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StellarTech Corp, a multinational conglomerate operating across diverse sectors including aerospace, finance, and consumer electronics, is embarking on a comprehensive data quality initiative. Recognizing the critical role of data in strategic decision-making and operational efficiency, the executive leadership team has mandated the implementation of a standardized data quality framework across all business units. However, each business unit operates with distinct legacy systems, data governance maturity levels, and business priorities. The CFO champions centralized control to ensure consistent reporting and regulatory compliance. The CTO advocates for decentralized execution, arguing that each unit possesses unique data landscapes requiring tailored solutions. The Chief Data Officer (CDO) is tasked with formulating a data quality strategy that balances the need for organizational consistency with the operational realities of diverse business units.\n\nWhich of the following approaches would be most effective in addressing StellarTech\'s data quality challenges, considering the inherent tensions between centralized governance and decentralized implementation?
Establish a centralized data quality governance framework with clearly defined policies and standards, while empowering business units to implement these policies through tailored data quality management practices and technologies, subject to periodic audits and performance reporting against agreed-upon metrics.
Implement a completely decentralized data quality model, allowing each business unit to independently define and manage its own data quality standards, processes, and tools, with minimal oversight from the corporate data governance team, to foster agility and innovation.
Prioritize short-term data quality improvements by focusing on quick wins and easily achievable objectives within each business unit, deferring the development of a comprehensive data quality strategy and governance framework to a later phase of the initiative.
Enforce a rigid, centralized data quality management system across all business units, mandating the use of standardized data quality tools and processes, with strict compliance requirements and minimal flexibility for local adaptations, to ensure uniformity and control.

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