From Big Data to Smart Data

A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for). Today’s decision making can be reached via information (related to the data), knowledge (related to people and processes), and timing (the capacity to decide, act and react at the right time). The huge increase in volume of data traffic, and its format (unstructured data such as blogs, logs, and video) generated by the “digitalization” of our world modifies radically our relationship to the space (in motion) and time, dimension and by capillarity, the enterprise vision of performance monitoring and optimization.

PREFACE ix LIST OF FIGURES AND TABLES  xiii INTRODUCTION xv CHAPTER 1. WHAT IS BIG DATA? 1 1.1. The four “V”s characterizing Big Data 3 1.1.1. V for “Volume” 3 1.1.2. V for “Variety” 4 1.1.3. V for “Velocity” 8 1.1.4. V for “Value”, associated with Smart Data 9 1.2. The technology that supports Big Data 10 CHAPTER 2. WHAT IS SMART DATA? 13 2.1. How can we define it? 13 2.1.1. More formal integration into business processes   13 2.1.2. A stronger relationship with transaction solutions  14 2.1.3. The mobility and the temporality of information  15 2.2. The structural dimension 17 2.2.1. The objectives of a BICC 17 2.3. The closed loop between Big Data and Smart Data 18 CHAPTER 3. ZERO LATENCY ORGANIZATION 21 3.1. From Big Data to Smart Data for a zero latency organization 21 3.2. Three types of latency 21 3.2.1. Latency linked to data 21 3.2.2. Latency linked to analytical processes 22 3.2.3. Latency linked to decisionmaking processes 23 3.2.4. Action latency 23 CHAPTER 4. SUMMARY BY EXAMPLE 25 4.1. Example 1: date/product/price recommendation 26 4.1.1. Steps “1” and “2”   28 4.1.2. Steps “3” and “4”: enter the world of “Smart Data” 29 4.1.3. Step “5”: the presentation phase  29 4.1.4. Step “6”: the “Holy Grail” (the purchase) 30 4.1.5. Step “7”: Smart Data 30 4.2. Example 2: yield/revenue management (rate controls)  31 4.2.1. How it works: an explanation based on the Tetris principle (see Figure 4.4) 35 4.3. Example 3: optimization of operational performance 38 4.3.1. General department (top management) 42 4.3.2. Operations departments (middle management) 42 4.3.3. Operations management (and operational players) 43 CONCLUSION 47 BIBLIOGRAPHY 51 GLOSSARY 53 INDEX  57