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 Topics

This thesis examines the impact of Big Data on managerial decision-making, aiming to determine whether data-driven approaches can mitigate human cognitive limitations and improve organizational performance. It explores the current application of Big Data in various industries, the functionality of artificial intelligence in decision processes, and the collaborative potential between human managers and algorithmic systems.

• The theoretical foundations of decision-making and the definition of Big Data.
• Use cases of Big Data implementation across retail, HR, manufacturing, and finance.
• An analysis of artificial intelligence systems, including IBM Deep Blue, Watson, and Google DeepMind.
• The debate regarding the automation of strategic decisions and the role of human intuition.
• Future trends in data-driven management and the evolving role of the human manager.

Excerpt from the book

Summary of Chapters

1 Introduction: Provides an overview of the rising importance of Big Data, defines the research objective, and outlines the structure of the thesis.

2 Theoretical principles: Examines decision-making theories and cognitive limitations, alongside the technical and historical context of Big Data and related analytics frameworks.

3 Analysis: Big Data in practice: Portrays practical industry use cases and evaluates artificial intelligence systems to assess parallels with human decision-making and the potential for manager replacement.

4 Closing remarks: Summarizes the thesis findings, discusses challenges like human resources and data privacy, and provides a future outlook on the interaction between humans and algorithms.

Keywords

Big Data, Managerial Decision-Making, Artificial Intelligence, Cognitive Computing, Data-Driven, Analytics, Business Intelligence, Machine Learning, Decision Traps, Automation, Human Resources, Strategy, Algorithm, Digitalization, Management.

Frequently Asked Questions

What is the core focus of this thesis?

The work investigates the integration of Big Data into organizational decision-making and evaluates whether this phenomenon can improve managerial outcomes by overcoming human cognitive biases.

What are the primary themes discussed?

Key themes include the technical classification of Big Data, its practical application in various industries, the role of artificial intelligence, and the changing landscape of management.

What is the central research question?

The study asks if and how Big Data can improve organizational decision-making and what implications these developments have for the managers of today and the future.

Which methodologies are employed in this analysis?

The research relies on the synthesis of scientific literature and empirical sources, comparing expert viewpoints with current case studies and survey data from organizations like PwC and Capgemini.

What topics are covered in the main body?

The main part covers the theoretical foundations of decision-making, the framework of Big Data technologies, and a detailed analysis of Big Data in practice through various use cases.

Which keywords best characterize this work?

Important terms include Big Data, Managerial Decision-Making, Artificial Intelligence, Business Intelligence, Data-Driven, and Machine Learning.

How does the author view the "second machine age"?

The author references the perspective that we are currently in an era where machines help overcome cognitive limitations, potentially revolutionizing productivity just as the first industrial revolution did for physical labor.

Can algorithms completely replace human managers?

The research concludes that complete replacement is unlikely for strategic, complex decisions, as human input remains essential for defining goals, interpreting data, and maintaining accountability.