Data architecture defines how an organization’s data assets are structured, integrated, governed, and accessed to support business operations, analytics, and strategic decision-making across the enterprise.
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A data architecture strategy translates business ambitions for data-driven operations into a coherent plan covering target models, platforms, governance, and phased implementation aligned with enterprise priorities.
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A data domain is a bounded area of enterprise data governed by shared definitions, ownership, and quality standards. Data domains organize the information landscape so architects, stewards, and product teams can scale governance without treating every table as a unique problem.
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Enterprise Architecture defines the structural context for data—domains, flows, platforms, and standards—while Data Governance defines ownership, policy, and quality accountability. Mature organizations integrate both so architecture decisions are enforceable and governance policies are implementable.
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Data maturity assessment measures how well an organization governs, models, integrates, and uses information relative to its ambitions. Structured assessments reveal gaps across domains, capabilities, and culture—not just tools—and inform prioritized roadmaps.
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