The five stages of data management are as follows: Data Collection: This stage involves gathering data from various sources, such as databases, sensors, applications, or external entities. It includes capturing and recording data in its raw form, ensuring that it is accurate, complete, and relevant to the desired objectives. Data Storage: Once collected, data needs to be stored in a structured and secure manner. This stage involves selecting appropriate storage technologies and platforms, such as databases, data warehouses, or cloud storage systems. The goal is to ensure efficient data retrieval, scalability, and data protection. Data Processing: In this stage, collected data is transformed, cleansed, and organized to make it usable and valuable. Data processing may involve tasks such as data cleansing, validation, aggregation, integration, and enrichment. The aim is to improve data quality, eliminate inconsistencies, and prepare the data for analysis or other downstream processes. Data Analysis: Once data is processed, it can be analyzed to extract insights, patterns, and meaningful information. Data analysis techniques may include data mining, statistical analysis, machine learning, or other analytical approaches. The objective is to uncover trends, make predictions, identify relationships, and derive actionable insights from the data. Data Governance: Data governance is the stage where policies, standards, and processes are established to manage and control data effectively. It involves defining data ownership, roles, and responsibilities, establishing data quality rules, ensuring compliance with regulations and privacy requirements, and implementing data security measures. Data governance ensures that data is managed in a consistent, secure, and compliant manner throughout its lifecycle.