Data Governance & Quality Control
Keeping product data consistent, compliant, and publication-ready.
Introduction
Data governance and quality control keep product catalogs accurate, compliant, and ready to publish. This case study shows how I define catalog standards, enforce validation rules, and build monitoring frameworks that keep product data reliable at scale. The work highlights my ability to design governance playbooks, formalize attribute specifications, translate standards into measurable rules, and close the loop through auditing and remediation.
The work below illustrates how I applied governance and quality control best practices to a Women’s Loafers demo catalog, translating standards into policies, validation rules, monitoring tools, and audit reports.
1. Catalog Governance & Quality Playbook
In this playbook I define a governance framework for catalog data using a Women’s Loafers demo set. It translates GS1 (Global Standards 1) and marketplace rules into policies for attributes, measurements, and images—supported by roles, decision rights, and review cadences. Appendices detail packaging hierarchy and accountability.
Impact
This playbook creates an auditable, role-based system that reduces listing rejections, improves accuracy, and keeps product data consistent across channels.
2. Attribute Governance & Measurement Specification
In this specification I define rules for catalog attributes—titles, colors, dimensions, and images—aligned to GS1 and marketplace standards. It establishes a single source of truth so data stays consistent, searchable, and publication-ready.
Impact
This specification reduces catalog errors, improves data quality, and supports reliable operations across ecommerce systems.
3. Validation Rules & KPI Checklist
In this checklist I translate standards into testable rules tied to measurable KPIs, with thresholds for GTINs, titles, dimensions, images, and more. A companion one-page mini sample distills five high-impact rules into a quick-reference format for daily use.
Impact
This checklist provides a transparent framework that reduces errors, speeds approvals, and creates auditable proof that product data meets compliance and quality targets.
4. Monitoring & Remediation Pack
In this pack I show how catalog data quality is monitored and resolved, combining a monitoring plan with a mock dashboard. It defines review cadences, ownership, remediation workflows, and KPI reporting views.
Impact
This pack prevents errors from persisting, speeds resolution through clear accountability, and creates transparency by linking validation rules directly to monitoring.
5. Catalog Data QA Audit — Demo Report & Template
In this package I demonstrate how catalog data quality can be audited and measured in practice, using a Women’s Loafers demo dataset. The report includes KPI pass/fail summaries, an issue log, and before/after fixes, while the template provides a reusable structure for SKU-level audits.
Impact
This package makes catalog quality visible, quantifies compliance against standards, and proves improvements with clear, auditable results.