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From Centralized to Federated: Evolving Data Governance Operating Model
- Data governance is a framework that ensures high-quality, trusted data is consistently accessible across an organization.
- A one-size-fits-all approach to data governance not only restricts scalability but also hampers the organization's ability to address unique challenges such as regulatory compliance, data quality, and cross-functional collaboration.
- There are three main data governance operating models that organizations can choose from, each with its own pros and cons: centralized, decentralized, and federated.
- Choosing the right data governance operating model requires a strategic approach that aligns with the organization's unique needs, maturity, and objectives.
- Selecting the right data governance operating model requires a strategic approach that aligns with the organization’s unique needs, maturity, and objectives.
- The ideal model must align with the organization’s data maturity, structure, and future goals.
- Too much control can stifle innovation, while too much flexibility can lead to inconsistencies, misalignments, and errors.
- The proposed blueprint for choosing the right data governance model for your enterprise includes: Assess the Current State, Define the Target State, Evaluate Model Suitability, Engage Stakeholders, and Pilot, Refine, and Scale.
- A real estate company successfully transitioned from a centralized to a federated data governance model. This shift empowered the organization to leverage its data more effectively, foster collaboration, streamline operations, and enable better decision-making.
- Shifting from centralized control to a decentralized, business-unit-driven governance framework better aligns governance with real business needs and positions data as a catalyst for innovation and growth.
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