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Record Nr. |
UNINA9910812838403321 |
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Autore |
Simon Phil |
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Titolo |
Too big to ignore : the business case for big data / / Phil Simon |
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Pubbl/distr/stampa |
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Hoboken, N.J., : John Wiley & Sons, c2013 |
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ISBN |
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1-119-20403-8 |
1-118-64186-8 |
1-299-31571-2 |
1-118-64210-4 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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Collana |
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Wiley & SAS business series |
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Disciplina |
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Soggetti |
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Business - Data processing |
Data mining |
Database management |
Big data |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- 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 Deluge -- The 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 |
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Extends Big Data -- Big Data Is a Complement, Not a Substitute -- Big Data Can Yield Better Predictions -- Big 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 Methods -- Regression -- 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 -- Chapter 6 Taking the Big Plunge -- Before Starting -- Infonomics Revisited -- Big Data Tools Don't Cleanse Bad Data -- The Big Question: Is the Organization Ready? -- Think Free Speech, Not Free Beer -- Starting the Journey -- Start Relatively Small and Organically -- First Aim for Little Victories -- New Employees and New Skills -- Experiment with Big Data Solutions -- Gradually Gain Acceptance throughout the Organization -- Open Your Mind -- Let the Data Model Evolve -- Tap into Existing Communities -- Realize That Big Data Is Iterative -- Avoiding the Big Pitfalls -- Big Data Is a Binary. |
Big Data Is an Initiative -- Big Data Is a Side Project -- There Is a Big Data Checklist -- IT Owns Big Data -- Remember the Goal -- Summary -- Notes -- Chapter 7 Big Data: Big Issues and Big Problems -- Privacy: Big Data = Big Brother? -- Big Security Concerns -- Big, Pragmatic Issues -- Big Consumer Fatigue -- Rise of the Machines: Big Employee Resistance -- Employee Revolt and the Big Paradox -- Summary -- Notes -- Chapter 8 Looking Forward: The Future of Big Data -- Predicting Pregnancy -- Big Data Is Here to Stay -- Big Data Will Evolve -- Projects and Movements -- The Vibrant Data Project -- The Data Liberation Front -- Open Data Foundation -- Big Data Will Only Get Bigger . . . and Smarter -- The Internet of Things: The Move from Active toPassive Data Generation -- Hi-Tech Oreos -- Hi-Tech Thermostats -- Smart Food and Smart Music -- Big Data: No Longer a Big Luxury -- Stasis Is Not an Option -- Summary -- Notes -- Final Thoughts -- Spreading the Big Data Gospel -- Notes -- Selected Bibliography -- About the Author -- index. |
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