[PDF] How Will Big Data Change The Way Managers Make Decisions Artificial Intelligence And Its Impact On Managerial Decision Making eBook
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Today’s most precious raw material is not gold, but Big Data: Each one of us generates a huge amount of information every single day, rendering thus both ourselves and our choices transparent. But in addition to that, Big Data helps companies to improve their decision-making. Since managers have to address highly complex issues in an ever more complicated world, they cannot do without Big Data and Artificial Intelligence, as Carolin Nothof explains. By taking into account various external factors, their algorithms predict right entrepreneurial choices. These choices can be made in areas such as retail, Human Resources, the Internet of Things, and marketing. Nothof’s publication is not only rich in theoretical explanations, but also gives examples of the practical use of Big Data in various industries. Machines are a man’s best co-workers. In this book: - Big Data; - decision-making; - AI; - Behavorial Economics; - Machine Learning; - algorithms
Artificial Intelligence (AI) is redefining the nature and principles of general management. The technological revolution is reshaping industries, disrupting existing business models, making traditional companies obsolete and creating social change. In response, the role of the manager needs to urgently evolve and adjust. Companies need to rethink their purpose, strategy, organisational design and decision-making rules. Crucially they will also need to consider how to nurture and develop the business leaders of the future and develop new ways to interact with society on issues such as privacy and trust. Containing international insights from leading figures from the world of management and technology, this book addresses the big challenges facing organisations, including: · Decision-making · Corporate strategy · People management and leadership · Organisational design Taking a holistic approach, this collection of expert voices provides valuable insight into how firms will discover and commit to what makes them unique in this new big data world, empowering them to create and sustain competitive advantage.
Dramatically improve your decisions with data and AI In Decision Intelligence: Transform Your Team and Organization with AI-Driven Decision-Making, a team of pioneering decision and AI strategists delivers a digestible and hands-on resource for professionals at every part of the decision-making journey. The book discusses the latest technology and approaches that bridge the gap between behavioral science, data science, and technological innovation. Discover how leaders from various industries and environments are using data and AI to make better future decisions, taking both human as well as business factors into account. This book covers: A demystifying behind-the-scenes peek inside how AI models, forecasts, and optimization for business challenges really work, and why they open up entirely new possibilities. A business-ready introduction to decision intelligence, exploring why traditional decision-making strategies are outdated and how to transition to decision-intelligence. The evolution of Decision Intelligence, coming from analytics and modern techniques like process mining and robotic process automation An examination of decision intelligence at the organizational level, including discussions of agile transformation, transparent organizational culture, and why psychological safety is a crucial enabler for new ways of decision-making in modern companies An overview of why (and where exactly) AI still needs human expertise and how to incorporate this topic in daily planning and decision making Decision Intelligence is essential reading for managers, executives, board members, other business leaders and soon-to-be leaders looking to improve the quality, adaptability, and speed of their decision-making. Praise for Decision Intelligence "In Decision Intelligence, Thorsten Heilig and Ilhan Scheer build a compelling case for the world of tomorrow’s version of decision-making.” ―Martin Lindstrom, New York Times best-selling author "Decision Intelligence will be one of the big topics for this decade and completely change the way organizations manage, plan, and operate. This book provides a comprehensive guide from the basics to the applications." ―Niklas Jansen, Entrepreneur and Tech Investor, Founding Partner Interface Capital and Co-Founder Blinkist "The book impressively demonstrates the potential and entry points into the world of AI-powered decision making. A very valuable reading for managers and their organizations". ―Michael Kleinemeier, Member of the Merck KG Board of Partners, former Member of the SAP SE Executive Board “The AI hype perfectly captured, easy to understand, de-mystified and mapped to clear use cases - a must-read for today's managers.” ―Dr. Daniela Gerd tom Markotten, Member of the Management Board for Digitalization and Technology, Deutsche Bahn AG
In todays competitive market, a manager must be able to look at data, understand it, analyze it, and then interpret it to design a smart business strategy. Big data is also a valuable source of information on how customers interact with firms through various mediums such as social media platforms, online reviews, and many more. The applications and uses of business analytics are numerous and must be further studied to ensure they are utilized appropriately. Data-Driven Approaches for Effective Managerial Decision Making investigates management concepts and applications using data analytics and outlines future research directions. The book also addresses contemporary advancements and innovations in the field of management. Covering key topics such as big data, business intelligence, and artificial intelligence, this reference work is ideal for managers, business owners, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
Document from the year 2019 in the subject Business economics - Business Management, Corporate Governance, , language: English, abstract: Geopolitically, economically and technologically, the world is in a state of upheaval. Technological progress is shortening innovation cycles, processes and projects in companies are steadily gaining in complexity, and the globalized economy is producing increasingly dynamic markets. In an era characterized by the networking of the real and virtual worlds, data is emerging as the resource of the future. However, the huge amounts of data, also known as Big Data, demand that they be used effectively. As a result, management must make increasingly frequent and complex decisions. However, human working time is scarce and the question arises as to whether human capacity, intelligence and creativity can still cope with the increasing demands. Could AI, using Big Data, possibly act as a success factor, in corporate decision making? Under this question, Mayer compares the decision-making of a human with that of an artificial intelligence. The focus here is primarily on the economic goal idea. What basic requirements does an AI need? What risks open up, among other things in terms of loss of control, ethics or data protection? And to what extent are the numerous visions actually realizable in practice? From the contents: - Digitization - Big Data - Artificial Intelligence - Management 4.0 - Enterprise
As we get caught up in the quagmire of Big Data and analytics, it remains critically important to be able to reflect and apply insights, experience, and intuition to your decision-making process. In fact, a recent research study at Tel Aviv University found that executives who relied on their intuition were 90 percent accurate in their decisions.Bu
Companies have more data at their fingertips than ever before. Yet, studies show that most executives and organizations fail to extract real value from it. Challenging the conventional wisdom of data-driven decision-making, marketing professors and behavioral scientists Bart De Langhe and Stefano Puntoni argue that many analytics efforts flounder because data analyses are disconnected from the decisions to be made. In their important book, they offer a new approach they call decision-driven analytics. Counterintuitively, they argue that the key to making good decisions with data is to start by putting data in the background. Drawing from their own research and teaching, as well as real-world business cases, De Langhe and Puntoni offer four pillars of decision-driven analytics and guide you around common mistakes that have held back many organizations from using data for impact. In Decision-Driven Analytics, you will learn how to: + Avoid common pitfalls in data-driven decision-making; + Close the gap between managers and decision-making on one side, and data scientists and data analytics on the other; + Enhance the impact of data analytics on business outcomes; + Think without data to make better decisions; + Prepare for artificial intelligence’s impact on data analytics; and + Evaluate the costs and benefits of decision-driven analytics. A must-read for anyone who wants to harness the power of data for competitive advantage, Decision-Driven Analytics will equip you with the skills and tools you need to more effectively use data for business outcomes and to make better decisions in today’s complex and data-rich world.
AI is described as human-produced structured organized knowledge formed with help of machines. Human insight techniques such as learning, thinking, and self-remedy are used in the production of AIs. Artificial intelligence is the future of business. Artificial intelligence paves the way for certain decision-making and saves time and lots of money also. Artificial intelligence system has the ability to gather data, forecasting, and trend analysis. The motive of the thesis is to highlight improving agricultural productivity with the introduction of artificial intelligence in the agriculture sector. Artificial intelligence enhances the economy and increases the productivity of the organization within a very short period of time. newlineDecisions are an important part of business management. Big data and large files along with data mining are considered important in decision-making in business. The safety of data is another important task. Along with these terms, the replacement of executives is the parameter on which the thesis is based. Ai and humans are very near to each other. One uses past data and the latter uses experience to make decisions. Data is a treasure to AI as it only makes it able to take decisions. All these have been used as parameters for this study. newline study further discusses the history of AI in detail. AI can be achieved in many ways. Machine learning, natural language processing, vision, robotics, etc. are some of the ways through which AI can be achieved and these have been disused in the study. AI has always been seen as a threat to human jobs. With the pace at which AI is replacing human, it becomes scary for humans to believe in AI. However manmade intelligence is found to take better and faster decisions. Artificial intelligence makes humans superhuman. There are too many differences between artificial intelligence and human which makes it difficult in replacing each other at a job. Research also describes the role of AI in money laundering. Money laundering is a serious issue pertaining to every business.
Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains.