This volume covers such topics as business intelligence as a strategic control system, competitive assessment, and intelligence analysis and intelligence collection.

This volume covers such topics as strategic intent and strategic intelligence, users and producers, organizing intelligence activities from offensive analysis to the organization and management of intelligence activities, and strategic planning.

This volume covers such topics as business intelligence as a strategic control system, competitive assessment, and intelligence analysis and intelligence collection.

Part A of this volume deals with issues surrounding intelligence analysis. This covers its often unrealized surprising role in the organization's ultimate survival and success, the organizational context of the entire intelligence cycle, and the ultimate users' perception and needs. Part B is concerned primarily with analysis: getting ready for it; doing it; and using it. The intelligence profession in the corporate world should be able to find in this text actual examples for use and applications of analytical methods relevant to the daily work requirements.

Embark on a transformative journey into the captivating realm where artistry meets precision in the world of business analytics. "The Art and Science of Business Analytics" is an immersive exploration into the fusion of creativity and methodical analysis, where raw data evolves into strategic insight. This book transcends the conventional boundaries of analytics, inviting readers to embrace the fluidity of interpreting data while adhering to the rigor of statistical methodologies. Through vivid narratives and practical frameworks, it illuminates how the convergence of art and science in analytics empowers organizations to make astute decisions that drive growth and innovation. Whether a seasoned data strategist or a novice explorer, this book serves as a beacon, guiding readers to wield the palette of analytics skillfully, shaping visions and sculpting strategies that redefine success. Join the journey where creativity merges with precision in the world of "The Art and Science of Business Analytics."

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.

This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.