Utilizing Big Data Analytics for Predictive Maintenance in Real Estate Management

Thesis Abstract

Abstract
The real estate industry is constantly evolving, with a growing emphasis on leveraging technological advancements to enhance operational efficiency and asset management. This thesis explores the application of big data analytics for predictive maintenance in real estate management, aiming to optimize maintenance processes, reduce operational costs, and minimize downtime. The research delves into the significance of predictive maintenance in the context of real estate management, highlighting the potential benefits and challenges associated with its implementation. The first chapter provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. Chapter two conducts a comprehensive literature review, examining existing studies on big data analytics, predictive maintenance, and their relevance to real estate management. The review encompasses ten key areas, including data collection methods, predictive modeling techniques, and best practices in predictive maintenance. Chapter three outlines the research methodology, detailing the approach taken to collect and analyze data for this study. The methodology includes data collection methods, data analysis techniques, sample selection criteria, and research instruments employed. Additionally, the chapter discusses the ethical considerations and limitations of the research methodology. Chapter four presents a detailed discussion of the research findings, including the outcomes of the data analysis and their implications for predictive maintenance in real estate management. The discussion addresses key themes such as predictive maintenance strategies, data-driven decision-making, and the integration of predictive analytics tools into existing maintenance workflows. Finally, chapter five offers a conclusion and summary of the thesis, highlighting the main findings, contributions to the field, and recommendations for future research. The conclusion underscores the potential of big data analytics for predictive maintenance in real estate management and proposes actionable insights for industry practitioners and policymakers. In conclusion, this thesis sheds light on the transformative potential of big data analytics for predictive maintenance in real estate management, offering valuable insights for industry professionals, researchers, and policymakers seeking to enhance asset performance, optimize maintenance operations, and drive sustainable growth in the real estate sector.