Too Big to Ignore : The Business Case for Big Data.
Title:
Too Big to Ignore : The Business Case for Big Data.
Author:
Simon, Phil.
ISBN:
9781118641866
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (197 pages)
Series:
Wiley and SAS Business Ser.
Contents:
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 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.
Abstract:
Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It's time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free
advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.
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.
Genre:
Electronic Access:
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