Data Governance
Data governance is the set of rules, roles and checks that decides who owns which data, how it is created and changed, and what quality it must meet.
What is Data Governance?
Data governance is the set of rules, roles and checks that decides who owns which data, how it may be created and changed, and what quality it must meet before anyone uses it. For product data that means named owners per attribute group, required fields per category, naming and unit conventions, and rules for when a product is complete enough to publish. Data governance is the policy layer; data management is the daily work of importing, editing and exporting that follows the policy. The data governance guide shows how to set up owners, rules and checks for a product catalog.
Why Data Governance matters for e-commerce
In e-commerce many people and systems touch the same product: buyers, content editors, the ERP, suppliers and channel feeds. Without clear ownership the same field gets overwritten, units mix, and nobody knows which value is right. Those errors reach product pages and marketplaces. Data governance makes product data predictable. Everyone knows who decides on a value, every category has its required fields, and incomplete products are caught before they go live. Shoppers notice the result: Akeneo's 2025 Consumer Returns Report found that about 60% of shoppers had returned a product because of inaccurate or misleading descriptions.
Examples of Data Governance
- 1Rule: Every product in Shoes needs size, color and material, Owner: category manager, Check: before publish
- 2Rule: Prices come only from the ERP, Owner: finance, Check: manual price edits are not allowed
- 3Rule: Weights are stored in grams, Owner: product data team, Check: import mapping converts units
- 4Rule: Descriptions follow the brand style guide, Owner: content lead, Check: sample review each week
How WISEPIM Helps
- Quality Guard rules check required fields and formats per category and flag products that break them.
- Attribute management defines which attributes exist, their type and their allowed values.
- Operation history undo on paid plans lets you reverse a bulk change that went wrong.
- Review queue collects AI-generated changes so a person can check them before they are kept.
Common mistakes with Data Governance
- Writing a governance document that nobody checks in the daily tools.
- Leaving ownership vague, so every team edits every field.
- Defining rules for the whole catalog instead of per category.
- Allowing the same data to be edited in several systems.
- Starting with too many rules at once instead of the fields that matter most.
Tips for Data Governance
- Name one owner per attribute group, not per product.
- Pick one system as the source for each type of data, such as the ERP for price and stock.
- Write required fields per category and check them automatically.
- Start with your best-selling category and expand from there.
- Review the rules every quarter and remove the ones nobody needs.
Trends around Data Governance
- Rules checked automatically in the tool instead of written in a policy document.
- AI-generated content that needs clear rules for review before it is published.
- Regulation such as the EU Ecodesign Regulation adding required product fields.
- AI shopping assistants that reward complete, consistent attributes.
- Smaller teams adopting light governance with a few owners and clear rules.
Tools for Data Governance
- WISEPIM (Quality Guard rules, attribute management)
- Collibra
- Informatica
- Akeneo
- Salsify
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