Following a major merger, three independent pharmaceutical research companies – \"AlphaBiotech,\" \"CureAll Pharma,\" and \"MediGenix Labs\" – have been integrated into a single global entity, \"Global Pharma Innovations\" (GPI). Each company historically operated with its own independent IT infrastructure, data management practices, and regulatory compliance standards. AlphaBiotech\'s research data is stored in a relational database with stringent validation rules but suffers from inconsistent naming conventions across different research groups. CureAll Pharma utilizes a NoSQL database for its clinical trial data, prioritizing speed of data entry over strict data validation, resulting in completeness issues. MediGenix Labs has a well-defined data warehouse with comprehensive metadata but lacks real-time data integration capabilities, leading to timeliness concerns. The newly formed GPI is facing significant challenges in consolidating research data, ensuring data consistency across different functional areas, and complying with global regulatory requirements. Which of the following approaches would be MOST effective in addressing the data quality challenges resulting from this merger and establishing a sustainable data quality management system for GPI?