Establish clear data quality dimensions such as accuracy, completeness, consistency, timeliness, and uniqueness. These dimensions should be prioritized based on their impact on business goals.

Bring everyone together to discuss their perspectives. Encourage open communication and ensure all voices are heard. Review the project’s goals and identify the data quality requirements that best support those goals. Use industry standards or best practices as a reference to find common ground. If disagreements persist, prioritize the standards that align most with business needs and performance. Document the agreed standards clearly and provide training if needed. By focusing on collaboration and shared objectives, you can resolve conflicts constructively.

Mediating a database team clash over data quality starts with open dialogue. Facilitate a meeting where each side explains their concerns and reasoning. Align everyone on a shared goal—ensuring data accuracy, consistency, and reliability. Use industry standards as a neutral reference point. Propose a compromise by adopting best practices from both sides. Implement clear guidelines with documented procedures. Regular audits and automated validation can reduce friction. Encourage a culture where feedback improves processes, not fuels disputes. By focusing on collaboration and long-term solutions, you turn conflict into a productive discussion that strengthens data integrity.