An Integrated Planning and Control Framework (IPCF) for Construction Projects—Step 1: Development of the Construction Data Hub (CDH)

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.