Aneesh Banerjee

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

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

When was the last time you faced a challenge that seemed too tough to handle? Was it a business obstacle you did not foresee, office politics that were hard to figure out, or a life-changing incident that left you shell-shocked?When life hands you a setback, how do you respond? Are you racked by doubt, unable to move--or do you charge ahead, eager for resolution, without much thought?Between these extremes is another approach built on the principles of resilience. In The Resilient Decision-Maker, Joseph Lampel, Aneesh Banerjee, and Ajay Bhalla show you how to approach challenges in life and business with resilience. This practical guide explains the different types of challenges we all face, examines the various forms resilience can take in the face of these challenges, and offers a guide to resilient leadership backed up by real-world examples. If you want to know your own resilience and how to grow it, you'll find the answers here.