###### Abstract  
Construction projects generate a significant volume of scattered data in various formats. However, having a large amount of data is insufficient; there is a need to obtain the appropriate metadata to enable extracting useful knowledge from it. Therefore, professionals need a consistent data acquisition model to gather comprehensive data from multiple projects and organizations in a format ready for applying machine learning. This research proposes an Integrated Planning and Control Framework (IPCF) to implement the concept of \"From Data to Decision (FD2D)\" in the construction industry. The first step of the framework is the development of the Construction Data Hub (CDH). The CDH seeks to collect data from twelve dimensions that impact the project's planning and control. It relies on using the industry-accepted concept of work packages, which is the optimum level of detail for data acquisition. To validate the CDH, a machine learning model that utilizes the data collected through the CDH is developed to analyze the factors influencing construction project profit. The study revealed six significant profit-influencing factors. These factors might assist estimators in predicting profit margins during the early estimation stage, instead of relying on intuition or uniform rates, which are not always reliable methods. data acquisition model; data warehouse; data management; from data to decision (FD2D); work breakdown structure (WBS); product breakdown structure (PBS); knowledge management; construction management Article  
Construction Engineering and Management, Department of Civil and Environmental Engineering,  
University of Alberta, Edmonton, AB T6G 1H9, Canada; [EMAIL_ADDRESS]  
15 April 202520 April 20  
monitoring to ensure the timely completion of projects [6]. However, during the early stages of the project, the amount of detailed available data related to the project is limited. This makes preparing accurate schedules and cost estimates nearly impossible and adversely affects resource management, production rates, execution strategies, and overall decision-making. Collecting large amounts of high-quality data that fully represent the real construction world has significant limitations [7]. Currently, the significant challenges facing machine learning (ML) are insufficient training data, unstructured data, low-quality data, and training data with irrelevant features, which lead to underfitting and overfitting [8; 9; 10]. Similarly, ref. [11] specifically highlighted the issue of the ineffective utilization of machine learning algorithms resulting from proper data limitations and information constraints. Various studies have used metaheuristics algorithms such as genetic algorithms (GA) to solve scheduling problems that arise from limited data--some even developing complex models to address real requirements, such as considering multiple projects [12], changing resource constraints [13], and simultaneously considering resource-constrained and time-cost trade-off problems [14]. However, [PERSON] et al. [15] identify the difficulty of data collection as a significant flaw in these studies; therefore, it is difficult to apply these methods to practical projects. Accordingly, ML and heuristic models should consider two major issues: the first is a structured data source to provide the time, cost, resources, greenhouse gas (GHG) emissions, etc., for project planning; the second is a suitable transfer method to move the data from the data source to the mathematical model such as BIM. [PERSON] and [PERSON] [16] highlighted that, as a field of computer science, AI enables computers to perceive and learn input much like humans do. It encompasses knowledge representation, reasoning, problem-solving, and planning to address complex and ambiguous problems in a deliberate, intelligent, and adaptive manner. Investment in AI is experiencing rapid growth, with machine learning playing a significant role in exploring robust data from multiple sources and leveraging insights for informed decision-making.