Autorentext

Aneesh Banerjee is an Associate Professor (Reader) at Bayes Business School, City, University of London. He teaches topics in data analytics, AI and machine learning, and digital technologies. His teaching has been recognised by several awards including the City Icons award - Celebrating 150 years of excellence in teaching and innovation. Before joining academia, Dr Banerjee worked in various strategy and consulting roles at leading technology firms.

Klappentext

Unlocking Business Insights provides a practical framework for extracting meaningful business insights from data, guiding leaders through fundamental principles that transform overwhelming datasets into actionable business value.

This book equips readers with essential analytical thinking skills through real-world examples that bridge theory and practice. Readers will gain the ability to navigate complex data landscapes, make informed decisions based on proper benchmarking, understand trends, compare like-for-like indicators, evaluate trade-offs, develop predictive models, account for competitor intelligence, and recognise cognitive biases - all presented in accessible language that demystifies data analytics for business application. The author's structured approach balances analytical rigor with business relevance, helping managers extract valuable insights without getting lost in technical complexities. Each principle builds upon the previous one, creating a comprehensive framework that transforms raw data into strategic advantage while addressing the tension between statistical precision and practical business needs.

Written for business leaders, managers, and students at all levels studying data analytics, this book is ideal for introductory courses in business analytics, data science for business, and data-driven decision making - offering a non-technical approach for those seeking to implement data-driven strategies without specialised expertise.

The book is supported by comprehensive online resources including a testbank and PowerPoint slides.

Inhalt

Chapter 1 Introduction. Chapter 2 Principle 1: Look for appropriate benchmarks. Chapter 3 Principle 2: The importance of trends. Chapter 4 Principle 3: Comparing likes for likes - as much as possible. Chapter 5 Principle 4: Understanding trade-offs in choices. Chapter 6 Principle 5: From descriptions to predictions. Chapter 7 Principle 6: Account for the intelligence of your competitors. Chapter 8 Principle 7: Mind the biases. Chapter 9 Principle 8: Getting data governance right Chapter 10 Principle 9: Ethics and responsible data-driven decisions. Chapter 11 Principle 10: From analysis to influence. Chapter 12 Conclusion: Building an Organisational culture for data-driven decision-making.