The text discusses the challenges of managing complex data architectures in organizations, particularly those relying on outdated systems. Operational Data Stores (ODS) have emerged as a solution to tackle these complexities by providing a centralized and organized way to store and manage enterprise data. An ODS serves as a secondary data store, holding data replicated from primary transactional systems, allowing organizations to operate on a more unified dataset. The adoption of an ODS typically follows an incremental approach, focusing on extracting data from one system into the Operational Data Store, gradually retiring legacy systems and eliminating intermediate data streams. This approach delivers value to the business by minimizing disruption and providing a complete overhaul. ODS are used to support businesses in three different ways: Data Access Layers, Operational Data Layer, and Developer ODL. Search capabilities, such as those provided by MongoDB Atlas Search, play a crucial role in maximizing the value of an ODS by empowering users to explore, analyze, and gain valuable insights from their data. By leveraging search functionality, organizations can streamline data retrieval, accelerate development cycles, and reduce developer training time, ultimately leading to improved data models and better system performance. The power of search within ODS lies in its ability to simplify data interaction, saving time and enhancing efficiency. Organizations that adopt an incremental approach and leverage search capabilities can unlock the true potential of their data, paving the way for a more productive and data-driven future.