Database Reference
In-Depth Information
1.4 Examples of Big Data Analytics
After describing the emerging Big Data ecosystem and new roles needed to support
its growth, this section provides three examples of Big Data Analytics in different
areas: retail, IT infrastructure, and social media.
As mentioned earlier, Big Data presents many opportunities to improve sales and
marketing analytics. An example of this is the U.S. retailer Target. Charles Duhigg's
topic
The Power of Habit
[4] discusses how Target used Big Data and advanced
analytical methods to drive new revenue. After analyzing consumer-purchasing
behavior, Target's statisticians determined that the retailer made a great deal of
money from three main life-event situations.
• Marriage, when people tend to buy many new products
• Divorce, when people buy new products and change their spending habits
• Pregnancy, when people have many new things to buy and have an urgency
to buy them
Target determined that the most lucrative of these life-events is the third situation:
pregnancy. Using data collected from shoppers, Target was able to identify this fact
and predict which of its shoppers were pregnant. In one case, Target knew a female
shopper was pregnant even before her family knew [5]. This kind of knowledge
allowed Target to offer specific coupons and incentives to their pregnant shoppers.
In fact, Target could not only determine if a shopper was pregnant, but in which
month of pregnancy a shopper may be. This enabled Target to manage its inventory,
knowing that there would be demand for specific products and it would likely vary
by month over the coming nine- to ten-month cycles.
Hadoop [6] represents another example of Big Data innovation on the IT
infrastructure. Apache Hadoop is an open source framework that allows companies
to process vast amounts of information in a highly parallelized way. Hadoop
represents a specific implementation of the MapReduce paradigm and was designed
by Doug Cutting and Mike Cafarella in 2005 to use data with varying structures. It
is an ideal technical framework for many Big Data projects, which rely on large or
unwieldy datasets with unconventional data structures. One of the main benefits of
Hadoop is that it employs a distributed file system, meaning it can use a distributed
cluster of servers and commodity hardware to process large amounts of data. Some
of the most common examples of Hadoop implementations are in the social media
space, where Hadoop can manage transactions, give textual updates, and develop
social graphs among millions of users. Twitter and Facebook generate massive
