The fundamental purpose of drinking water distribution systems is to provide safe drinking water at sufficient volumes and optimal pressure with the lowest lifecycle costs from the source (treatment plants, raw water source) to the customers (residences, industries). Most of the distribution systems in the US were laid out during the development phase after World War II. As the drinking water infrastructure is aging, water utilities are battling the increasing break rates in their water distribution system and struggling to bear the associated economic costs. However, with the growth in sensory technologies and data science, water utilities are seeing economic value in collecting data and analyzing it to monitor and predict the performance of their distribution systems. Many mathematical models have been developed to guide repair and rehabilitation decisions in the past but remain largely unused because of low reliability. This is because any effort to build a decision support framework based on a model should rest its foundations on a robust knowledge base of the critical factors influencing the system, which varies from utility to utility. Mathematical models built on a strong understanding of the theory, current practices and the trends in data can prove to be more reliable. This study presents a framework to support repair and rehabilitation decisions for water utilities using water pipeline field performance data.