Big data war : : how to survive global big data competition / / Patrick H. Park.

• Superior document: Big data and business analytics collection,
• VerfasserIn: Park, Patrick H.,
• Place / Publishing House: New York, New York (222 East 46th Street, New York, NY 10017) : : Business Expert Press,, 2016.
• Year of Publication: 2016
• Edition: First edition.
• Language: English
• Series: Big data and business analytics collection.
• Physical Description: 1 online resource (x, 195 pages)
• Notes: Includes index.

Written by Patrick H. Park, an author of Brain Work (Korea, 2014). The book mainly focuses on why data analytics fails in business. It provides an objective analysis and root causes of the phenomenon, instead of abstract criticism of utility of data analytics. The author, then, explains in detail on how companies can survive and win the global big data competition, based on actual cases of companies. Having established the execution and performance-oriented big data methodology based on over 10 years of experience in the field as an authority in big data strategy, the author identifies core principles of data analytics using case analysis of failures and successes of actual companies. Moreover, he endeavors to share with readers the principles regarding how innovative global companies became successful through utilization of big data. This book is a quintessential big data analytics, in which the author's know-how from direct and indirect experiences is condensed. How do we survive at this big data war in which Facebook in SNS, Amazon in e-commerce, and Google in search expand their platforms to other areas based on their respective distinct markets? The answer can be found in this book.

Table of Contents

Part 1. Dump the data -- 1. Global data war -- 2. Why did Google TV fail? -- 3. Why do they analyze data? -- Part 2. Data is human -- 4. Think as a customer -- 5. Big data, start from human -- 6. Why does Nike compete with Nintendo? -- Part 3. Data is created by me -- 7. Knowing necessary data is everything in data analytics -- 8. Create data -- Part 4. We don't need the past -- 9. Predict human unconsciousness -- 10. Anything with a pattern can be predicted -- Part 5. What matters at the end is performance -- 11. Data is strategy -- 12. Big data, a long way to go -- Epilogue -- About the author -- Index.