Download PDF Competing on Analytics: The New Science of Winning; With a New Introduction, by Thomas H. Davenport
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Competing on Analytics: The New Science of Winning; With a New Introduction, by Thomas H. Davenport
Download PDF Competing on Analytics: The New Science of Winning; With a New Introduction, by Thomas H. Davenport
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Product details
Hardcover: 320 pages
Publisher: Harvard Business Review Press; Revised edition (September 19, 2017)
Language: English
ISBN-10: 9781633693722
ISBN-13: 978-1633693722
ASIN: 1633693724
Product Dimensions:
6.4 x 1.2 x 9.3 inches
Shipping Weight: 1.1 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
14 customer reviews
Amazon Best Sellers Rank:
#32,431 in Books (See Top 100 in Books)
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 capabilityâ€.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 competitorâ€. 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 analyticsâ€.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.
If you like or not, analytics is the new reality in for the highest functioning businesses. As more and more data is available it is no longer optional. However getting your arms around what to do, where to start and what does this ultimately look like can seem almost insurmountable.In this book Jeannie Harris and Thomas Davenport do such and incredible job of outlining the natural progression of analytics in multiple different industries! I learned how to recognize an organization that is ready to embrace their data and find a better way vs an organization that is not ready. I learned where to start and more important, what kind of questions to ask to get the most out of analytics. As someone who makes a living helping ask these questions, this book was rocket fuel! I can't thank the authors enough for their work. I have personally purchased the Kindle edition and the audio version. Both well worth it.
Fantastic and informative book for anyone interested in understanding data science and how companies are applying it in the industry. The book offers a lot of useful lessons and concepts to keep in mind when attempting to build any analytically competitive culture in an organization. Very engaging and easy to read.
Well written, great examples of companies using analytics as a competitive edge, as well as practical advice to take a company to the next level of data and analytics
I got the audio version to set the background to an MBA crash course on analytics. I like the short history from data bases to the birth of the data scientists.
This book provides and excellent roadmap of skills needed to advance your business’s analytical journey. It also provides wonderful examples.
Thoroughly enjoyed the book, it was an easy read and using it as part of the company’s strategic planning process.
Too dense...it could have been written in less than a half of the pages. Interesting though.
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