Data curation is the process of creating, organizing, managing, and maintaining data or datasets to ensure their quality, usability, and relevance. It involves various processes such as source data collection, error identification and cleaning, data conversion for analysis, and archiving for long-term access. Data curation plays a crucial role in unlocking data's full potential, transforming raw data into valuable assets that drive success and efficiency, and preventing organizations from making flawed business decisions. The importance of data curation lies in its ability to improve data usability, enhance decision-making, increase reusability, ensure security, facilitate collaboration, and promote compliance with legal standards. Data curation requires a robust framework for managing data, including components such as data collection, organization, validation, storage, and sharing. It also involves the use of tools and technologies like automation, AI, and data curation platforms to streamline the process. However, data curation poses several challenges, including managing heterogeneous datasets, balancing privacy and accessibility, dealing with large-scale data volumes, and ensuring best practices such as clear objectives, data quality, organization, metadata management, storage, sharing, governance, and data reuse. In different domains like scientific research, business intelligence, and academic libraries, data curation approaches vary to suit the specific nature of each domain's objectives. Ultimately, data curation is essential for organizations to improve the quality, accessibility, and usability of their data assets and make informed business decisions.