---
title: Data Quality and Governance — RGM Training
url: https://realgrowthmatters.com/training/marketing-operations/data-quality-and-governance/
updated: 2026-06-10
source_html: https://realgrowthmatters.com/training/marketing-operations/data-quality-and-governance/
---

[Home](../../../index.html) › [Training](../../index.html) › [Marketing Operations](../index.html) › Data Quality and Governance

RGM° · Training

# Data Quality and Governance

Unglamorous and the most leveraged investment in any analytics program. Six dimensions, lifecycle, testing, monitoring, MDM.

### What you will learn

1. [Why data quality matters](#why)
2. [Dimensions of data quality](#dimensions)
3. [Data quality lifecycle](#lifecycle)
4. [Automated testing](#testing)
5. [Production monitoring](#monitoring)
6. [Cataloging and lineage](#cataloging)
7. [Privacy and compliance](#privacy)
8. [Master data management](#master)
9. [Advanced playbook](#advanced)
10. [Common mistakes](#mistakes)
11. [Operating checklist](#checklist)

## Why data quality matters

Bad data flows downstream silently. Reports show wrong numbers; algorithms learn from noise; decisions optimize the wrong outcomes. Data quality is unglamorous and the most leveraged investment in any analytics program.

## Six dimensions

| Dimension | What it means |
| --- | --- |
| Accuracy | Values reflect reality |
| Completeness | No missing required fields |
| Consistency | Same value across systems |
| Timeliness | Data is fresh enough |
| Validity | Conforms to expected format/range |
| Uniqueness | No unintended duplicates |

## Lifecycle

1. **Collection.** Validate at point of capture.
2. **Ingestion.** Test schemas at warehouse load.
3. **Transformation.** Tests on derived tables (dbt tests).
4. **Consumption.** Anomaly detection in dashboards.
5. **Feedback.** Issues reported and tracked.
6. **Remediation.** Root cause fixed; not just patched.

## Automated testing

- **Schema tests.** Column types, nullable, allowed values.
- **Uniqueness tests.** Primary keys actually unique.
- **Referential integrity.** Foreign keys resolve.
- **Freshness tests.** Data updated within expected window.
- **Volume tests.** Row counts within expected range.
- **Business logic tests.** Domain-specific validity (e.g., revenue > 0).
- **Tools:** dbt tests, Great Expectations, Monte Carlo.

## Production monitoring

- Real-time alerts on data quality breaches.
- Daily summary of test passes/failures.
- Anomaly detection on key metrics.
- Data SLAs with owners and response times.
- Incident response runbook.
- Postmortems for significant issues.

## Cataloging and lineage

- **Data catalog.** Searchable inventory of tables, columns, definitions.
- **Lineage tracking.** Where data comes from; what depends on it.
- **Ownership documented.** Who maintains each dataset.
- **Tools:** dbt docs, Atlan, Alation, Collibra.
- **Self-service.** Stakeholders find their data without asking.

## Privacy and compliance

- PII handling documented.
- Deletion requests propagate across systems.
- Retention policies enforced.
- Access controls role-based.
- Consent management integrated.
- GDPR, CCPA, sector-specific compliance.
- Audit trails for sensitive data access.

## Master data management

- **Customer master.** Canonical customer identity across systems.
- **Product master.** Single source of truth for SKU data.
- **Reference data.** Standardized values (country codes, currencies).
- **Hierarchies.** Organizational structures, product categories.
- **Stewardship.** Roles responsible for master data accuracy.

## Advanced playbook

- **Data contracts.** Formal agreements between producers and consumers; enforced in CI.
- **Shift-left testing.** Tests at producer (source application) not consumer (warehouse).
- **Anomaly detection ML.** Beyond rule-based tests; ML-detected pattern breaks.
- **Data observability platforms.** Monte Carlo, Bigeye, Soda — comprehensive monitoring.
- **SLA tiers.** Critical data has hour-level SLA; secondary has day-level.
- **Quarterly DQ review.** Top issues, root causes, prevention plans.
- **Cross-functional ownership.** Engineering owns producers; analytics owns warehouse; together own quality.
- **Documentation as deliverable.** Data catalog kept current alongside code.
- **Incident postmortems.** Each significant DQ incident reviewed for prevention.
- **Annual privacy compliance audit.** External or internal review of data handling.

## Common mistakes

- No automated testing; issues found late.
- Tests at consumer only; producer issues missed.
- No data ownership; orphaned datasets.
- Incident remediation patches symptoms; root cause unaddressed.
- Catalog missing; nobody knows where data is.
- Lineage not tracked; impact of changes unknown.
- Privacy compliance reactive; audit reveals gaps.
- Master data inconsistent across systems.
- No incident postmortems; same issues recur.
- Access controls coarse; sensitive data over-shared.
- Retention policies undocumented.
- Data quality metrics not reported to leadership.

## Operating checklist

- Automated data quality testing (dbt tests, Great Expectations, etc.)
- Production monitoring with alerts
- Data catalog current
- Lineage tracking
- Data ownership documented
- SLA tiers documented
- Privacy compliance audit annually
- Master data management discipline
- Incident postmortems for significant issues
- Quarterly DQ review
- Data contracts where applicable
- Access controls role-based

## Sources and further reading

- Monte Carlo — data observability methodology
- Bigeye, Soda — data quality platforms
- dbt tests documentation
- Great Expectations documentation
- Atlan, Alation, Collibra — data catalogs
- Chad Sanderson — data contracts
- David Jayatillake — data quality writing
- DAMA International data governance
- Modern Data Stack community
- GDPR and CCPA compliance documentation
- Tristan Handy and dbt community
- RGM Marketing Analytics fundamentals module

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Part of the [Marketing Operations](../index.html) series.