Too big to ignore : the business case for big data / Phil Simon.

Format:

Book

Author/Creator:

Simon, Phil.

Series:

Wiley & SAS business series.

Wiley & SAS business series

Language:

English

Subjects (All):

Business--Data processing.

Business.

Data mining.

Database management.

Big data.

Physical Description:

257p.

Edition:

1st ed.

Place of Publication:

Hoboken, N.J. : John Wiley & Sons, c2013.

Summary:

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.

Contents:

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

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

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

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

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

NASA: How Contests, Gamification, and OpenInnovation Enable Big Data

Background

Examples

A Sample Challenge

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

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

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

Final Thoughts

Spreading the Big Data Gospel

Selected Bibliography

About the Author

index.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from title page (ebrary, viewed April 17, 2013).

ISBN:

9781119204039

1119204038

9781118641866

1118641868

9781299315716

1299315712

9781118642108

1118642104

OCLC:

828776041