The overarching objective of the research included in this dissertation was to develop and evaluate several novel data-driven tools and management strategies aimed at improving the health and reproductive performance and management of dairy cattle through automation or semi-automation of management tasks and targeted interventions driven by dairy cow and herd data. The objectives of the studies presented in Chapter II and II were to characterize the patterns of rumination time, physical activity, and lying time monitored by an automated health monitoring system based on an ear-attached sensor, immediately before, during, and after clinical diagnosis of metabolic-digestive disorders (Chapter II), and metritis and mastitis (Chapter III). The objectives of the randomized control trial presented in Chapter IV were to evaluate the effects on herd outcomes and performance of a health monitoring program that relied only on automated monitoring systems (AMS) alerts for selecting cows for clinical examination in early lactation. In Chapter V, the total cash flow per cow and per slot for 100 d after calving for cows under the health monitoring strategies tested in the experiment presented in chapter IV were compared. Chapter VI presents the results of a randomized controlled trial that tested the feasibility and effects on herd reproductive performance of a targeted reproductive management program to optimize submission to first service using treatments tailored to cows with or without estrus alerts during the voluntary waiting period. Chapter VII includes a study that explored the value of combining automated estrus alerts data with other predictors of reproductive performance collected during the voluntary waiting period for creating groups of cows with larger differences in reproductive performance than created by these factors alone. Finally, Chapter VIII includes work on the development and validation of an electronic system based on a lateral flow assay strip and a portable optical device for cattle pregnancy testing. Collectively, this body of work demonstrated the value of several data-driven tools and strategies for better understanding, monitoring, and managing dairy herd health and reproduction.