###### Abstract
They also discussed the current gap in the adoption of AI techniques within construction projects, as it still lags behind other industries despite the exponential increase in generated data. Consequently, there is substantial interest in implementing various AI methods in the AEC industry to utilize the valuable opportunity presented by digital evolution for improved performance and profitability. One of the primary applications of artificial intelligence lies in the domain of \"From Data to Decision\" (FD2D). This term focuses on digital transformation and artificial intelligence from the data value chain to human value [17]. It involves developing and studying digital transformation and artificial intelligence applications across various academic disciplines and industrial sectors, and their impact on society. FD2D is a framework designed to convert diverse raw data into actionable insights using organized workflows. In construction, FD2D consists of three key steps: First is data acquisition, which standardizes and integrates scattered project information such as schedules, budgets, resources, and embodied carbon emissions. Second, knowledge extraction involves using analytics tools like AI and machine learning to identify patterns. Lastly, decision support translates these insights into actionable operational strategies, focusing on areas such as risk mitigation and resource optimization. The proposed Integrated Planning and Control Framework (IPCF) operationalizes FD2D's first step by tackling the challenge of data fragmentation in construction. Specifically, IPCF's Construction Data Hub (CDH) standardizes data collection across 12 dimensions (Section 4), facilitating downstream FD2D processes such as predictive modeling (Section 6). Therefore, IPCF is fundamental to FD2D's objective of data-driven construction management. Building Information Modeling (BIM) is a significant advancement in architecture, engineering, and construction (AEC). BIM technology creates accurate digital models of buildings, enhancing design analysis and control over manual processes. These models in clude precise geometry and data to support construction, fabrication, and procurement [18]. BIM also models a building's lifecycle, enabling new design and construction capabilities and evolving project team roles. When effectively implemented, BIM leads to more integrated design and construction, resulting in higher quality buildings, reduced costs, and shorter project durations. BIM has emerged as a crucial tool for structuring and sharing project data among stakeholders, particularly in design and coordination [16]. However, BIM's focus on geometric and procedural data often neglects essential dimensions such as labor productivity, environmental impact, and social metrics, which remain scattered across disparate systems. The proposed CDH enhances BIM's capabilities by integrating these non-geometric dimensions into a unified framework, enabling comprehensive analysis. For instance, while BIM may track material quantities, the CDH associates this data with cost, carbon emissions, and supplier histories, bridging gaps in early-stage decision-making. This research focuses on resolving the first issue found in the previous models: collecting and structuring the data model. It employs the concept of FD2D in the construction industry to develop a data-driven decision-making tool called Integrated Planning and Control Framework (IPCF), by proposing a methodology (Figure 1) that transforms the significant volume of scattered construction data in various formats into valuable decisions. First, by collecting comprehensive data and appropriate metadata from projects during different construction project stages. Second, linking these data points to one or more construction project phases, then developing a data acquisition model called Construction Data Hub (CDH) to collect data from multiple projects and organizations in a format ready for applying machine learning and artificial intelligence techniques.