What does data governance actually mean?
Data governance encompasses the strategic and operational management of data within an organization. The aim is to ensure the quality, security and consistency of data in the long term - across departmental, system and process boundaries.
Typical elements of a data governance strategy:
- Roles & responsibilities (e.g. data owner, data steward)
- Data quality standards and checking mechanisms
- Guidelines for the use, storage and transfer of data
- Processes for classification, approval and versioning
- Tool-supported automation and documentation
This strategic control is more important today than ever before - because companies are facing a multitude of data-related challenges.
Why data governance is indispensable today
1. increasing complexity
With increasing channel diversity, new touchpoints and individual customer requirements, the demands on product data maintenance are also growing. Without governance, this complexity quickly becomes unmanageable.
2. data quality as a success factor
Missing or incorrect product information not only leads to internal friction, but also to a loss of trust on the customer side. Governance helps to systematically identify and close quality gaps.
3 Regulatory requirements
Whether GDPR, supply chain law or industry-specific standards - data must be managed in a traceable, correct and legally compliant manner. Governance provides a resilient framework here.
4. enable scalability
Growing product portfolios and international markets require scalable data processes. A good governance architecture forms the backbone of this scalability.
All of these requirements - from increasing complexity and quality demands to regulatory pressure - cannot be solved by individual measures. What is needed is a systematic approach: a governance framework that provides orientation, secures processes and makes responsibilities transparent.
The governance process: from rules and regulations to lived practice
Strategic goal definition
What should data governance achieve? What risks should be addressed and what opportunities should be exploited?
Roles & responsibilities
Clear responsibilities - for example through the introduction of data owners or stewards - make governance manageable in day-to-day business.
Establish rules & standards
Which fields are mandatory? Which classifications are mandatory? What does "complete" mean in which context?
Technical implementation & tool support
Data guidelines should not only be documented, but also systematically implemented and automatically checked - e.g. via a PIM or a dedicated data governance tool.
Training & change management
Data governance is not an IT project, but a cultural change. Successful implementation requires broad involvement and communication.
A data governance framework creates the necessary regulatory framework: It defines how data is handled - who is responsible, what quality requirements apply and how processes are safeguarded. Practical guidelines are needed to ensure that this framework not only exists on paper, but is actually anchored in the company.
5 expert tips for a functioning data governance framework
1. start with a clear vision.
Governance is not an end in itself. Whether it's higher data quality, better time-to-market or compliance - define measurable goals before you invest in roles and processes.
2. keep the framework lean and practical.
A governance model must work on a day-to-day basis. Avoid unnecessary complexity and focus on the most important core processes and responsibilities.
3. separate roles - create responsibility.
Avoid overlaps. A clearly defined interaction between data owner, data steward and operational teams ensures transparency and acceptance.
4. think governance along with the system.
A PIM system alone does not make governance. But it can technically map rules, review processes and approvals - if the framework is properly integrated.
5. governance needs communication.
Rules are only effective if they are understood and accepted. Training, guidelines and a central governance manual help with change management.
Data governance and product communication - a strong duo
The value of governance is particularly evident in product data communication:
If you want to control, enrich and distribute your product information in a targeted manner - for example in the direction of marketplaces, customer catalogs or B2B portals - you need consistent, trustworthy data as a basis.
A clean governance structure is therefore also an enabler for data syndication, PIM projects and customer experience management.
Conclusion: More structure, more quality, more trust
Data governance only unfolds its benefits through consistent implementation: it creates a reliable basis for decision-making, reduces operational costs and strengthens the reliability of the database - both within the company and in external processes. This lays the foundation for an efficient, scalable and future-proof data strategy.