PART I: ANALYTICAL FOUNDATIONS Chapter 1: What is analytics? Chapter 2: Data, evidence, and claims Chapter 3: Data provenance Chapter 4: Data collection: Design, sampling, and structure Chapter 5: Perception and cognitive bias Chapter 6: Reasoning, inference, and analytical fallacies Chapter 7: Ethical responsibility, defensibility, and disclosure PART II: THE NATURE AND STRUCTURE OF DATA Chapter 8: Data types, tables, datasets, and databases Chapter 9: Structured data in practice Chapter 10: Data beyond tables Chapter 11: Preparing data for analysis PART III: ANALYTICS AND INFERENCE Chapter 12: Tools for business analytics Chapter 13: Framing questions and problems Chapter 14: Descriptive analytics: What has happened? Chapter 15: Time series analysis and basic forecasting Chapter 16: Patterns and relationships Chapter 17: Sampling, uncertainty, and estimation Chapter 18: Hypothesis testing, statistical significance, and causal claims Chapter 19: Linear regression and the evaluation of relationships Chapter 20: Model validation and the limits of inference PART IV: VISUALIZATION FOR ANALYTICS Chapter 21: Why visualization? Charts are arguments Chapter 22: Principles of data visualization Chapter 23: Chart types and visual distortions PART V: DECISIONS, COMMUNICATION, AND THE FUTURE Chapter 24: Decision support, sensitivity analysis, and optimization under uncertainty Chapter 25: Financial and investment decisions Chapter 26: Financial modeling Chapter 27: Narrative, persuasion, and ethical influence Chapter 28: Oral presentation Chapter 29: Dashboards and organizational reporting Chapter 30: The future of business analytics
Biography
Alym Amlani CPA, CA, is an accomplished educator and author. He specializes in business analytics, accounting, information systems, and emerging technologies. He currently teaches at the University of British Columbia (Sauder) and Kwantlen Polytechnic University (Melville) Schools of Business. His research focuses on the practical application of data, financial analysis, technology, and decision-making at the intersection with Artificial Intelligence.
Paul Davis MBA, LLD, is an educator, author, and researcher with four decades of experience in law, finance, business, and accounting. He began his professional career as an Assistant Professor of Law at the University of Ottawa, then founded several successful businesses before returning to academia in 2020. His research and writing range from criminal sentencing to ethics, judgment, and emerging technologies in education, with particular emphasis on communication and the responsible use of new technologies.
Business Analytics: From Data to Decision is a timely and practical resource for professionals who need to work confidently with data in an AI-enabled world. Amlani and Davis clearly show that strong analytics requires more than technical skill. It demands good questions, sound reasoning, ethical judgment, and clear communication.
Mynda Treacy, Microsoft MVP, Founder and lead trainer, My Online Training Hub
This book is an important resource for anyone working with data. It provides an accessible understanding of data, analytics, and visualization that will empower the reader to be more effective in a world enriched with new sources of information. The presentation is comprehensive, and ensures exposure to data fundamentals, effective approaches to analysis, and good guidance on how to best communicate and understand outcomes. This work is a practical guide to better data analytics and decision making.
Dr. Darren Dahl, Dean, UBC Sauder School of Business
As a finance executive, I've sat across from a lot of analysts. We've always been told to trust the data, this book tells us something more important, to trust the analyst. Data doesn't make decisions, people do. Business Analytics: From Data to Decision stands out because it covers the full reality of analytics in practice; from data sourcing, cognitive bias, and ethical responsibility, to forecasting, financial modeling, visualization, and narrative communication. It doesn't just teach students how to run an analysis; it teaches them how to think critically, communicate under uncertainty, and defend their conclusions to decision-makers. What makes it especially timely is how thoughtfully it addresses AI, not as a shortcut, but as a tool that makes these human skills more essential than ever. In a world where anyone can generate an analysis in seconds, the professional edge belongs to the person who can interrogate, interpret, and communicate it with confidence. This book builds that person. And that's the analyst I want sitting across from me in a budget meeting.
Judy Hoang CPA, CA, MBA, VP Finance
