How will Big Data change the way managers make decisions? Artificial intelligence and its impact on managerial decision making

Table of Contents

• 1 Introduction
• 1.1 Problem and relevance of the subject
• 1.2 Objectives
• 1.3 Structure
• 2 Theoretical principles
• 2.1 Classification of the term decision-making
• 2.2 Classification of the term Big Data
• 2.3 Big Data's role in decision-making
• 3 Analysis: Big Data in practice
• 3.1 Structure of the analysis
• 3.2 Big Data-decision use cases in different industries
• 3.3 Artificial data-based decision makers
• 3.4 Parallels between human and artificial decision makers
• 3.5 Might managers be replaced by Big Data?
• 4 Closing remarks
• 4.1 Summary
• 4.2 Result: How changes Big Data managerial decision making?
• 4.3 Challenges
• 4.4 Trends and developments

Objectives and Key Themes

This thesis aims to investigate the impact of Big Data on managerial decision-making. It analyzes how Big Data is utilized in various companies, explores the functionality of artificial intelligence-driven decision-makers, and compares their rationality to that of human decision-makers. The ultimate goal is to determine whether Big Data can enhance or even replace human managerial decision processes.

• The role of Big Data in modern decision-making processes.
• A comparison of human and artificial intelligence-based decision-making.
• The potential of Big Data to improve the efficiency and rationality of managerial decisions.
• The implications of Big Data for the future of management and leadership.
• Challenges and trends related to Big Data in managerial decision-making.

Chapter Summaries

1 Introduction: This introductory chapter sets the stage for the thesis by establishing the problem statement – the increasing importance of Big Data and its potential to revolutionize managerial decision-making. It outlines the objectives of the study, aiming to clarify how Big Data influences managerial decision-making. The chapter also details the structure of the thesis, providing a roadmap for the reader. The relevance of the topic is underscored, highlighting the growing volume of data available to companies and the cognitive limitations faced by human decision-makers.

2 Theoretical principles: This chapter establishes the foundational theoretical concepts necessary to understand the relationship between Big Data and managerial decision-making. It begins by clarifying the definitions and classifications of both “decision-making” and “Big Data.” The chapter then explores the multifaceted role Big Data plays in informing and shaping decisions within organizations. This section lays the groundwork for analyzing real-world applications of Big Data in later chapters, providing a framework for interpreting the practical use cases discussed subsequently.

3 Analysis: Big Data in practice: This chapter delves into practical applications of Big Data in managerial decision-making across various industries. It presents numerous case studies illustrating the diverse ways in which companies leverage Big Data for improved decision-making. The analysis extends to examining the functioning of artificial intelligence (AI)-based decision-making systems, drawing parallels between human and artificial decision-makers. Finally, it explores the question of whether Big Data might ultimately replace human managers in decision-making roles, considering the implications and limitations of both human and AI approaches.

Keywords

Big Data, managerial decision-making, artificial intelligence, data analytics, business intelligence, decision support systems, cognitive limitations, rationality, data-driven decision-making, industry applications, AI decision-makers.

Frequently Asked Questions: A Comprehensive Language Preview on Big Data and Managerial Decision-Making

What is the main topic of this thesis?

This thesis investigates the impact of Big Data on managerial decision-making. It analyzes how Big Data is used in various companies, explores AI-driven decision-makers, compares their rationality to human decision-makers, and ultimately aims to determine whether Big Data can enhance or replace human managerial decision processes.

What are the key themes explored in this work?

Key themes include the role of Big Data in modern decision-making, a comparison of human and AI-based decision-making, the potential of Big Data to improve managerial decisions, the implications of Big Data for the future of management, and the challenges and trends related to Big Data in managerial decision-making.

What does the Table of Contents reveal about the structure of the thesis?

The thesis is structured into four chapters: an introduction establishing the problem and objectives; a theoretical chapter defining Big Data and decision-making and exploring their relationship; an analysis chapter examining real-world applications of Big Data in various industries, including AI-driven decision-making; and a concluding chapter summarizing findings, discussing challenges, and outlining future trends.

What are the objectives of the research?

The thesis aims to understand how Big Data influences managerial decision-making, compare human and AI decision-making, assess Big Data's potential to improve decision efficiency and rationality, and explore the implications for the future of management and leadership, including challenges and trends.

How does the thesis analyze Big Data in practice?

The analysis chapter presents case studies showcasing how companies use Big Data for improved decision-making across different industries. It examines AI-based decision-making systems, comparing their functionality and rationality to human decision-makers. The chapter also considers the possibility of Big Data replacing human managers.

What are the key findings and conclusions of the thesis (as previewed)?

The preview provides chapter summaries highlighting the exploration of Big Data's influence on managerial decisions, the comparison between human and AI decision-making, and an examination of the potential for Big Data to replace human managers. The complete findings and conclusions are detailed in the full thesis.

What are the keywords associated with this research?

Keywords include Big Data, managerial decision-making, artificial intelligence, data analytics, business intelligence, decision support systems, cognitive limitations, rationality, data-driven decision-making, industry applications, and AI decision-makers.

What is the target audience for this research?

The target audience is primarily academic, focusing on researchers and students interested in the intersection of Big Data, management, and artificial intelligence. The structured and professional manner of the presentation suggests suitability for academic use and analysis of themes within these fields.