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254 Part Two Information Technology Infrastructure

6.3 USING DATABASES TO IMPROVE BUSINESS
PERFORMANCE AND DECISION MAKING

Businesses use their databases to keep track of basic transactions, such as paying
suppliers, processing orders, keeping track of customers, and paying employees.
But they also need databases to provide information that will help the company
run the business more efficiently, and help managers and employees make
better decisions. If a company wants to know which product is the most popular
or who is its most profitable customer, the answer lies in the data.

THE CHALLENGE OF BIG DATA

Up until about five years ago, most data collected by organizations consisted
of transaction data that could easily fit into rows and columns of relational
database management systems. Since then, there has been an explosion of
data from Web traffic, e-mail messages, and social media content (tweets,
status messages), as well as machine-generated 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 products that organize data in the form of
columns and rows. We now use the term big data to describe these datasets
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 exabyte 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, Twitter generates over 8 terabytes of data daily. According
to the International Data Center (IDC) technology research firm, data are more
than doubling every two years, so the amount of data available to organizations
is skyrocketing.
Businesses are interested in big data because they can reveal more patterns
and interesting anomalies than smaller data sets, with the potential to provide
new insights into customer behavior, weather patterns, financial market
activity, or other phenomena. However, to derive business value from these
data, organizations need new technologies and tools capable of managing and
analyzing non-traditional data along with their traditional enterprise data.

BUSINESS INTELLIGENCE INFRASTRUCTURE

Suppose you wanted concise, reliable information about current operations,
trends, and changes across the entire company. If you worked in a large
company, the data you need might have to be pieced together from separate
systems, such as sales, manufacturing, and accounting, and even from external
sources, such as demographic or competitor data. Increasingly, you might need
to use big data. A contemporary infrastructure for business intelligence has
an array of tools for obtaining useful information from all the different types
of data used by businesses today, including semi-structured and unstructured
big data in vast quantities. These capabilities include data warehouses and data
marts, Hadoop, in-memory computing, and analytical platforms.

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