Data visualization and analytics (DV/A) facilitate the extraction of insight from raw data. This study presents the arguments on data-driven construction management and highlights a gap in the use of DV/A for managing construction portfolios at a large scale. Given the complexity involved in contracting and subcontracting strategies, managing the supply and allocation of resources across a portfolio of projects, the research objective is to create a DV/A environment that facilitates understanding the intra-portfolio risks and triggers optimized solutions. Accordingly, the K-means clustering method, in integration with the genetic algorithm, is employed in the data analytic part, and the interactive dashboard facilitates the geospatial visualization. The model feasibility is tested on Wellington City Council’s $712 million 10-year water-related project pipeline (the years 2021 to 2031), revealing potential overloads among assumed builder contractors in 2028, and suggested strategies to mitigate capacity shortfalls, with the need for further validation acknowledged. The outcome contributes to the construction sector’s capacity and capability evaluation and portfolio-level solutions.