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# Competing on Analytics: The New Science of Winning; With a New Introduction

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Competing on Analytics: The New Science of Winning; With a New Introduction [Davenport, Thomas H., Harris, Jeanne, Abney, David] on desertcart.com. *FREE* shipping on qualifying offers. Competing on Analytics: The New Science of Winning; With a New Introduction

Review: Essential book for every business - Before reading this book, I would like to inform the readers that, this book is not about in-depth technology of analytics. It does not contain any analytical model or detailed methods of analytics. It is a business book which focuses on analytical strategy and other related topics on analytics in businesses. If you’re expecting analytical techniques or predictive models for analytics you are not going to find them in this book. 1. The original edition of this book was probably the first book on business analytics which came out after article on “Competing on Analytics” written by Tom Davenport in early 2006. 2. This book will help readers to understand concepts, evolutions, management issues as well as applications of analytics. It will also explain how companies can use analytics to optimize key business processes as well as how they can make profit from their product and services by using analytical approaches. Further exploration could be achieved by using concepts, studies, suggestions, examples and references provided throughout this book. 3. The authors have divided the book into two parts; Part one: “The nature of analytical competition” Part two: “Building analytical capability”. 4. Most information from the original edition is included in this edition with some updates. Some new information is also included. 5. The “definition of analytics” is clearly defined in this book. It is identical to the definition defined in original edition. 6.The authors have added new topics such as data scientist, big data, Hadoop and other open-source software technology, data products, machine learning, IoT etc. to this edition. 7. In addition to the first three eras of analytics that had been defined in authors’ previous books and articles, new type of analytics known as Analytics 4.0 (Autonomous analytics) is added to this edition. 8. “Distinctive capability” is a term frequently used throughout this book. The authors have suggested that ‘distinctive capability’ is an attribute for organizations that want to be competitive. 9. The authors have defined “an organization that uses analytics extensively and systematically to out think and out execute the competition” as “Analytical competitor”. They’ve provided four common key characteristics of successful analytical companies which they’ve found in their studies. I strongly agree with these key characteristics particularly the senior management commitment. My experience suggested that without senior executive’s sponsorship it is unlikely for an organization to achieve its goal in analytics. 10. In addition to those characteristics, the authors have also suggested that to become a master in analytics, a company needs to build analytics capabilities which consist of five essential attributes. They’ve proposed a model called DELTA model (Data, Enterprise, Leadership, Targets, and Analyst) to help in building these analytics capabilities. 11. The authors have highlighted some studies by demonstrating that adoption of an analytical approach is closely correlated with high performance and is a source of competitive advantage of companies. 12. Analytics for internal processes and external processes are provided with some typical techniques including some detailed information in each domain. These types of analytics can be applied to support business processes. 13.The authors have proposed five stages roadmap for developing analytical capabilities with some detailed information in each step. DELTA model is applied to this roadmap with two more additional capabilities namely technology and analytical techniques. Some examples from successful companies are also included in stage two, stage three and stage four of the road map. The authors noted that these companies have one thing in common: “an absolute passion for analytics”. 14. Managing Analytical People is my favorite chapter. The authors have classified analytical people in organizations into three groups; senior management teams particularly the CEO, professional analyst and analytical amateurs. The authors have provided the characteristics of analytical leaders and the roles of C-suite executives including the roles of CDAO (Chief Data and Analytics Officer). Some information on analytical professional and data scientists as well as analytical amateurs is also given in this chapter. Autonomous decision making and override issues are provided with some examples from those who applied this type of decision and actions in different manner. The differences between analysts and data scientists are provided with some detailed information. Based on their studies, the authors have provided five critical success factors including “trust” between analysts and decision makers. I believe that these factors are very important in terms of analytics in operations which management teams might have overlooked. 15. The analytics and big data architecture provided in chapter 8 explained how technology, data and governance processes for analytics are incorporated into the company’s existing environments. The definition of analytics and big data architecture is clearly defined. The architecture is broken down into six elements ranging from data management to deployment processes. General information on analytical technology which covers some types of software tools is also given in this chapter. In summary, the authors have emphasized that senior management commitment and flexibility of architecture are important factors in support of analytical tasks. 16. The authors have provided information on future of analytics in the final chapter. They have divided analytics world in the future into three categories: Technology-driven approach, Human- driven approach and Strategy-driven approach. In the end, the authors have provided a summary of the key attributes of analytical competitors and a prediction of the future. They have suggested that analytical competitors will continue to examine their strategies and their business capabilities, refine their analytical capabilities, create data-driven culture and think broadly about whether their analytical models and data are still relevant to their businesses. Finally and perhaps most importantly the authors have emphasized that analytical competitor will continue to find ways to outperform their competitors. This book is packed with examples, studies, interviews and surveys from many successful as well as unsuccessful companies. However, I really don’t know whether the information in many parts of this book is still valid since some surveys, studies, and interviews had been conducted a couple of years prior to publication of original version. I felt that there have been a lot of changes as big data and new analytical tools came into existence. This book is an essential book that every C-level executives, Heads of Strategic department, senior managers, middle managers, managers and those who want to apply analytical approaches in a company should have read it. Data scientists themselves may not enjoy reading these non-technical texts, however, they can gain some good information on managing analytical people provided in chapter seven. Adoption of analytics in a business requires changes in culture, process, people’s behavior and skills, if you are looking for a good guideline to help you to move along the path toward analytical competition, this book could be an invaluable source for you. This book is easy to read. Non-English speaking background readers can enjoy reading this great book.
Review: Very Good! - it arrived a little damaged in the cover, but the book itself is in a very good condition (hard case)

## Technical Specifications

| Specification | Value |
|---------------|-------|
| ASIN  | 1633693724 |
| Best Sellers Rank | #454,324 in Books ( See Top 100 in Books ) #217 in Decision-Making & Problem Solving #607 in Business Management (Books) #630 in Business Decision Making |
| Customer Reviews | 4.5 4.5 out of 5 stars (259) |
| Dimensions  | 6.4 x 1.2 x 9.3 inches |
| Edition  | Revised |
| ISBN-10  | 9781633693722 |
| ISBN-13  | 978-1633693722 |
| Item Weight  | 1.2 pounds |
| Language  | English |
| Print length  | 320 pages |
| Publication date  | September 19, 2017 |
| Publisher  | Harvard Business Review Press |

## Images

![Competing on Analytics: The New Science of Winning; With a New Introduction - Image 1](https://m.media-amazon.com/images/I/51iaLaS6jUL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Essential book for every business
*by P***N on November 18, 2017*

Before reading this book, I would like to inform the readers that, this book is not about in-depth technology of analytics. It does not contain any analytical model or detailed methods of analytics. It is a business book which focuses on analytical strategy and other related topics on analytics in businesses. If you’re expecting analytical techniques or predictive models for analytics you are not going to find them in this book. 1. The original edition of this book was probably the first book on business analytics which came out after article on “Competing on Analytics” written by Tom Davenport in early 2006. 2. This book will help readers to understand concepts, evolutions, management issues as well as applications of analytics. It will also explain how companies can use analytics to optimize key business processes as well as how they can make profit from their product and services by using analytical approaches. Further exploration could be achieved by using concepts, studies, suggestions, examples and references provided throughout this book. 3. The authors have divided the book into two parts; Part one: “The nature of analytical competition” Part two: “Building analytical capability”. 4. Most information from the original edition is included in this edition with some updates. Some new information is also included. 5. The “definition of analytics” is clearly defined in this book. It is identical to the definition defined in original edition. 6.The authors have added new topics such as data scientist, big data, Hadoop and other open-source software technology, data products, machine learning, IoT etc. to this edition. 7. In addition to the first three eras of analytics that had been defined in authors’ previous books and articles, new type of analytics known as Analytics 4.0 (Autonomous analytics) is added to this edition. 8. “Distinctive capability” is a term frequently used throughout this book. The authors have suggested that ‘distinctive capability’ is an attribute for organizations that want to be competitive. 9. The authors have defined “an organization that uses analytics extensively and systematically to out think and out execute the competition” as “Analytical competitor”. They’ve provided four common key characteristics of successful analytical companies which they’ve found in their studies. I strongly agree with these key characteristics particularly the senior management commitment. My experience suggested that without senior executive’s sponsorship it is unlikely for an organization to achieve its goal in analytics. 10. In addition to those characteristics, the authors have also suggested that to become a master in analytics, a company needs to build analytics capabilities which consist of five essential attributes. They’ve proposed a model called DELTA model (Data, Enterprise, Leadership, Targets, and Analyst) to help in building these analytics capabilities. 11. The authors have highlighted some studies by demonstrating that adoption of an analytical approach is closely correlated with high performance and is a source of competitive advantage of companies. 12. Analytics for internal processes and external processes are provided with some typical techniques including some detailed information in each domain. These types of analytics can be applied to support business processes. 13.The authors have proposed five stages roadmap for developing analytical capabilities with some detailed information in each step. DELTA model is applied to this roadmap with two more additional capabilities namely technology and analytical techniques. Some examples from successful companies are also included in stage two, stage three and stage four of the road map. The authors noted that these companies have one thing in common: “an absolute passion for analytics”. 14. Managing Analytical People is my favorite chapter. The authors have classified analytical people in organizations into three groups; senior management teams particularly the CEO, professional analyst and analytical amateurs. The authors have provided the characteristics of analytical leaders and the roles of C-suite executives including the roles of CDAO (Chief Data and Analytics Officer). Some information on analytical professional and data scientists as well as analytical amateurs is also given in this chapter. Autonomous decision making and override issues are provided with some examples from those who applied this type of decision and actions in different manner. The differences between analysts and data scientists are provided with some detailed information. Based on their studies, the authors have provided five critical success factors including “trust” between analysts and decision makers. I believe that these factors are very important in terms of analytics in operations which management teams might have overlooked. 15. The analytics and big data architecture provided in chapter 8 explained how technology, data and governance processes for analytics are incorporated into the company’s existing environments. The definition of analytics and big data architecture is clearly defined. The architecture is broken down into six elements ranging from data management to deployment processes. General information on analytical technology which covers some types of software tools is also given in this chapter. In summary, the authors have emphasized that senior management commitment and flexibility of architecture are important factors in support of analytical tasks. 16. The authors have provided information on future of analytics in the final chapter. They have divided analytics world in the future into three categories: Technology-driven approach, Human- driven approach and Strategy-driven approach. In the end, the authors have provided a summary of the key attributes of analytical competitors and a prediction of the future. They have suggested that analytical competitors will continue to examine their strategies and their business capabilities, refine their analytical capabilities, create data-driven culture and think broadly about whether their analytical models and data are still relevant to their businesses. Finally and perhaps most importantly the authors have emphasized that analytical competitor will continue to find ways to outperform their competitors. This book is packed with examples, studies, interviews and surveys from many successful as well as unsuccessful companies. However, I really don’t know whether the information in many parts of this book is still valid since some surveys, studies, and interviews had been conducted a couple of years prior to publication of original version. I felt that there have been a lot of changes as big data and new analytical tools came into existence. This book is an essential book that every C-level executives, Heads of Strategic department, senior managers, middle managers, managers and those who want to apply analytical approaches in a company should have read it. Data scientists themselves may not enjoy reading these non-technical texts, however, they can gain some good information on managing analytical people provided in chapter seven. Adoption of analytics in a business requires changes in culture, process, people’s behavior and skills, if you are looking for a good guideline to help you to move along the path toward analytical competition, this book could be an invaluable source for you. This book is easy to read. Non-English speaking background readers can enjoy reading this great book.

### ⭐⭐⭐⭐⭐ Very Good!
*by Ó***T on February 2, 2026*

it arrived a little damaged in the cover, but the book itself is in a very good condition (hard case)

### ⭐⭐⭐⭐ General overview of firms competing with analytics
*by L***K on December 30, 2019*

Good read to understand the importance of analytics and the way some firms win with analytics. However, the book lacks of insight on analytical models.

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