Big Data

A Business and Legal Guide

James R. Kalyvas, Michael R. Overly

• Publisher: Auerbach Publications
• Year: 2014
• ISBN:
  9781498760225
• Edition: 1
• Topic: Business
• Subtopic: Management
• Index: Business

Big Data: A Business and Legal Guide supplies a clear understanding of the interrelationships between Big Data, the new business insights it reveals, and the laws, regulations, and contracting practices that impact the use of the insights and the data. Providing business executives and lawyers (in-house and in private practice) with an accessible p

Chapter 1

A Big Data Primer for Executives

James R. Kalyvas

1.1 What Is Big Data?

The phrase Big Data is commonplace in business discussions, yet it does not have a universally understood meaning. The main objective of this chapter is to provide a simple framework for understanding Big Data.

There have been many different definitions for Big Data proposed by technology experts and a wide range of organizations. For purposes of this book, we developed the following definition:

Big Data is a process to deliver decision-making insights. The process uses people and technology to quickly analyze large amounts of data of different types (traditional table structured data and unstructured data, such as pictures, video, email, transaction data, and social media interactions) from a variety of sources to produce a stream of actionable knowledge.

Because there is no commonly accepted definition of Big Data, we offer this definition because it is both descriptive and practical. Our definition emphasizes that the term Big Data really refers to a process that results in information that supports decision making, and the definition underscores that Big Data is not simply a shorthand reference to an amount or type of data. Our definition is derived from our research and elements of a number of existing definitions.

We include several frequently referenced definitions next for context and comparison. According to the McKinsey Global Institute:

“Big Data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered Big Data—i.e., we don’t define Big Data in terms of being larger than a certain number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of datasets that qualify as Big Data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, Big Data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes). (McKinsey Global Institute. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey & Company, June 2011.)

Gartner indicates the following:

Big Data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. (Gartner. IT Glossary. 2013. http://www.gartner.com/it-glossary/big-data/.)

The term Big Data is sometimes used in this book as part of a phrase, such as “Big Data analytics,” when a particular part of the process is being emphasized. In the rest of this chapter, we continue to build on the framework for understanding Big Data and describe at a very high level and in relatively nontechnical terms how it works.

1.1.1 Characteristics of Big Data

You will rarely see a discussion of Big Data that does not include a reference to the “3 Vs”1—volume, velocity, and variety—as distinguishing characteristics of Big Data. Simply put, it is the volume (amount of data), velocity (the speed of processing and the pace of change to data), and variety (sources of data and types of data)2 that most notably distinguish Big Data from the traditional approaches used to capture, store, manage, and analyze data.

1.1.2 Volume

The volume of data available to enterprises has dramatically increased since 2004. In 2004, the total amount of data stored on the entire Internet was 1 petabyte (equivalent to 100 years of all television content). As can be seen in Figure 1.1, by 2011 the total worldwide amount of information stored electronically was 1 zettabyte (1 million petabytes or 36 million years of high-definition [HD] video). By 2015, that number is estimated to reach 7.9 zettabytes (or 7.9 million petabytes), and then by 2003 skyrocket to 35 zettabytes (or 35 million petabytes).3 The size of the datasets in use today, and continually and exponentially growing, has outpaced the capabilities of traditional data tools to capture, store, manage, and analyze the data.

Figure 1.1

Visualizing Big Data.

1.1.3 The Internet of Things and Volume

The volume of data to be stored and analyzed will experience another dramatic upward arc as more and more objects are equipped with sensors that generate and relay data without the need for human interaction. Known as the Internet of Things (IoT), a concept hailing from the Massachusetts Institute of Technology (MIT) since 2000, it is the ability for machines and other objects, through sensors or other implanted devices, to communicate relevant data through the Internet directly to connected machines. The IoT is already in action regularly today (think exercise devices such as Fitbit® or FuelBand or connected appliances like the Nest thermostat or smoke detector), and we are still at the early stages of how ubiquitous it will become. For example, a basketball was recently produced with sensors that provide direct feedback to the user on the arc, spin, and speed of release of the player’s shots. While the player is receiving instant feedback and even “coaching” from the app on his or her iPhone, the app is also sending all of this data to the manufacturer as well as other important data relating to the frequency and duration of use, places the user frequents to play; by matching weather information, the manufacturer can even collect information on the impact of weathe...

Table of contents

• Dedications
• Disclaimer
• Why We Wrote This Book
• Acknowledgments
• About the Authors
• Contributors
• Chapter 1 - A Big Data Primer for Executives
• Chapter 2 - Overview of Information Security and Compliance: Seeing the Forest for the Trees
• Chapter 3 - Information Security in Vendor and Business Partner Relationships
• Chapter 4 - Privacy and Big Data
• Chapter 5 - Federal and State Data Privacy Laws and Their Implications for the Creation and Use of Health Information Databases
• Chapter 6 - Big Data and Risk Assessment
• Chapter 7 - Licensing Big Data
• Chapter 8 - The Antitrust Laws and Big Data
• Chapter 9 - The Impact of Big Data on Insureds, Insurance Coverage, and Insurers
• Chapter 10 - Using Big Data to Manage Human Resources
• Chapter 11 - Big Data Discovery
• Glossary