GlobalTech Solutions, a multinational corporation operating across diverse sectors including finance, healthcare, and manufacturing, is grappling with inconsistent data quality across its various divisions. The company aims to implement a data quality management system aligned with ISO 8000-110:2021 to ensure data accuracy, consistency, and reliability across all its operations. Different approaches are proposed by the senior management team:\n\nApproach 1: Implement data quality checks only when data inconsistencies are reported by end-users or during critical business processes, focusing on reactive measures to address immediate data quality issues.\n\nApproach 2: Establish a centralized data quality team responsible for defining data quality standards and conducting periodic data audits, without integrating data quality responsibilities into the roles of data creators and users within each division.\n\nApproach 3: Integrate data quality management into the existing data governance framework, defining clear roles and responsibilities for data quality across all divisions, establishing data quality metrics, and continuously monitoring and improving data quality based on these metrics.\n\nApproach 4: Focus primarily on investing in advanced data cleansing tools and technologies, automating data cleansing processes without establishing clear data quality policies, procedures, or training programs for employees.\n\nWhich of the proposed approaches is most aligned with the principles and requirements of ISO 8000-110:2021 for data quality management?