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210123 ||| eng |
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|a 111911926X
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|a 9781119119265
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|a 9781119119258
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|a 9781119116189
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|a 111911618X
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|a 1848217552
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|a 9781848217553
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|a QA76.9.B45
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|a Iafrate, Fernando
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|a From big data to smart data
|c Fernando Iafrate
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| 260 |
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|a London
|b John Wiley and Sons, Inc.
|c 2015
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| 300 |
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|a 1 online resource
|b illustrations
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|a 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 transactionsolutions; 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
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|a 4.3. Example 3: optimization of operational performance4.3.1. General department (top management) ; 4.3.2. Operations departments (middle management); 4.3.3. Operations management (and operationalplayers); Conclusion; Bibliography; Glossary; Index
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|a Includes bibliographical references and index
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|a 3.2.1. Latency linked to data3.2.2. 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 "SmartData"; 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 Tetrisprinciple (see Figure 4.4)
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|a 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
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|a Big data / fast
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|a Information Technology / bisacsh/2022
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|a COMPUTERS. / bisacsh/2022
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|a COMPUTERS / Database Management / General / bisacsh
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|a Données volumineuses
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a Advances in information systems set
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| 028 |
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|a 10.1002/9781119116189
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| 015 |
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|a GBC080610
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| 776 |
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|z 111911926X
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| 776 |
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|z 9781119119265
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| 776 |
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|z 9781119116189
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| 776 |
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|z 111911618X
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781848217553/?ar
|x Verlag
|3 Volltext
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|a 658
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|a 005.7
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|a 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 enterpr
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