Fernando Iafrate 
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.

Table des matières

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

A propos de l’auteur

Fernando Iafrate, Senior Manager Business Intelligence & Data Architecture for Disneyland Paris, France.