Point clouds have proven over the years to be a suitable spatial representation of scenes and objects at varying scales and levels of complexity, making them widely used across several scientific domains and applications. Advancements in sensor technology, computer vision, and data science have produced high-resolution point clouds and advanced analytical approaches, leading to broader adoption for spatial information extraction to support decision making. However, traditional point cloud management systems for organizing and distributing data throughout the point cloud lifecycle often create significant duplication at each stage. This causes data fragmentation as multiple copies and versions are scattered across different processing steps, workgroups, and storage locations, further limiting cross-domain applications. In this paper, we propose a unified point cloud data management (PCDM) approach that supports the principles of findability, accessibility, interoperability, and reusability (FAIR) across domains at scale. The proposed approach aims to support diverse point cloud retrieval for cross-domain analysis by leveraging a single, reusable PCDM system built on a shared data model. Our approach improves on existing frameworks and provides a foundation for point cloud data management and data spaces.