Linear Regression Forecast (Trend)

**Used by:** Trend chart on the VCL Report and CCP Metrics pages.

**Inputs:** Monthly compliance percentages from `compliance_snapshots` (one decimal place).

**Algorithm:** Least-squares linear regression on the time series.

```javascript
// X = month index (0, 1, 2, ...), Y = compliance_pct
slope     = (n * SUM(X*Y) - SUM(X) * SUM(Y)) / (n * SUM(X^2) - SUM(X)^2)
intercept = (SUM(Y) - slope * SUM(X)) / n
```

For each future month `i` (1, 2, 3 — three months out):

```javascript
forecast_pct = ROUND((slope * (n + i - 1) + intercept) * 10) / 10
forecast_pct = Math.min(100, Math.max(0, forecast_pct))   // clamp to [0, 100]
```

**Activation:** Forecast appears only when **3 or more** historical months exist. With fewer points, the regression is unreliable, so the dashed line is omitted.

**Why it works for compliance trends:** Compliance is bounded [0, 100] and changes slowly across months. A linear fit captures the directional trajectory ("we're trending up two points per month") accurately enough to inform planning, though it can over-predict near the boundaries (the clamp prevents impossible values).

### Resolution-Date Burndown Forecast

**Used by:** Aggregated burndown, per-vertical burndown, the deprecated team-level forecast in the legacy VCL Report.

**Inputs:** Active non-compliant devices and their `resolution_date` values.

**Algorithm:** Simple monthly bucketing — no math beyond grouping and counting.

```javascript
buckets = {}                                  // YYYY-MM → count
for each device with a non-null resolution_date:
    month = first 7 chars of resolution_date  // 'YYYY-MM-DD' → 'YYYY-MM'
    buckets[month] += 1

remaining = total_with_dates
projection = {}
for each month (sorted ascending):
    remaining -= buckets[month]
    projection[month] = { remediated: buckets[month], remaining }
```

**Projected Clear date:** the last month in `projection` **only if** `blockers === 0`. If any device lacks a date, no projection is shown — there is no honest way to forecast something with no commitment.

**Why this is preferred over regression for burndown:** Resolution dates are explicit human commitments. Linear regression on past remediation rates would project an average pace that ignores what teams have actually committed to. The bucketing approach reports exactly what has been promised and nothing more.

### Per-Metric Forecast (Historical + Projected)

**Used by:** The CCP Metrics per-metric forecast burndown chart.

**Inputs:** Historical snapshots (vertical-level) + current metric devices (per-metric) + the metric's `total_assets` from the Summary sheet.

**Algorithm:** Two separate parts joined at the current month.

**Historical part — Ratio Method:**

```javascript
// For each historical month from compliance_snapshots:
metric_share        = current_metric_nc / current_vertical_total_nc
month.non_compliant = ROUND(snapshot.non_compliant * metric_share)
month.compliance_pct = ROUND((total_assets - month.non_compliant) / total_assets * 1000) / 10
```

> The ratio method assumes the metric's share of vertical non-compliance is stable. If a metric was recently introduced or recently fixed at scale, the historical bars will be off. Validate against the source Summary sheet for that month if you need precision.

**Forecast part — Resolution-Date Bucketing:**

Identical to [Resolution-Date Burndown Forecast](#resolution-date-burndown-forecast), with one extra rule: **past-due dates roll into the current month**. A device with `resolution_date = '2026-02-15'` when today is May is bucketed into May, not February. Empirically, past-due dates are commitments that slipped — projecting them as remediating "now" reflects reality (the team is overdue and has to act this month) better than leaving them stuck in the past.

**Termination:** the loop exits as soon as `remaining_non_compliant <= blockers`. Once every dated device is projected to be done, continuing would just show flat blocker count.

---

## Cross-Cutting Correctness Rules

These rules apply across every metric on both pages. Violations indicate a real bug — the dashboard's tests verify these properties hold.

**Rule 1: Rollup-only aggregation across verticals.**
Every cross-vertical query that touches `vcl_multi_vertical_summary` filters with `WHERE team LIKE 'ALL:%'`. Aggregating both rollup rows and sub-team rows would double-count. (Validated by the `ccp-metrics-view-restructure` spec, Property 1.)

**Rule 2: Latest upload per vertical.**
Cross-vertical queries select `DISTINCT ON (vertical)` from `compliance_uploads ORDER BY vertical, id DESC` to take only the most recent upload per vertical. Older uploads contribute to `compliance_snapshots` but not to current totals.

**Rule 3: Snapshot deduplication.**
`compliance_snapshots` is keyed `UNIQUE(snapshot_month, vertical)` and updated via `ON CONFLICT DO UPDATE`. Re-uploading the same month for a vertical overwrites the earlier snapshot. Snapshots are upserted using the upload's `report_date` month, not the current calendar month, so a backfilled upload for March lands in `2026-03` even if it is uploaded in May.

**Rule 4: Status classification on duplicate hostnames.**
When a hostname has rows in multiple verticals (one active, one resolved), the snapshot logic uses `MIN(status)` inside a CTE — `'active'` lexicographically wins over `'resolved'`, so the device is classified as non-compliant. This guarantees `compliant + non_compliant <= total_devices` for every snapshot row.

**Rule 5: Per-(hostname, metric_id) deduplication.**
Queries that bucket or count active findings use `DISTINCT ON (hostname, metric_id)` with the canonical `ORDER BY hostname, metric_id, seen_count DESC, upload_id DESC`. A device failing the same metric in two verticals contributes one entry, not two. (Validated by the `compliance-duplicate-failing-metrics` spec, Properties 1–5.)

**Rule 6: Aggregation by `report_date`, not upload ID.**
Trends, top-recurring, and category-trend queries `GROUP BY report_date` rather than `GROUP BY id`. A multi-vertical day produces multiple `compliance_uploads` rows sharing one `report_date`. (Validated by the `compliance-duplicate-chart-entries` spec, Properties 1–3.)

**Rule 7: Decimal vs whole-number percentages.**
Two conventions coexist:

| Source | Format | Example |
|---|---|---|
| `vcl_multi_vertical_summary.compliance_pct` | Decimal | `0.95` |
| `compliance_snapshots.compliance_pct` | Whole-number | `95.00` |
| `/vcl/stats` response `stats.compliance_pct` | Whole-number integer | `95` |
| `/vcl-multi/stats` response `stats.compliance_pct` | Whole-number integer | `95` |
| `/vcl-multi/metrics` response `compliance_pct` | Decimal | `0.95` |

The frontend handles both — multiply decimals by 100 and call `toFixed(1)` for the metric-table view, or display the whole number directly for stats bars. Mismatching the formats is a common source of "values look 100x off" bugs.

---

## Verifying Values by Hand

When you suspect a number is wrong, work through this checklist before opening a bug.

**1. Check whether the value comes from `compliance_items` or `vcl_multi_vertical_summary`.**

- `compliance_items` only has non-compliant rows. If a "compliant count" is wrong, the bug is in the Summary sheet path, not item counting.
- `vcl_multi_vertical_summary` is the source of truth for both compliant and non-compliant totals on the CCP Metrics page.

**2. Check the upload date.**

- Cross-vertical numbers use the **latest upload per vertical**. If you uploaded NTS_AEO yesterday but TSI two weeks ago, the aggregate uses today's NTS_AEO and the two-week-old TSI.
- The "Last Upload" column in the vertical breakdown shows the `report_date` of each vertical's most recent upload.

**3. Check the snapshot.**

- The trend chart reads from `compliance_snapshots`, not from current `compliance_items`. If you fixed a hostname today, it will not appear in the trend until the next upload writes a new snapshot.
- Historical months are frozen — only the current month's snapshot updates on re-upload.

**4. Check for ALL: rollup vs sub-team aggregation.**

- If a vertical or cross-vertical total looks roughly 2x too high, you are probably summing rollup AND sub-team rows. Confirm with:

  ```sql
  SELECT team, COUNT(*) FROM vcl_multi_vertical_summary
  WHERE upload_id = <latest> AND metric_id = '<metric>'
  GROUP BY team;
  ```

  You should see one `ALL: <vertical>` row plus one row per sub-team. Use only the `ALL:` row for cross-vertical totals.

**5. Check for cross-vertical hostname collisions.**

- A hostname appearing in two verticals (e.g., it was migrated between teams) needs the deduplication rules in [Cross-Cutting Correctness Rules](#cross-cutting-correctness-rules) to count once. Confirm with:

  ```sql
  SELECT hostname, vertical, team, status, seen_count
  FROM compliance_items
  WHERE hostname = '<hostname>'
  ORDER BY hostname, vertical;
  ```

  If you see two rows with different `team` values, the device is counted under the team from its representative row (highest `seen_count`, then most recent `upload_id`).

**6. Reconcile against the Summary sheet.**

- Open the source xlsx for the upload, navigate to the Summary tab, and find the `ALL: <vertical>` row for the metric in question. The `Total`, `Compliant`, and `Non-Compliant` columns should match `vcl_multi_vertical_summary` exactly (the parser does not transform these numbers — it copies them verbatim).

If after these steps the displayed value still does not match the source data, file an issue with the SQL output from steps 4–5 and the relevant Summary sheet rows attached.

---

## Worked Example — Vulns_Aging vs 7.1.1 Forecast Charts

This example walks the playbook through two side-by-side per-metric forecast burndown charts that look very different despite using the same code path. It is the canonical case study for "the chart looks weird but the data is internally consistent."

### What the charts show

**Vulns_Aging — 17,628 devices.** Every bar is full-height, predominantly orange. The compliance line reads `0.1%` for March, jumps to `27.3%` in April, then back to `0.0%` for May. The total bar height is `17628` on every month.

**7.1.1 — 6,149 non-compliant out of ~66,674 total.** Every bar is mostly blue (compliant) with a thin orange slice at the top. The compliance line stays between `90.8%` and `93.3%`. The total bar height is `66674` on every month.

Both charts are produced by the same endpoint (`GET /api/compliance/vcl-multi/metric/:metricId/forecast-burndown`) and the same helper (`computeMetricForecastBurndown`). The difference is entirely in the input data.

### Walking the playbook

**Step 1 — Where does the value come from?**

Both charts blend two sources: `compliance_items` for active non-compliant devices, and `vcl_multi_vertical_summary` for the metric's `total_assets`. The historical bars also use `compliance_snapshots` (vertical-level totals) reshaped via the ratio method.

The differing input here is `total_assets`, sourced from this query:

```sql
SELECT SUM(total)::int AS total
FROM vcl_multi_vertical_summary
WHERE metric_id = $1 AND team LIKE 'ALL:%'
  AND upload_id IN (
      SELECT id FROM compliance_uploads
      WHERE vertical IS NOT NULL ORDER BY id DESC LIMIT 20
  );
```

For `7.1.1` this returns ~66,674. For `Vulns_Aging` it returns 0 or null, and the route handler falls back to `totalAssets = metricNcCount` — the device count from `compliance_items` instead.

**Step 2 — Check the upload date.**

Both metrics are read from the latest 20 uploads, so this is not the cause of the difference. Skip.

**Step 3 — Check the snapshot.**

`compliance_snapshots` is vertical-level only — it does not store per-metric totals. Both metrics use the same snapshot rows scaled by the ratio method:

```javascript
metricNc = ROUND(snapshot.non_compliant * (currentMetricNc / currentVerticalTotalNc))
```

For `Vulns_Aging` the ratio is large (most of the vertical's non-compliant load is aging vulnerabilities), so historical `metricNc` values are sizeable. April happened to have a snapshot where the vertical's total was smaller — the ratio method produced a metricNc of roughly `17628 * 0.727 ≈ 12810`, leaving `(17628 - 12810) / 17628 * 100 ≈ 27.3%` compliance for that bar.

That April spike is not a real compliance gain. It is an artifact of the historical snapshot's vertical-level non-compliant count being lower that month, multiplied by the metric's current share.

**Step 4 — ALL: rollup vs sub-team aggregation.**

Run the diagnostic query for each metric:

```sql
SELECT team, total, compliant, non_compliant
FROM vcl_multi_vertical_summary
WHERE metric_id = '7.1.1' AND team LIKE 'ALL:%'
ORDER BY upload_id DESC LIMIT 5;
```

For `7.1.1` this returns rows like `('ALL: NTS-AEO', 66674, 60525, 6149)` — a clean, complete rollup.

```sql
SELECT team, total, compliant, non_compliant
FROM vcl_multi_vertical_summary
WHERE metric_id = 'Vulns_Aging' AND team LIKE 'ALL:%'
ORDER BY upload_id DESC LIMIT 5;
```

For `Vulns_Aging` this returns **zero rows**. The Summary sheet of the source xlsx does not contain `Vulns_Aging` as a tracked metric. The metric exists only as a detail sheet inside the workbook, never as a Summary row.

**Step 5 — Cross-vertical hostname collisions.**

Confirm the device counts come from `compliance_items`:

```sql
SELECT COUNT(DISTINCT hostname) FROM compliance_items
WHERE metric_id = 'Vulns_Aging' AND status = 'active' AND vertical IS NOT NULL;
-- Returns 17628
```

This matches the chart's `total_assets` exactly, which is the smoking gun. The chart is showing 17,628 in scope and 17,628 non-compliant — `compliance_pct = 0%` is mathematically correct for the data the chart received.

**Step 6 — Reconcile against the Summary sheet.**

Open any recent xlsx upload for any vertical and inspect the Summary tab. Search for `Vulns_Aging` in the `Metric` column — it is not there. The Summary sheet enumerates the standard metric IDs (`2.3.5`, `5.2.4`, `7.1.1`, etc.) and has no row for the aging vulnerability dashboard.

Now search for `7.1.1` — it appears with rows for both the rollup (`ALL: NTS-AEO`) and each sub-team. The Summary's `Total`, `Compliant`, and `Non-Compliant` columns match `vcl_multi_vertical_summary` exactly.

### Why the charts look the way they do

**Vulns_Aging is a tracked-but-uncategorized detail metric.** The Python parser walks every detail sheet of the xlsx and writes one `compliance_items` row per non-compliant device, using the sheet name as the `metric_id`. `Vulns_Aging` is one such sheet. But because `Vulns_Aging` does not appear in the workbook's Summary sheet, no row is written to `vcl_multi_vertical_summary` — there is no rollup, no compliant count, no total population.

When the per-metric forecast endpoint asks for the metric's `total`, the query returns null. The handler falls back to `totalAssets = metricNcCount`, which is the count of non-compliant devices. The chart's denominator is therefore identical to its numerator, which forces every "current" compliance percentage to 0%. The April 27.3% spike is the historical ratio method projecting a smaller non-compliant count for that month against the same fallback denominator. May returns to 0% because May is the current month and uses the live device count, which always equals the fallback total by construction.

The chart is **internally consistent with the data the database has**. It is not internally consistent with what an executive expects "compliance" to mean for an aging dashboard, because the source xlsx never reported a population total for that detail. The fix is upstream — either the xlsx Summary sheet needs to include a `Vulns_Aging` row with a population total, or the handler needs a special case to mark metrics with no Summary data as "not measurable for compliance percentage" and render the chart differently (e.g., counts only, no percentage line).

**7.1.1 is a fully populated standard metric.** It has a sheet for the failing devices, a `metric_categories` mapping (`Logging & Monitoring`), and a Summary row with `Total`, `Compliant`, and `Non-Compliant` numbers. The route gets a real `totalAssets` value (~66,674), the compliance percentage is `(66674 - 6149) / 66674 ≈ 90.8%`, and the bar visualizes the actual ratio of compliant to non-compliant devices. Historical and forecast bars track real population data, not a fallback.

### What this tells you about the dashboard

The forecast burndown chart is honest about what it knows. When the source data lacks a population total, the chart degrades gracefully to "every device in scope is non-compliant" — which is a literal reading of the rows that exist. It does not fabricate a denominator. The cost is that metrics without Summary entries look catastrophically non-compliant on the chart even when the underlying business reality may be different.

The diagnostic flow above is the canonical procedure: when a chart looks wrong, walk down to the Summary sheet rows and ask whether the metric is even represented as a tracked compliance metric in the source file. If the answer is no, the chart is reflecting an upstream data shape, not a calculation bug.


---

## Worked Example — Vulns_Aging vs 7.1.1 Forecast Charts

This example walks the playbook through two side-by-side per-metric forecast burndown charts that look very different despite using the same code path. It is the canonical case study for "the chart looks weird but the data is internally consistent."

### What the charts show

**Vulns_Aging — 17,628 devices.** Every bar is full-height, predominantly orange. The compliance line reads `0.1%` for March, jumps to `27.3%` in April, then back to `0.0%` for May. The total bar height is `17628` on every month.

**7.1.1 — 6,149 non-compliant out of ~66,674 total.** Every bar is mostly blue (compliant) with a thin orange slice at the top. The compliance line stays between `90.8%` and `93.3%`. The total bar height is `66674` on every month.

Both charts are produced by the same endpoint (`GET /api/compliance/vcl-multi/metric/:metricId/forecast-burndown`) and the same helper (`computeMetricForecastBurndown`). The difference is entirely in the input data.

### Walking the playbook

**Step 1 — Where does the value come from?**

Both charts blend two sources: `compliance_items` for active non-compliant devices, and `vcl_multi_vertical_summary` for the metric's `total_assets`. The historical bars also use `compliance_snapshots` (vertical-level totals) reshaped via the ratio method.

The differing input here is `total_assets`, sourced from this query:

```sql
SELECT SUM(total)::int AS total
FROM vcl_multi_vertical_summary
WHERE metric_id = $1 AND team LIKE 'ALL:%'
  AND upload_id IN (
      SELECT id FROM compliance_uploads
      WHERE vertical IS NOT NULL ORDER BY id DESC LIMIT 20
  );
```

For `7.1.1` this returns ~66,674. For `Vulns_Aging` it returns 0 or null, and the route handler falls back to `totalAssets = metricNcCount` — the device count from `compliance_items` instead.

**Step 2 — Check the upload date.**

Both metrics are read from the latest 20 uploads, so this is not the cause of the difference. Skip.

**Step 3 — Check the snapshot.**

`compliance_snapshots` is vertical-level only — it does not store per-metric totals. Both metrics use the same snapshot rows scaled by the ratio method:

```javascript
metricNc = ROUND(snapshot.non_compliant * (currentMetricNc / currentVerticalTotalNc))
```

For `Vulns_Aging` the ratio is large (most of the vertical's non-compliant load is aging vulnerabilities), so historical `metricNc` values are sizeable. April happened to have a snapshot where the vertical's total non-compliant count was lower — the ratio method produced a `metricNc` of roughly `17628 * 0.727 ≈ 12810`, leaving `(17628 - 12810) / 17628 * 100 ≈ 27.3%` compliance for that bar.

That April spike is not a real compliance gain. It is an artifact of the historical snapshot's vertical-level non-compliant count being lower that month, multiplied by the metric's current share.

**Step 4 — ALL: rollup vs sub-team aggregation.**

Run the diagnostic query for each metric:

```sql
SELECT team, total, compliant, non_compliant
FROM vcl_multi_vertical_summary
WHERE metric_id = '7.1.1' AND team LIKE 'ALL:%'
ORDER BY upload_id DESC LIMIT 5;
```

For `7.1.1` this returns rows like `('ALL: NTS-AEO', 66674, 60525, 6149)` — a clean, complete rollup.

```sql
SELECT team, total, compliant, non_compliant
FROM vcl_multi_vertical_summary
WHERE metric_id = 'Vulns_Aging' AND team LIKE 'ALL:%'
ORDER BY upload_id DESC LIMIT 5;
```

For `Vulns_Aging` this returns **zero rows**. The Summary sheet of the source xlsx does not contain `Vulns_Aging` as a tracked metric. The metric exists only as a detail sheet inside the workbook, never as a Summary row.

**Step 5 — Cross-vertical hostname collisions.**

Confirm the device counts come from `compliance_items`:

```sql
SELECT COUNT(DISTINCT hostname) FROM compliance_items
WHERE metric_id = 'Vulns_Aging' AND status = 'active' AND vertical IS NOT NULL;
-- Returns 17628
```

This matches the chart's `total_assets` exactly, which is the smoking gun. The chart is showing 17,628 in scope and 17,628 non-compliant — `compliance_pct = 0%` is mathematically correct for the data the chart received.

**Step 6 — Reconcile against the Summary sheet.**

Open any recent xlsx upload for any vertical and inspect the Summary tab. Search for `Vulns_Aging` in the `Metric` column — it is not there. The Summary sheet enumerates the standard metric IDs (`2.3.4i`, `5.2.4`, `7.1.1`, etc.) and has no row for the aging vulnerability dashboard.

Now search for `7.1.1` — it appears with rows for both the rollup (`ALL: NTS-AEO`) and each sub-team. The Summary's `Total`, `Compliant`, and `Non-Compliant` columns match `vcl_multi_vertical_summary` exactly.

### Why the charts look the way they do

**Vulns_Aging is a tracked-but-uncategorized detail metric.** The Python parser walks every detail sheet of the xlsx and writes one `compliance_items` row per non-compliant device, using the sheet name as the `metric_id`. `Vulns_Aging` is one such sheet. But because `Vulns_Aging` does not appear in the workbook's Summary sheet, no row is written to `vcl_multi_vertical_summary` — there is no rollup, no compliant count, no total population.

When the per-metric forecast endpoint asks for the metric's `total`, the query returns null. The handler falls back to `totalAssets = metricNcCount`, which is the count of non-compliant devices. The chart's denominator is therefore identical to its numerator, which forces every "current" compliance percentage to 0%. The April 27.3% spike is the historical ratio method projecting a smaller non-compliant count for that month against the same fallback denominator. May returns to 0% because May is the current month and uses the live device count, which always equals the fallback total by construction.

The chart is **internally consistent with the data the database has**. It is not internally consistent with what an executive expects "compliance" to mean for an aging dashboard, because the source xlsx never reported a population total for that detail. The fix is upstream — either the xlsx Summary sheet needs to include a `Vulns_Aging` row with a population total, or the handler needs a special case to mark metrics with no Summary data as "not measurable for compliance percentage" and render the chart differently (e.g., counts only, no percentage line).

**7.1.1 is a fully populated standard metric.** It has a sheet for the failing devices, a `metric_categories` mapping (`Logging & Monitoring`), and a Summary row with `Total`, `Compliant`, and `Non-Compliant` numbers. The route gets a real `totalAssets` value (~66,674), the compliance percentage is `(66674 - 6149) / 66674 ≈ 90.8%`, and the bar visualizes the actual ratio of compliant to non-compliant devices. Historical and forecast bars track real population data, not a fallback.

### What this tells you about the dashboard

The forecast burndown chart is honest about what it knows. When the source data lacks a population total, the chart degrades gracefully to "every device in scope is non-compliant" — which is a literal reading of the rows that exist. It does not fabricate a denominator. The cost is that metrics without Summary entries look catastrophically non-compliant on the chart even when the underlying business reality may be different.

The diagnostic flow above is the canonical procedure: when a chart looks wrong, walk down to the Summary sheet rows and ask whether the metric is even represented as a tracked compliance metric in the source file. If the answer is no, the chart is reflecting an upstream data shape, not a calculation bug.
