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IT项目经理应该如何推动数据治理项目?
3 6 Ke·2025-11-24 03:43

Core Insights - Data governance is often viewed as an ancillary project rather than a core component of data initiatives, leading to questions about its actual value in the short to medium term [1] - The shift from centralized management to a more decentralized approach in data governance has been driven by advancements in low-cost storage, cloud computing, and artificial intelligence [2] - The need for seamless access to accurate data through various means has led to the rapid adoption of data mesh architecture, which emphasizes business ownership over data rather than IT [2] - Successful data governance now requires a shift in strategy for technical project managers, focusing on business initiatives and mapping them to data products [2][3] - The integration of data governance with business objectives is essential for achieving effective outcomes and realizing short-term benefits [3][4] Summary by Sections - Data Governance Perception: Traditionally seen as a supplementary function, data governance lacks immediate value perception, leading to its relegation in overall data strategy [1] - Evolution of Data Governance: The last decade has seen a transition to decentralized data governance models, driven by technological advancements and the need for diverse data access [2] - Decentralized Data Mesh: The data mesh architecture promotes business department ownership of data, enhancing understanding, traceability, and interoperability [2] - Strategic Shift for Implementation: Technical project managers are encouraged to adopt a right-to-left approach, prioritizing business stakeholder involvement in data governance initiatives [2][3] - Business Alignment: Effective data governance must align closely with business goals to be successful, moving away from fragmented approaches [3][4] - Implementation Roadmap: A viable roadmap for building a business-oriented decentralized data governance framework is suggested [5][6] - Frequent Value Realization: The focus is on achieving business value and data governance goals frequently in the short term, contrasting with traditional methods that aim for one-time achievements [8]