From Big Data to Smart Data.
Title:
From Big Data to Smart Data.
Author:
Iafrate, Fernando.
ISBN:
9781119119265
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (89 pages)
Contents:
Cover -- Title Page -- Copyright -- Contents -- Preface -- List of Figures and Tables -- Introduction -- I.1. Objectives -- I.2. Observation -- I.2.1. Before 2000 (largely speaking, before e-commerce) -- I.2.2. Between 2000 and 2010 (the boom of e-commerce, then the advent of social networks) -- I.2.3. Since 2010 (mobility and real-time become keywords) -- I.2.4. And then … (connected objects…) -- I.3. In sum -- 1: What is Big Data? -- 1.1. The four "V"s characterizing Big Data -- 1.1.1. V for "Volume" -- 1.1.2. V for "Variety" -- 1.1.3. V for "Velocity" -- 1.1.4. V for "Value", associated with Smart Data -- 1.1.4.1. What value can be taken from Big Data? -- 1.2. The technology that supports Big Data -- 2: What is Smart Data? -- 2.1. How can we define it? -- 2.1.1. More formal integration into business processes -- 2.1.2. A stronger relationship with transaction solutions -- 2.1.3. The mobility and the temporality of information -- 2.1.3.1. The automation of analysis -- 2.2. The structural dimension -- 2.2.1. The objectives of a BICC -- 2.3. The closed loop between Big Data and Smart Data -- 3: Zero Latency Organization -- 3.1. From Big Data to Smart Data for a zero latency organization -- 3.2. Three types of latency -- 3.2.1. Latency linked to data -- 3.2.2. Latency linked to analytical processes -- 3.2.3. Latency linked to decision-making processes -- 3.2.4. Action latency -- 4: Summary by Example -- 4.1. Example 1: date/product/price recommendation -- 4.1.1. Steps "1" and "2" -- 4.1.2. Steps "3" and "4": enter the world of "Smart Data" -- 4.1.3. Step "5": the presentation phase -- 4.1.4. Step "6": the "Holy Grail" (the purchase) -- 4.1.5. Step "7": Smart Data -- 4.2. Example 2: yield/revenue management (rate controls) -- 4.2.1. How it works: an explanation based on the Tetris principle (see Figure 4.4).
4.3. Example 3: optimization of operational performance -- 4.3.1. General department (top management) -- 4.3.2. Operations departments (middle management) -- 4.3.3. Operations management (and operational players) -- Conclusion -- Bibliography -- Glossary -- Index.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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