## 6 Case Study
### Data Investigation and Analysis  
The original data set obtained from the organization consisted of 2018 construction projects from disciplines such as residential, commercial and institutional, infrastructure, and industrial. It included projects executed between 2004 and 2024. Only projects with 100% execution percentages were selected to be included in the research data set, while other projects with execution percentages less than 100% were discarded. Although the organization invested a lot of hours collecting data from different data sources and project management software, it was found that the collected data were still not ready for the data mining process. [PERSON] and [PERSON] [51] highlighted the reason behind this issue: the absence of clearly defined, automated mechanisms to extract, process, analyze data, and summarize results for construction managers.  
The original data set included 186 fields. Hence, the authors performed data cleaning and further investigation processes for the data set, which revealed some main issues such as data duplications and combining many fields. These issues were mainly due to the way data were collected, as some information was in MS Excel sheets, which was different from the information collected in other management software, leading to low data quality and analytic suitability. For instance, some fields combined significant information such as project delivery methods (PDM), project financing models (PFM), and contract types in one field. To avoid any future data collection issues, the authors created a data dictionary to facilitate the data collection process and serve as a foundation for required analytics data. Further, a data gap analysis was performed to determine the missing fields and information. As a result, the data were organized based on seven categories: organization breakdown structure (OBS), project details, stakeholders, location, budget, cost and profit, and duration.  
Although the authors could not collect data at the work package level as proposed earlier in their methodology, employing the proposed CDH idea at the project level proved its applicability. The authors collected the appropriate metadata from different structures and dimensions and then arranged and categorized the fields. They collected, cleaned, and organized construction projects' metadata under the two structures: the OBS and RBS. The metadata related to the other two structures, WBS and PBS, were not available in the obtained data set. Also, metadata from six dimensions out of the proposed twelve dimensions was obtained. The available dimensions are scope management, planning and scheduling, cost estimating, cost control, labor resources, and document management (Table 3). The authors integrated the metadata from the two structures and six dimensions in one data sheet and used it in their analysis. To avoid redundancy, the metadata fields were re-evaluated; for instance, the authors calculated the \"approved change orders %\" and removed the field of \"approved change orders\". They also used the fields \"baseline start date\", \"baseline finish date\", \"actual start date\", and \"actual finish date\" to calculate the new fields called the \"planned duration in days\" and \"actual duration in days\" and then discarded the dates fields. Other fields, such as \"project financing model (PFM)\" and \"postal/zip code\", were discarded due to missing values. Thus, the resulting data set included 507 projects with 28 significant fields as profit influential factors, which will be utilized later as inputs to the machine learning model.