Too big to ignore : the business case for big data /

• Κύριος συγγραφέας: Simon, Phil
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken, New Jersey : John Wiley & Sons, Inc., [2013]
• Σειρά: Wiley and SAS business series.
• Θέματα: Data mining.; Database management.; Business > Data processing.; Big data.; COMPUTERS > Database Management > Data Mining.; Electronic books.

How businesses of all shapes and sizes can harness the power of Big Data If you haven't heard of Big Data, you're increasingly in the minority. People produce a mind-boggling amount of data every day-so much that making sense of it all is simply beyond the current capabilities of most orga...

Πίνακας περιεχομένων:

• Too Big to Ignore; Contents; List of Tables and Figures; Preface; Acknowledgments; Introduction: This Ain't Your Father's Data; Better Car Insurance through Data; Potholes and General Road Hazards; Recruiting and Retention; How Big Is Big? The Size of Big Data; Why Now? Explaining the Big Data Revolution; The Always-On Consumer; The Plummeting of Technology Costs; The Rise of Data Science; Google and Infonomics; The Platform Economy; The 11/12 Watershed: Sandy and Politics; Social Media and Other Factors; Central Thesis of Book; Plan of Attack; Who Should Read This Book?; Summary; Notes.
• Chapter 1 Data 101 and the Data DelugeThe Beginnings: Structured Data; Structure This! Web 2.0 and the Arrival of Big Data; Unstructured Data; Semi-Structured Data; Metadata; The Composition of Data: Then and Now; The Current State of the Data Union; The Enterprise and the Brave New Big Data World; The Data Disconnect; Big Tools and Big Opportunities; Summary; Notes; Chapter 2 Demystifying Big Data; Characteristics of Big Data; Big Data Is Already Here; Big Data Is Extremely Fragmented; Big Data Is Not an Elixir; Small Data Extends Big Data; Big Data Is a Complement, Not a Substitute.
• Big Data Can Yield Better PredictionsBig Data Giveth-and Big Data Taketh Away; Big Data Is Neither Omniscient Nor Precise; Big Data Is Generally Wide, Not Long; Big Data Is Dynamic and Largely Unpredictable; Big Data Is Largely Consumer Driven; Big Data Is External and "Unmanageable" in the Traditional Sense; Big Data Is Inherently Incomplete; Big Overlap: Big Data, Business Intelligence, and Data Mining; Big Data Is Democratic; The Anti-Definition: What Big Data Is Not; Summary; Notes; Chapter 3 The Elements of Persuasion: Big Data Techniques; The Big Overview.
• Statistical Techniques and MethodsRegression; A/B Testing; Data Visualization; Heat Maps; Time Series Analysis; Automation; Machine Learning and Intelligence; Sensors and Nanotechnology; RFID and NFC; Semantics; Natural Language Processing; Text Analytics; Sentiment Analysis; Big Data and the Gang of Four; Predictive Analytics; Two Key Laws of Big Data; Collaborative Filtering; Limitations of Big Data; Summary; Notes; Chapter 4 Big Data Solutions; Projects, Applications, and Platforms; Hadoop; Other Data Storage Solutions; NoSQL Databases; NewSQL; Columnar Databases.
• Google: Following the Amazon Model?Websites, Start-Ups, and Web Services; Kaggle; Other Start-Ups; Hardware Considerations; The Art and Science of Predictive Analytics; Summary; Notes; Chapter 5 Case Studies: The Big Rewards of Big Data; Quantcast: A Small Big Data Company; Steps: A Big Evolution; Buy Your Audience; Results; Lessons; Explorys: The Human Case for Big Data; Better Healthcare through Hadoop; Steps; Results; Lessons; NASA: How Contests, Gamification, and OpenInnovation Enable Big Data; Background; Examples; A Sample Challenge; Lessons; Summary; Notes.