Operational data is the backbone of daily business activities, capturing live transactions, system logs, customer interactions, and supply chain movements, providing instant visibility for quick decision-making and business continuity. It differs from analytical data in that it's real-time, transactional, and continuously updated, whereas analytical data is used for long-term strategy and decision-making. Operational data is essential for businesses to function efficiently and respond to real-time challenges, enabling real-time decision-making, enhancing automation and efficiency, strengthening security and fraud detection, improving customer experience, and ensuring compliance and risk management. It comes from various systems and processes within an organization, including transactional databases, IoT devices and sensor data, IT system logs and event data, customer interaction data, supply chain and logistics data, and financial transactions and banking systems. Managing operational data involves several challenges, such as managing large data volumes, dealing with data silos and lack of integration, ensuring data quality and accuracy, real-time data processing and latency issues, and security and compliance risks. To effectively manage operational data, businesses should implement real-time data processing, integrate data across systems, strengthen data governance and security, utilize AI and machine learning for anomaly detection, optimize storage for cost-effective scaling, enable automated data quality management, and maximize operational data efficiency with solutions like Acceldata.