Compliance and Privacy Metrics, Monitoring and Reporting Questions

1. Data accuracy — daily violation counts match source-of-truth ETL table

• Pass: Aggregated counts on the dashboard equal the results of the canonical SQL query against the compliance_events table for each region/date (exact match).
• Fail: Any region/date count differs; or discrepancy > 0 rows. Test: run provided SQL and compare.

2. Freshness — dashboard reflects most recent load within SLA

• Pass: Dashboard shows data up to the last ETL batch timestamp; the latest event timestamp is ≤ 24 hours old (or SLA agreed) and matches ETL metadata.
• Fail: Latest data older than SLA or ETL metadata indicates failure. Test: compare dashboard max(event_time) to ETL run_time.

3. Permissions & access control

• Pass: Role-based access enforces permissions: users without compliance role cannot view PII columns or restricted regions; filters respect row-level security; test users validated.
• Fail: Unauthorized user can access restricted data or export sensitive columns. Test: validate with five personas (admin, compliance, regional manager, auditor, guest).

4. Reproducibility — query and ETL lineage provided

• Pass: Dashboard includes documented SQL/dashboard queries, ETL job IDs, dataset version, and instructions to reproduce the daily numbers end-to-end. Running documented steps yields identical results.
• Fail: Missing or non-functional reproduction steps. Test: follow docs to reproduce counts.

5. Auditability & logging

• Pass: All dashboard exports, data refreshes, and user views are logged; logs include user, timestamp, action, and dataset version; logs retained per policy (e.g., 1 year).
• Fail: Missing logs or insufficient detail for an audit. Test: produce sample audit log for a dashboard view and export.

6. Visual & granularity checks

• Pass: Time granularity defaults to daily; region filter includes all active regions and maps consistently to canonical region codes; date axis shows local timezone per spec.
• Fail: Missing regions, incorrect granularity, or mismatched region codes. Test: compare listed regions to master region table and verify default date binning.

Each criterion is binary: provide evidence (query outputs, ETL metadata, permission test results, logs) to mark Pass/Fail before release.

Requirements summary:

• Purpose: single-pane-of-glass for GDPR DSAR intake, progress, SLA compliance and root-cause analysis for stakeholders (Privacy team, Legal, Ops, Execs).
• Audience/Views: Privacy Ops (detailed queue), Team Leads (workload & SLA), Execs (trends, risk).

Required fields (per DSAR record shown in detail panel + export):

• DSAR ID (unique)
• Case creation timestamp (UTC)
• Requestor identity ID(s) and type (user/customer/employee)
• Source channel (web form, email, phone, partner)
• Case owner / assigned team
• Case status (New, In progress, Awaiting verification, Completed, Rejected)
• Priority / risk level
• SLA target (days) and SLA deadline timestamp
• Fulfillment timestamp (if completed)
• Verification steps completed (KYC flags)
• Data items requested (categories)
• Outcome (fulfilled/partial/rejected) and reason codes
• Related systems touched (CRM, Marketing DB, Data Lake)
• Notes / escalation flags / appeals
• Last updated timestamp; created_by, updated_by

KPIs (dashboard tiles + drilldowns):

• Open DSAR count (by status, team)
• New DSARs (rolling 7/30/90 days)
• Avg time-to-fulfill (median + mean) — overall and by team/type/channel
• SLA breach count & % (past period) and trend
• Time-in-state distribution (avg days in each status)
• Backlog age profile (count by age buckets: 0–3,4–7,8–14,15+ days)
• Verification failure rate & rework count
• Escalations and appeals count
• Closed-first-contact % (fulfilled without follow-up)
• Workload per analyst (active cases, throughput)
• Root-cause breakout (by reason code: missing identity, system access, data not found)

Filters and interactions:

• Date range, rolling window presets
• Status, team, owner, priority
• Requestor type, channel, outcome
• SLA window (custom)
• Data category requested
• Free-text search on DSAR ID / requestor ID / notes
• Drill from KPI to case list and case details; export selected cases

Data sources & integration:

• Primary: Case management system (source of truth for case lifecycle)
• Identity store / HR / CRM (for requestor attributes and identity verification)
• Audit logs / access logs (to validate data retrieval actions)
• Data catalog / system inventory (to map systems touched)
• ETL layer: scheduled incremental loads from APIs or CDC; near-real-time for SLA-sensitive fields

Ownership & access:

• Dashboard steward: BI team (owner) + Privacy Operations as subject-matter owner
• Data owners: Case mgmt vendor/data engineers, Identity store owner
• Access control: Row-level security (RLS) by team; masked PII for exec view
• Update cadence: near-real-time/ hourly for ops; daily summary for execs

Validation approach (to ensure dashboard matches source):

1. Source-query parity: store canonical SQL/API queries used by dashboard; run them directly against case mgmt to compare counts/aggregates.
2. Row-level reconciliation: daily automated job that compares key fields (DSAR ID, status, created_at, fulfilled_at) between dashboard materialized tables and case management export; report diffs.
3. KPI reconciliation tests:
   • Recompute KPIs in a validation pipeline and compare tolerance thresholds (e.g., counts exact; avg time tolerances ±1 minute).
4. Sampling audit: weekly manual spot-check of sample cases (10–20) verifying dashboard values vs case record and audit logs.
5. Alerting: data drift alerts when reconciliation fails or when ETL latency > SLA threshold.
6. Regression tests: on ETL/schema changes, run full reconciliation in staging.
7. Documentation & lineage: maintain data lineage in catalog, including transformation logic and business rules for SLA calculation.
8. Governance: monthly review with Privacy Ops to sign off on metrics and rule changes.

Implementation notes / best practices:

• Calculate SLA using business days if required; store both raw and business-day-adjusted SLA fields.
• Use median for skewed time distributions; show both mean and median.
• Expose raw export for compliance requests and investigations.
• Keep PII minimization: only show hashed IDs where not necessary.
• Provide annotations for SLA policy changes and show baseline periods for trend comparisons.

This specification enables a BI-built DSAR tracker that is auditable, testable, and actionable for privacy compliance.

1. Open findings count — Indicates backlog and immediate risk exposure; target: ≤ 50 active findings enterprise-wide (or trending down month-over-month by ≥10%).
2. Average time-to-remediate (MTTR) — Shows responsiveness of owners and effectiveness of remediation processes; SLA: critical ≤ 7 days, high ≤ 30 days, medium ≤ 90 days.
3. Percent of findings overdue — Measures process adherence and bottlenecks; target: ≤ 5% overdue at any time.
4. Audit pass rate (controls tested vs. passed) — Reflects control effectiveness and readiness for external audits; target: ≥ 95% pass rate per audit cycle.
5. Percent of controls automated — Tracks maturity and efficiency of control execution; target: ≥ 60% automation for routine controls within 12 months.
6. Repeat findings rate (recurrence within 12 months) — Highlights chronic issues and root-cause effectiveness; target: ≤ 10% of findings are repeats.
7. Risk-weighted exposure (sum of findings × severity weight) — Prioritizes remediation by business impact rather than count; target: month-over-month reduction ≥ 8%.
8. Evidence delivery SLA (percent of evidence provided on time for audits) — Measures stakeholder readiness and audit support efficiency; target: ≥ 98% evidence delivered within agreed SLA.

For BI: present these as a single dashboard with trend lines, drilldowns by control owner/business unit, heatmap of risk-weighted exposure, and alerts for SLA breaches.