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Chapter 6 Foundations of Business Intelligence: Databases and Information Management 261

INTERACTIVE SESSION: TECHNOLOGY

BIG DATA, BIG REWARDS

Today’s companies are dealing with an avalanche of records, and 33 billion public records. The system’s
data from social media, search, and sensors as well as search capabilities allow the NYPD to quickly obtain
from traditional sources. In 2012, the amount of digital data from any of these data sources. Information on
information generated is expected to reach 988 exa- criminals, such as a suspect’s photo with details of
bytes, which is the equivalent to a stack of books from past offenses or addresses with maps, can be visual-
the sun to the planet Pluto and back. Making sense of ized in seconds on a video wall or instantly relayed
“big data” has become one of the primary challenges to officers at a crime scene.
for corporations of all shapes and sizes, but it also rep- Other organizations are using the data to go
resents new opportunities. How are companies cur- green, or, in the case of Vestas, to go even greener.
rently taking advantage of big data opportunities? Headquartered in Denmark,Vestas is the world’s
The British Library had to adapt to handle big data. largest wind energy company, with over 43,000
Every year visitors to the British Library Web site wind turbines across 66 countries. Location data are
perform over 6 billion searches, and the library is also important to Vestas so that it can accurately place its
responsible for preserving British Web sites that no turbines for optimal wind power generation. Areas
longer exist but need to be preserved for historical without enough wind will not generate the necessary
purposes, such as the Web sites for past politicians. power, but areas with too much wind may damage
Traditional data management methods proved inade- the turbines. Vestas relies on location-based data to
quate to archive millions of these Web pages, and leg- determine the best spots to install their turbines.
acy analytics tools couldn’t extract useful knowledge To gather data on prospective turbine locations,
from such quantities of data. So the British Library Vestas’s wind library combines data from global
partnered with IBM to implement a big data solution weather systems along with data from existing
to these challenges. IBM BigSheets is an insight turbines. The company’s previous wind library
engine that helps extract, annotate, and visually ana- provided information in a grid pattern, with each
lyze vast amounts of unstructured Web data, deliver- grid measuring 27 x 27 kilometers (17 x 17 miles).
ing the results via a Web browser. For example, users Vestas engineers were able to bring the resolution
can see search results in a pie chart. IBM BigSheets down to about 10 x 10 meters (32 x 32 feet) to estab-
is built atop the Hadoop framework, so it can process lish the exact wind flow pattern at a particular loca-
large amounts of data quickly and efficiently. tion. To further increase the accuracy of its turbine
State and federal law enforcement agencies are placement models, Vestas needed to shrink the grid
analyzing big data to discover hidden patterns in area even more, and this required 10 times as much
criminal activity such as correlations between time, data as the previous system and a more powerful
opportunity, and organizations, or non-obvious data management platform.
relationships (see Chapter 4) between individuals The company implemented a solution consisting
and criminal organizations that would be difficult to of IBM InfoSphere BigInsights software running on
uncover in smaller data sets. Criminals and criminal a high-performance IBM System x iDataPlex server.
organizations are increasingly using the Internet to (InfoSphere BigInsights is a set of software tools for
coordinate and perpetrate their crimes. New tools big data analysis and visualization, and is powered by
allow agencies to analyze data from a wide array of Apache Hadoop.) Using these technologies, Vestas in
sources and apply analytics to predict future crime creased the size of its wind library and is able manage
patterns. This means that law enforcement can and analyze location and weather data with models
become more proactive in its efforts to fight crime that are much more powerful and precise.
and stop it before it occurs. Vestas’s wind library currently stores 2.8 petabytes
In New York City, the Real Time Crime Center of data and includes approximately 178 parameters,
data warehouse contains millions of data points on such as barometric pressure, humidity, wind direc-
city crime and criminals. IBM and the New York City tion, temperature, wind velocity, and other company
Police Department (NYPD) worked together to create historical data. Vestas plans to add global deforesta-
the warehouse, which contains data on over 120 mil- tion metrics, satellite images, geospatial data, and
lion criminal complaints, 31 million national crime data on phases of the moon and tides.

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