Data Quality & Ownership Model Template

Purpose

This template helps operational teams make source data trustworthy and actionable by defining clear ownership, simple SLAs, routine validation checks, reconciliation steps, incident response, and a repeatable quarterly review agenda. Use it as a starting point and tailor thresholds, cadence, and roles to your environment.

How to use this template

1. Copy the template into your team workspace.
2. Populate the Data Element Catalog for the systems and feeds your operations use.
3. Assign an Owner (accountable) and a Steward (day-to-day care) for each element.
4. Set realistic SLAs and validation rules, then implement routine checks and reconciliation routines.
5. Run the quarterly review, close actions, and iterate.

Roles & responsibilities (recommended)

• Data Owner — accountable for correctness and business use of the data element; approves thresholds and SLA targets and signs off on fixes.
• Data Steward — performs day-to-day validation, runs reconciliation, coordinates fixes, documents assumptions and lineage.
• Data Custodian / IT — maintains pipelines and infrastructure, implements automated checks, and supports incident remediation.
• Data Consumer — uses the data, reports quality issues, and participates in reviews when metrics impact decisions.

Data Element Catalog (fields to capture)

For each data element (or logical dataset) capture the following fields. Treat this as a concise living record rather than a one-time exercise.

Example catalog entries (short)

Suggested SLAs and thresholds (examples)

• Freshness — streaming feeds: within 15 minutes; batch feeds: available within agreed window (e.g., by 06:00 daily).
• Completeness — required elements present in >99% of records for operational dashboards.
• Null Rate — for critical numeric fields <0.5% (adjust per reality).
• Accuracy tolerance — for reconciled totals, differences <1% are acceptable before triggering an investigation.

Validation rules - practical examples

• Range checks (e.g., temperature > -40 and < 150). Flag out-of-range values.
• Null checks (required vs optional fields). Send daily report of missing required fields.
• Format/conformity (timestamp must be ISO 8601; IDs must match regex pattern).
• Cross-field rules (if status = 'shipped' then ship_date must not be null).
• Uniqueness (unique transaction IDs per day).

Reconciliation routines (template)

1. Schedule automated reconciliation job (daily/hourly depending on SLA).
2. Compare primary source to secondary system or summary totals.
3. Calculate discrepancy metrics (absolute, percent) and classify severity.
4. Auto-create incident tickets for discrepancies above threshold and notify Owner/Steward.
5. Steward investigates root cause, documents findings, and closes ticket when fixed.

Incident handling and escalation (recommended)

Define severity levels and corresponding response times.

• Severity 1 (Critical) — data unavailable or corrupted for critical dashboards; response within 1 hour, Owner escalation, all-hands if needed.
• Severity 2 (High) — KPI drift or repeated validation failures impacting decisions; response within 4 business hours.
• Severity 3 (Medium) — minor quality issues or documentation gaps; response within 3 business days.

Always record incident root cause, corrective action, and preventive action (RCA and CAPA brief) in the catalog notes.

Quarterly data quality review agenda (60–90 minutes)

1. Opening (5 min) — purpose, participants, owner review.
2. Key metrics snapshot (10–15 min) — completeness, null rate, timeliness, reconciliation failures since last review.
3. Incidents & trends (15–20 min) — high-severity incidents, root causes, open actions.
4. Priority elements (15–20 min) — review top 5 operational data elements affecting decisions.
5. Action planning (10–15 min) — owners commit to fixes, automation, or documentation tasks with dates.
6. Wrap-up (5 min) — confirm owners and next review date.

Straightforward KPIs to track

• Completeness % by element
• Null rate % by element
• Timeliness SLA compliance %
• Reconciliation discrepancy %
• Number of open data incidents

Implementation checklist

1. Create or extend the Data Element Catalog with the fields above.
2. Assign Owners and Stewards for all critical elements.
3. Define SLAs and validation rules for each critical element.
4. Automate routine checks where possible and configure alerts for breaches.
5. Define reconciliation jobs and incident ticket templates.
6. Schedule recurring quarterly DQ reviews and populate agenda with owners.

Tailoring notes

Start lightweight: focus first on the 10–20 data elements that most affect operational decisions. Apply stricter checks to high-risk elements and lighter checks to exploratory data. Update thresholds after two review cycles based on observed false positives and operational impact.

Templates & quick text

Next steps

Copy this template into your team domain, complete the catalog for your highest-impact elements, and run the first monthly reconciliation and the first quarterly review within 60 days.

Where this template can improve with platform capabilities

Consider adding an interactive catalog form so teams can save entries and track incidents. Connect checks to automated jobs and store submissions for historical dashboards and trend analysis.