This book comprehensively assesses the growing importance of project data for project scheduling risk analysis and control. It discusses the relevance of project data for both researchers and professionals and illustrates why the collection processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as datadriven project management and includes the discussion of using computer algorithms human intuition and project data for managing projects under risk. The book reviews the basic components of datadriven project management by summarizing the current stateoftheart methodologies including the latest computer and machine learning algorithms and statistical methodologies for project risk and control. It highlights the importance of artificial project data for academics and describes the specific requirements such data must meet. In turn the book discusses a wide variety of statistical methods available to generate theseampnbspartificial data and shows how they have helped researchers to develop algorithms and tools to improve decisionmaking in project management. Moreover it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decisionmaking. Finally the book introduces a new approach to data collection generation and analysis for creating project databases making it relevant for academic researchers and professional project managers alike.