Enabling Big Data and Machine Learning Applications in High-Stakes Environments

High-Stakes Environments, such as industrial settings and natural disaster relief operations, are pivotal areas where decision-making speed and accuracy critically influence safety, reliability, and success. Within these critical contexts, the management of Data and Machine Learning (ML) models lifecycles is essential. Converting immense volumes of data into actionable insights, thereby enhancing the effectiveness of decision-making processes and ensuring more reliable outcomes. Data management ensures the meticulous organization of extensive datasets, facilitating their use for deriving meaningful information. ML enhances this process by employing algorithms capable of learning from data, thereby automating intricate decision-making tasks. These technologies enable real-time analysis that adjusts to evolving conditions, significantly automating complex decision-making. Nevertheless, implementing ML necessitates capabilities for real-time data acquisition, processing, storage, and adaptive training of ML models to meet immediate operational demands, thereby generating actionable knowledge for swift, efficient, and effective decisions. This paper discusses the advancements in my PhD journey, detailing the research methodology, challenges, and opportunities to improve the data-driven service management in these demanding environments.