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����vlog�ٷ���� is entering a new paradigm of automation and data-driven discovery. Automated discovery is grounded in well-curated “big data.” As generative and predictive models fueled by simulation data see growing success, emerging robotic automation enables the generation of unprecedented volumes of experimental data. Automation-powered, data-driven approaches hold tremendous potential for groundbreaking insights and innovations, particularly in the study and discovery of electroactive π-conjugated molecules. Realizing this potential, however, requires democratizing chemical data and the automation needed to generate and use it. There is a need to expand access to the tools for findable, accessible, interoperable, and reusable (FAIR) data management and experimental automation. This dissertation contends that efficient discovery in the realm of electroactive π-conjugated molecules requires a coalition of automation and data-driven design with chemists and chemical intuition; this necessitates both large-scale FAIR data and intuitive man-machine interfaces. This dissertation investigates the automation of big-data generation, management, and analysis in the context of studying small electroactive π-conjugated molecules. First, this work examines the philosophical and historical foundations underpinning chemical data ontologies upon which automation and data-driven approaches depend. It advocates for interdisciplinary collaboration between philosophers and chemists to create more realistic, intuitive, and FAIR-compliant data structures. Then, this dissertation explores data generation and management in practice by producing computational data for over 40,000 electroactive molecules via automated high-throughput quantum chemical calculations and building a management infrastructure for the resulting data. It next demonstrates the insights gained through analyzing big data with a study of dihedral angle rotations in π-conjugated systems. The results demonstrate the ability of data-empowered machine learning (ML) to inexpensively automate the estimation of experiment-aligned for mesoscale properties. Likewise, it discusses how big data can be utilized for informing the selection of similarity measures, a key metric in many automated discovery applications. This work finally transitions to the automated generation of experimental data. It overviews a software developed for translating experimental protocols to robotic actions, validating the system by reproducing well-reported electrochemical experiments. Overall, this dissertation offers a path through effective organization, generation, management, and use of chemical data towards the automated study and discovery of electroactive π-conjugated molecules.