In an ecosystem where retailers rely on data to sell effectively, the quality of product information has become a strategic lever. Incomplete, inconsistent or poorly structured data sheets lead to catalog errors, reduced online visibility and, ultimately, lost sales.
Data governance aims to ensure that every piece of data transmitted by a supplier is reliable, compliant and usable by distributors.
Define roles and responsibilities
A clear governance process begins with the definition of roles:
- Data managers/Data owners: guaranteeing the consistency and conformity of information within the company.
- Business referents (Category Manager/Product Manager): contribute their product expertise to qualify critical data.
- Contributors (Data steward): ensure regular updating of information in the validation portal (manufacturer/distributor)
This organization avoids duplication and ensures complete traceability of modifications.
Structuring the product data life cycle
Good governance is based on a controlled life cycle:
- Creation: all new data must be entered in a format that complies with portal requirements.
- Validation: automatic or manual check before publication.
- Publication: the data becomes available to distributors.
- Monitoring: quality indicators (completeness, consistency, freshness) ensure continuous monitoring.
- Continuous improvement: feedback from distributors is used to identify areas for improvement.
Integrate quality and compliance rules
An effective governance framework is based on shared business rules:
- Harmonization of units of measurement and field formats (weight, dimensions, GTIN code).
- Standardization of visuals (resolution, angle, neutral background).
- Automatic verification of critical values (references, compatibilities, certifications).
Monitoring performance and involving teams
Governance is not a one-off control, but a continuous steering process, with committees organizing the circulation of information.
Dashboards are used to :
- measure data quality by supplier and by category,
- identify recurring anomalies,
- promote exemplary suppliers.
Regular communication between suppliers and distributors encourages collective improvement in data quality.
