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The Challenge of Big Data

Most data collected by organizations used to be transaction data that could

easily fit into rows and columns of relational database management systems.

We are now witnessing an explosion of data from web traffic, e-mail messages,

and social media content (tweets, status messages), as well as machinegenerated

data from sensors (used in smart meters, manufacturing sensors,

and electrical meters) or from electronic trading systems. These data may be

unstructured or semi-structured and thus not suitable for relational database

Chapter 6 Foundations of Business Intelligence: Databases and Information Management 227

products that organize data in the form of columns and rows. We now use

the term b ig data to describe these data sets with volumes so huge that they

are beyond the ability of typical DBMS to capture, store, and analyze.

Big data doesn't refer to any specific quantity but usually refers to data in

the petabyte and exabytc range-in other words, billions to trillions of records,

all from different sources. Big data are produced in much larger quantities and

much more rapidly than traditional data. For example, a single jet engine is

capable of generating 10 terabytes of data in just 30 minutes, and there are

more than 25,000 airline flights each day. Even though "tweets" are limited

to 140 characters each, 1\~itter generates more than 8 terabytes of data daily.

According to the International Data Center (!DC) technology research firm,

data are more than doubling every two years, so the amount of data availabUe to

organizations is skyrocketing.

Businesses are interested in big data because they can reveal more patterns

and interesting relationships than smaller data sets, with the potential to provide

new insights into customer behavior, weather patterns, financial market

activity, or other phenomena. For example, Shuttcrstock, the global online

image marketplace, stores 21 million images, adding 10,000 more each day.

To find ways to optimize the buying experience, Shutterstock analyzes its big

data to find out where its website visitors place their cursors and how long they

hover over an image before making a purchase.

Big data is also finding many uses in the public sector. The chapter-opening

case on the U.S. Postal Service is one example, as arc city governments using big

data to manage traffic flows and fight crime. The Interactive Session on Organizations

describes how New York City is using big data to lower its crime rate.

However, to derive business value from these data, organizations need new

technologies and tools capable of managing and analyzing nontraditional data

along with their traditional enterprise data. They also need to know what questions

to ask of the data and limitations of big data. Capturing, storing, and

analyzing big data can be expensive, and information from big data may not

necessarily help decision makers. It's important to have a clear understanding

of the problem big data. will solve for the business. The chapter-ending case

explores these issues.