In my experience, the first point is key to achieve a balance between data integrity and project compliance, led by a team that really understands data and their protocols to be able to enforce them and timely solve issues which, believe me, will happen. Perhaps even devote a resource(s) for this. Issue management in this area is key.

Start by defining clear data quality standards and focus on what's most critical to the project's success. Automate data validation where possible to save time, and keep stakeholders updated about any issues that could affect timelines. By addressing data quality throughout the project instead of just at the end, you can maintain integrity without delaying progress.

To ensure both data integrity and project timelines are met, I utilize a combination of automation and structured governance. Automated data quality checks are embedded into the pipeline through tools like Apache NiFi and dbt’s test capabilities, allowing real-time validation across datasets. Data governance frameworks are enforced using metadata management solutions, ensuring adherence to defined quality standards. Agile project management practices, such as sprint-based iterations, incorporate data validation checkpoints into each phase. Additionally, I employ dependency tracking tools like Airflow to manage ETL processes and ensure critical path visibility.