| Abstract |
vii Big Data Analytics (BDA) and Business Intelligence Systems (BIS) are pivotal for modern organizations, fueling data-driven insights that strengthen decision-making, operational efficiency, and strategic planning. These technologies enable businesses to harness vast and varied data sources, optimizing processes and maintaining competitiveness in today's data-centric environment. Their true worth lies in their capacity to transform raw data into actionable intelligence, steering business success. By offering decision-makers comprehensive, real-time insights, BDA and BIS foster informed, strategic choices, identifying market trends, customer preferences, and resource allocation. They streamline operations by automating tasks and enhancing efficiency while supporting long-term strategic planning through data analysis and risk assessment. Moreover, these technologies confer a competitive edge by facilitating personalized customer experiences and targeted marketing efforts. In conclusion, BDA and BIS are indispensable tools that convert data into an asset, empowering organizations to thrive in an increasingly complex and data-rich world. The thesis work is structured into five chapters, each serving a specific purpose in exploring the impact of Big Data Analytics and Business Intelligence Systems on decision-making within organizations: • Chapter 1: Introduction: In the first chapter, we provide an introduction to the research. This section discusses various Big Data technologies, outlines the functions of Business Intelligence systems, presents an overview of the research workflow, and explores the role of Big Data in Business Intelligence. Additionally, we delve into the diverse applications of Big Data and analytics in different contexts. • Chapter 2: Review of Literature: Chapter two is dedicated to the review of literature. Here, we comprehensively examine previous research related to Big Data Analytics and Business Intelligence systems. We investigate their impact on administrative effectiveness, decision-making processes, and identify key factors associated with technical, structural, and managerial aspects. This chapter also scrutinizes the influence of Big Data Analytics and Business Intelligence on productivity and overall organizational effectiveness. • Chapter 3: Research Methodology: In the third chapter, we detail the research methodology employed in this thesis. This includes an explanation of our research design, the sampling technique utilized, the rationale for selecting a specific sample size and population, an analysis of the existing research gaps, the hypotheses tested, and the statistical tools and techniques applied in our study. • Chapter 4: Interpretation, Results, and Discussion: Chapter four is dedicated to presenting the profile of the respondents, as well as the interpretation of results and subsequent discussions. In this section, we explore the impact of Big Data Analytics and Business Intelligence systems on productivity and overall organizational effectiveness based on the data collected. We analyze the findings and engage in a comprehensive discussion to draw meaningful insights. • Chapter 5: Findings, Recommendations, Conclusions, and Future Scope: The final chapter, Chapter five, encompasses the key findings from our research, followed by a set of practical recommendations based on those findings. We then draw conclusions based on the study's outcomes and outline potential avenues for future research in this field. In conclusion, the impact of Big Data Analytics and Business Intelligence Systems on decision-making within Indian companies is substantial, with promising prospects for the future. As technology continues to advance and data becomes more readily available, these tools are poised to play an increasingly central role in shaping the strategies and dayto-day operations of Indian businesses. This contribution is expected to enhance their growth and competitive edge in both domestic and global markets. However, it is crucial for companies to remain adaptable in the face of evolving technologies, adhere to ethical standards in data usage, and navigate the changing landscape of regulatory requirements. By doing so, they can fully leverage the benefits of these technologies while effectively managing the associated challenges.
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