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on significantly larger volumes of data. Those larger volumes of data allow
a much more detailed level of analysis for everything from geographic and
spatial analysis of three-dimensional survey data to predicting where the
most cost-efficient and least environmentally impactful deposits and
withdrawal activity could happen.
The energy industry's need to predict customer usage and to optimize the
availability of multiple fuel types will continue to grow as the various fuel
types become more and more popular (for instance, as geothermal and
hydroelectric power begin to move more households away from traditional
types of electricity derived from coal or oil). These types of analytics will
show whether specific areas of the country could really benefit from the
various types of fuel or alternative fuel sources.
Retail
Many of you are already familiar with the types of analysis that retail
organizations do. As you shop at Amazon.com , eBay, or any other major
retail sites, “you may also like this” sections pop up (known as market
basket analysis ). In large Hadoop implementations, this type of analysis
occurs on the fly and behind the scenes based on your shopping cart and
your shopping preferences. The companies are running an analytics model
designed to tell you about other products/services that you might be
interested in. Mahout is the tool in the Hadoop ecosystem that many
organizations use to build and leverage these models for testing solution
combinations. This market basket analysis value-added service provides
recommended tracking for customers, and through tracking customer
patterns, it may enable the retailer to sell more and make more money per
individual transaction (a key metric in the retail space).
A new type of analytics in the retail space tracks customers as they move
through a store. Some organizations do this with a little chip in a shopping
cart. Others do it by getting you to install their smartphone application.
However these stores physically track customers, the most important piece
of information is where customers are spending most of their time. For
example, are they spending most of their time shopping for groceries and
spending some time in sporting goods? Do they go to automotive every time
they come in to buy groceries? These analytics give retailers an incredible
amount of insight into how they can stock their stores and how they can
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