When does our organization need data governance?
Data governance becomes important when data quality issues keep recurring, multiple teams or systems manage the same data, or your organization grows across users, data domains, countries or entities. When implementing a new ERP, PIM or MDM solution, introducing governance from the start also helps prevent the new system from becoming polluted with inconsistent or unreliable data over time.
How can we structurally improve data quality?
A one-time clean-up does not address the root causes of poor data quality. Structural improvement requires clear quality rules, ownership, workflows and validations. By embedding these agreements into processes and systems, data quality can be monitored and maintained continuously.
What role does data governance play in compliance?
Good governance provides clarity on where data comes from, who is responsible for it and which quality rules are applied. This helps organizations maintain accurate, up-to-date and traceable data for areas such as traceability, product information, sustainability reporting and industry-specific regulations.
Why is data governance important for AI?
AI and agentic AI depend on reliable, up-to-date and well-structured data. Without governance, inconsistent or incomplete data can directly affect analyses, automation and decision-making. Data governance creates the reliable foundation that AI applications need to deliver trustworthy results.