Point cloud analysis is rapidly evolving, targeting new applications and use cases with novel information retrieval needs that challenge existing solutions' scalability, robustness, and reusability to manage and process point cloud data. Analytical approaches to gain insights are increasingly based on machine learning and tend to turn away from data management solutions in favour of internalizing custom, dedicated workflow-specific query capabilities, satisfying their requirements. Unfortunately, these ad-hoc solutions often fail to scale well with big point cloud datasets, such as terrestrial laser scanning. To address these limitations, we propose a point cloud search taxonomy and use it to identify fundamental requirements for a scalable, robust, and reusable data management system for state-of-the-art point cloud retrieval and data analytics. Our findings build a foundational analysis serving as a basis for future holistic development of point cloud data management solutions to overcome current bottlenecks.