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Full Description
Research shows that companies that employ data-driven decision-making are more productive, have a higher market value, and deliver higher returns for their shareholders. In this book, the reader will discover the history, theory, and practice of data-driven decision-making, learning how organizations and individual managers alike can utilize its methods to avoid cognitive biases and improve confidence in their decisions. It argues that value does not come from data, but from acting on data.
Throughout the book, the reader will examine how to convert data to value through data-driven decision-making, as well as how to create a strong foundation for such decision-making within organizations. Covering topics such as strategy, culture, analysis, and ethics, the text uses a collection of diverse and up-to-date case studies to convey insights which can be developed into future action. Simultaneously, the text works to bridge the gap between data specialists and businesspeople. Clear learning outcomes and chapter summaries ensure that key points are highlighted, enabling lecturers to easily align the text to their curriculums.
Data-Driven Decision-Making for Business provides important reading for undergraduate and postgraduate students of business and data analytics programs, as well as wider MBA classes. Chapters can also be used on a standalone basis, turning the book into a key reference work for students graduating into practitioners. The book is supported by online resources, including PowerPoint slides for each chapter.
Contents
1. Introduction: What is Data-Driven Decision Making and Why Does It Matter? 2. Data Strategy: How to Align Data Initiatives with Business Goals and Objectives 3. Data Products 4. Data Culture: How to Foster a Data-Driven Mindset (data literacy) and Behaviour 5. Data Sources: How to Find, Collect, and Manage Data for Business Value 6. Data Visualization and Presentation 7. Data Analysis: Understand How Descriptive, Predictive, and Prescriptive Analytics can suppport the organizational Decision Processes 8. Data Infrastructure: How to Build and Manage a Modern Data Stack 9. Data Ethics: How to Ensure The Data Practices Are Responsible, Secure, and Legal 10. Perspectives on Decision Making using generative AI
