Work from one clear base
Define fields, values and sources once, for everyone.
Product Data Governance
Product data everyone can trust.
Without clear owners, rules and checks, you quickly lose control of product data. Versions start to differ, agreements become unclear and no one knows what's authoritative any more. We bring structure to ownership, rules and control, so teams and channels work from one reliable base.
Organisations staying in control of their product data
More structure, more control
What does good governance deliver?
See immediately where data falls outside the rules
Quality rules make errors and missing information visible.
Always know who needs to act
Roles and workflows route every deviation to the right person.
Sound familiar?
Without clear agreements you lose control.
More products, teams and channels quickly lead to different versions, rules and ways of working.
No one knows what's authoritative
The same product information sits in multiple files and systems.
Good enough means something different to everyone
Without fixed quality rules, definitions of “complete” and “correct” differ between teams and channels.
Deviations are left unresolved
If no one owns it, no one knows who needs to act.
You're relying on gut feeling
Without insight into quality, you don't know where things go wrong.
How we approach it
Governance built into the process.
Define what good looks like
Make definitions, sources and requirements concrete for every category.
- Data model per category
- Source per field
- Fixed value lists
Make agreements enforceable
Translate quality agreements into checks, roles and workflows in the platform.
- Validation rules
- Roles and permissions
- Approval flows
See where action is needed
Make quality and deviations visible per product, team and channel.
- Quality scores
- Exception lists
- Reporting by owner
How customers stay in control of their product data.
These organisations make agreements, quality and ownership part of their daily product data flow.
Managing over 1 million technical products from one clear, reliable structure.
- 1M+ items
- Complex technical data
Organising product information consistently so teams and channels work from the same base.
- One clear structure
- Multiple teams and channels
Monitoring quality and agreements centrally across a large assortment and multiple brands.
- 2.5M+ SKUs
- Governance at scale
Importing supplier data automatically and publishing from one central source to the webshop and other channels.
- Automated imports
- One central source
Structuring technical product data and specifications and making them consistently available for all channels.
- Technical specifications
- One central structure
Technical architecture
How data quality is built into the process.
01 · Define
Define what good looks like
Establish fields, values, definitions and authoritative sources.
Technical
- Data dictionary
- Taxonomy
- Source ownership
AI bonus
- Recognises overlapping or conflicting definitions
02 · Assign
Make ownership explicit
Link data, categories and process steps to the right roles.
Technical
- Roles
- Permissions
- Data scopes
AI bonus
- Suggests suitable owners
03 · Validate
Check automatically
Continuously test product data against fixed quality rules.
Technical
- Required fields
- Value rules
- Dependencies
AI bonus
- Flags deviations that fixed rules may miss
04 · Route
Send deviations to the right person
Ensure errors and exceptions land in the right place straight away.
Technical
- Workflows
- Task queues
- Approvals
AI bonus
- Prioritises deviations by impact
05 · Approve
Make decisions traceable
Record changes, checks and approvals.
Technical
- Audit trail
- Versioning
- Status model
AI bonus
- Summarises changes for review
06 · Measure
Make quality visible
Measure completeness, accuracy and progress per product, category and channel.
Technical
- Quality scores
- Dashboards
- SLA tracking
AI bonus
- Recognises patterns in declining data quality
07 · Improve
Improve at the source
Use insights to continuously improve rules, sources and processes.
Technical
- Root cause
- Rule updates
- Supplier feedback
AI bonus
- Suggests new rules and improvements
08 · Let’s talk
What does this look like for your product data?
We look at where more structure, clear ownership and automation can improve control straight away.
- 30 minutes, straight to the point
- No standard demo
- Focused on your situation
Where are you losing control of product data?
We show where definitions, quality and ownership diverge, and where to intervene first.
- See where control is being lost
- Discover what needs attention
- Know what to improve first
Frequently asked questions.
Can't find your question? Get in touch
Product data governance means defining which definitions, rules, sources and responsibilities are authoritative. This makes clear what good product data is, who's responsible for what and how deviations get resolved.
Yes. We build on your existing structure and look at where definitions, value lists, sources or rules can be sharpened. So you don't have to start from scratch. See how this works in a demo.
By making agreements part of the process. Validations, roles, permissions and workflows ensure checks don't depend on separate spreadsheets or manual agreements.
Yes. You can start with one category, team or quality question and expand from there. That way, governance becomes part of the daily product data flow step by step. Start with the Product Data Scan.
With fixed quality rules and scores for completeness, accuracy and exceptions. This shows you where data falls outside the rules and where action is needed. See how to gain more control over product data.
Yes. Especially when multiple teams, brands or channels work with the same product data, governance helps everyone work from one set of definitions and rules.