--- name: data-quality-metrics-dashboard-requirements-builder description: "Builds a structured data quality dashboard requirements document defining metrics (fill-rate, accuracy, freshness, cost-per-record, pipeline reliability), acceptance criteria per segment, calculation logic, data sources, and alert thresholds — formatted as a handoff spec for a BI or reporting partner. Use when scoping a data quality dashboard, defining DQ metrics and SLAs, or writing requirements for a reporting team to build against." argument-hint: "[data-domain] [segments]" allowed-tools: ["Read"] --- # Data Quality Metrics Dashboard Requirements Builder ## Purpose Translate vague "we need better data quality visibility" requests into a precise, buildable requirements document — with defined metrics, calculation logic, per-segment acceptance criteria, and alert thresholds — so a BI or reporting partner can build the dashboard without a dozen clarifying calls. ## When to Use Invoke this skill when the user asks to: - Scope or spec a data quality dashboard for a BI/reporting team - Define data quality metrics (fill-rate, accuracy, freshness, cost-per-record, pipeline reliability) - Set acceptance criteria or SLAs for data quality by segment - Write requirements to hand off to an analytics engineer or BI partner - Establish thresholds and alerting for a data pipeline or CRM data health - Standardize how data quality is measured across records, accounts, or segments ## Inputs Required Before starting, collect: - **Data domain**: What data is being measured (e.g., CRM accounts, lead records, product usage events, enrichment data) - **Primary systems/sources**: Where the data lives (Salesforce, HubSpot, warehouse tables, enrichment vendor feeds) - **Segments to track separately**: How data should be sliced (e.g., by region, tier, source, record type, owner team) - **Key fields that matter most**: The 5–15 fields where quality has business impact (e.g., email, industry, employee count, MRR) - **Consumers of the data**: Who relies on it downstream (SDRs, forecasting, routing, marketing ops) - **Existing SLAs or pain points**: Known problems or targets already agreed upon - **BI/reporting stack**: The tool the dashboard will be built in (Tableau, Looker, Power BI, Sigma, etc.) If segments or key fields are missing, propose a sensible default set and flag it as an assumption for the buyer to confirm. ## Instructions ### Step 1: Confirm Scope and Segmentation Grain Establish the unit of analysis before defining any metric. - **Grain**: Is quality measured per record, per account, per event, or per field? - **Segmentation dimensions**: List the 2–4 dimensions each metric will be sliced by (region, tier, source, owner). - **Reporting frequency**: Real-time, daily snapshot, or weekly rollup — this drives freshness expectations. - State the grain explicitly at the top of the output; every metric below inherits it. ### Step 2: Define the Five Core Metric Families For each of the five families, produce a metric definition block with: **Metric name**, **What it measures**, **Calculation formula (in plain SQL-like pseudocode)**, **Data source(s)**, **Segmentation applied**, and **Refresh cadence**. **Family 1 — Fill-Rate (Completeness)** - Percentage of records where a required field is populated with a non-null, non-placeholder value. - Formula pattern: `COUNT(records WHERE field IS NOT NULL AND field NOT IN ('', 'N/A', 'unknown')) / COUNT(records) per segment` - Define completeness per key field AND a weighted composite fill-rate across the field set. **Family 2 — Accuracy (Validity/Conformance)** - Percentage of populated values that conform to expected format, range, or reference list. - Formula pattern: `COUNT(records WHERE field MATCHES validation_rule) / COUNT(records WHERE field IS NOT NULL)` - Specify the validation rule per field (email regex, employee-count range, industry in picklist, domain resolves). **Family 3 — Freshness (Timeliness)** - Age of the most recent update relative to expected refresh cadence. - Formula pattern: `NOW() - MAX(last_modified) per record/segment`; report % of records updated within SLA window. - Distinguish source freshness (when the pipeline last ran) from record freshness (when a record last changed). **Family 4 — Cost-per-Record (Efficiency)** - Total data acquisition/enrichment/maintenance cost divided by records processed or held. - Formula pattern: `SUM(enrichment_spend + tooling_cost) / COUNT(records) per source/segment` - Include a cost-per-usable-record variant (cost / records passing accuracy + fill thresholds). **Family 5 — Pipeline Reliability (Operational Health)** - Success rate and stability of the jobs feeding the data. - Metrics: job success rate (`successful_runs / total_runs`), duplicate rate, row-count variance vs. prior run (%), and SLA-miss count. - Flag anomalies where row counts deviate more than a defined % from the trailing average. ### Step 3: Set Acceptance Criteria Per Segment Build a segment × metric matrix. For each metric family, define three thresholds per segment: - **Target** (green): the desired state - **Acceptable** (yellow): tolerable but monitored - **Breach** (red): triggers action - Higher-value segments (top tier, primary regions) warrant stricter thresholds than long-tail segments — make this explicit rather than applying one flat standard. ### Step 4: Define Alerting and Ownership For each red-threshold breach, specify: - **Trigger condition** (metric + threshold + segment) - **Alert channel** (Slack, email, dashboard flag) - **Owner** (which role investigates) - **Escalation window** (how long before it escalates) ### Step 5: Specify Dashboard Layout for the BI Partner Describe the intended views so the reporting team can build directly: - **Executive summary tile**: composite health score per segment, RAG-coded - **Metric drill-downs**: one view per metric family with segment filters - **Trend view**: each metric over time (rolling 90 days minimum) - **Breach log**: table of current and recent threshold violations - List required filters, default date range, and any calculated fields the BI tool must create. ## Output Format - **Structure**: A requirements document organized in five parts — Scope & Grain, Metric Definitions, Acceptance Criteria Matrix, Alerting & Ownership, Dashboard Layout Spec - **Length**: 1,000–1,500 words - **Format**: Structured document with metric definition blocks, one acceptance-criteria table (segment × metric with three thresholds each), and a bulleted layout spec - **Sections**: Scope & Segmentation Grain → Core Metrics (5 families with full definition blocks) → Acceptance Criteria Matrix → Alerting & Ownership → Dashboard Layout Spec → Open Questions for the BI Partner - **Tone**: Data ops manager writing a handoff spec — precise, unambiguous, formula-first; no marketing language ## Quality Checklist Before delivering output, verify: - [ ] All five metric families are defined (fill-rate, accuracy, freshness, cost-per-record, pipeline reliability) - [ ] Every metric includes a calculation formula in SQL-like pseudocode — not just a description - [ ] The acceptance criteria matrix includes three thresholds (target/acceptable/breach) for every segment - [ ] Segmentation grain is stated once and applied consistently across all metrics - [ ] Every red-threshold breach has an assigned owner and alert channel - [ ] Dashboard layout spec names specific views, filters, and calculated fields the BI tool must create ## Notes - Accuracy is the metric most often skipped because it requires validation rules — push the buyer to define at least a format or reference-list rule per key field, since fill-rate without accuracy overstates health. - Freshness commonly conflates pipeline-run time with record-change time; keep them as separate metrics or the dashboard will hide stale-but-recently-touched records. - One flat threshold across all segments is the most common spec mistake — tiered acceptance criteria are what make the dashboard actionable for a RevOps team. - If the BI tool can't compute a metric server-side (e.g., cost-per-record spanning finance and CRM data), note it as an "Open Question for the BI Partner" rather than assuming it's buildable. --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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