This paper delves into the challenges encountered in decision-making processes within Hybrid Energy Systems (HES), placing a particular emphasis on the critical aspect of data integration. Decision-making processes in HES are inherently complex due to the diverse range of tasks involved in their management. We argue that to overcome these challenges, it is imperative to possess a comprehensive understanding of the HES architecture and how different processes and interaction layers synergistically operate to achieve the desired outcomes. These decision-making processes encompass a wealth of information and insights pertaining to the operation and performance of HES. Furthermore, these processes encompass systems for planning and management that facilitate decisions by providing a centralized platform for data collection, storage, and analysis. The success of HES largely hinges upon its capacity to receive and integrate various types of information. This includes real-time data on energy demand and supply, weather data, performance data derived from different system components, and historical data, all of which contribute to informed decision-making. The ability to accurately integrate and fuse this diverse range of data sources empowers HES to make intelligent decisions and accurate predictions. Consequently, this data integration capability allows HES to provide a multitude of services to customers. These services include valuable recommendations on demand response strategies, energy usage optimization, energy storage utilization, and much more. By leveraging the integrated data effectively, HES can deliver customized and tailored services to meet the specific needs and preferences of its customers.