Robust health data ecosystems require frameworks that clearly define how data flow through and across systems. Despite their widespread adoption in other sectors, data architecture frameworks remain underutilized in healthcare, limiting the sector’s ability to harness data for system improvement. This study presents the development of a data architecture framework designed to enhance health data ecosystems, illustrated through the use case of health workforce planning. To inform the development of the framework, we conducted an environmental scan to identify leading practices, principles, standards, guidelines, existing frameworks, and persistent challenges in health workforce data and planning. Using an iterative development process, we created a comprehensive description and visual representation of the framework. We then assessed its applicability to the Canadian context, implementation considerations, potential remediations, and added value to health systems. The resulting data architecture framework features four fundamental components: data ingestion, integration, storage, and fit-for-purpose use. The framework directly addresses key data challenges, including lack of comprehensiveness, granularity, standardization, interoperability, timeliness, usability, and accessibility. The assessment of the applicability of the framework to the Canadian context revealed that added value stems primarily from its promotion of comprehensiveness, interoperability, compatibility with artificial intelligence and accessibility of data to decision-makers. This data architecture framework addresses persistent challenges in health workforce data by enabling more consistent, timely, and actionable insights. It provides policymakers, planners, and researchers with fit-for-purpose data to improve resource allocation, forecasting, and innovation. Ultimately, it establishes health data ecosystems that support evidence-informed decision-making, workforce optimization, and sustainable health care delivery.