Big Data Analytics

Turning Big Data into Big Money

AvFrank J. Ohlhorst

Inbunden, Engelska, 2013

Del 65 i serien Wiley and SAS Business Series

366 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

Beskrivning

Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantageTakes an in-depth look at the financial value of big data analyticsOffers tools and best practices for working with big dataOnce the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

Produktinformation

Utgivningsdatum:2013-01-08
Mått:163 x 239 x 18 mm
Vikt:345 g
Format:Inbunden
Språk:Engelska
Serie:Wiley and SAS Business Series
Antal sidor:176
Förlag:John Wiley & Sons Inc
ISBN:9781118147597

Utforska kategorier

Ledarskap och motivation inom Ekonomi och Ledarskap

Mer om författaren

FRANK J. OHLHORST is an award-winning technology journalist, professional speaker, and IT business consultant with over twenty-five years of experience in the technology arena. He has written for several leading technology publications, speaks at many industry conferences, and has several industry certifications.

Innehållsförteckning

Preface ixAcknowledgments xiiiChapter 1 What Is Big Data? 1The Arrival of Analytics 2Where Is the Value? 3More to Big Data Than Meets the Eye 5Dealing with the Nuances of Big Data 6An Open Source Brings Forth Tools 7Caution: Obstacles Ahead 8Chapter 2 Why Big Data Matters 11Big Data Reaches Deep 12Obstacles Remain 13Data Continue to Evolve 15Data and Data Analysis Are Getting More Complex 17The Future Is Now 18Chapter 3 Big Data and the Business Case 21Realizing Value 22The Case for Big Data 22The Rise of Big Data Options 25Beyond Hadoop 27With Choice Come Decisions 28Chapter 4 Building the Big Data Team 29The Data Scientist 29The Team Challenge 30Different Teams, Different Goals 31Don’t Forget the Data 32Challenges Remain 32Teams versus Culture 34Gauging Success 35Chapter 5 Big Data Sources .37Hunting for Data 38Setting the Goal 39Big Data Sources Growing 40Diving Deeper into Big Data Sources 42A Wealth of Public Information 43Getting Started with Big Data Acquisition 44Ongoing Growth, No End in Sight 46Chapter 6 The Nuts and Bolts of Big Data 47The Storage Dilemma 47Building a Platform 52Bringing Structure to Unstructured Data 57Processing Power 59Choosing among In-house, Outsourced, or Hybrid Approaches 61Chapter 7 Security, Compliance, Auditing, and Protection 63Pragmatic Steps to Securing Big Data 64Classifying Data 65Protecting Big Data Analytics 66Big Data and Compliance 67The Intellectual Property Challenge 72Chapter 8 The Evolution of Big Data 77Big Data: The Modern Era 80Today, Tomorrow, and the Next Day 84Changing Algorithms 90Chapter 9 Best Practices for Big Data Analytics 93Start Small with Big Data 94Thinking Big 95Avoiding Worst Practices 96Baby Steps 98The Value of Anomalies 101Expediency versus Accuracy 103In-Memory Processing 104Chapter 10 Bringing It All Together 111The Path to Big Data 112The Realities of Thinking Big Data 113Hands-on Big Data 115The Big Data Pipeline in Depth 116Big Data Visualization 121Big Data Privacy 122Appendix Supporting Data 125“The MapR Distribution for Apache Hadoop” 126“High Availability: No Single Points of Failure” 142About the Author 151Index 153