To address conflicting data interpretations, standardize definitions with a shared glossary and data dictionary to ensure consistent terminology and metrics. Implement a single source of truth using centralized platforms like data warehouses or governance tools for accurate access. Facilitate discussions or workshops to resolve discrepancies and align on data use. Apply governance policies to enforce consistency and use conflict resolution frameworks to prioritize disagreements. Address urgent conflicts with escalation mechanisms tied to timelines. Conduct regular audits, provide targeted training, and showcase the value of aligned interpretations through efficiency gains and better decision-making.

To resolve conflicting interpretations among teams, organizations should use experimentation and A/B testing for data-driven decision-making. Promoting data literacy through training programs will ensure all team members understand key concepts, reducing confusion. Implementing version control for data will help maintain consistency, allowing everyone to work with the same dataset. Utilizing versioning tools enables teams to track changes and understand data evolution more effectively. This comprehensive approach fosters clarity and collaboration within the organization.

The implementation of data quality practices, including source validation, helps the team identify the true source of data. The adoption of standards and types of treatments must be carried out after validating the true source of data so that there is no rework.