Database Reference
In-Depth Information
which products get the most foot traffic using geospatial data
collected from the RFID chips
•
Data aggregators
(the dark gray ovals in
Figure 1.11
, marked as (3))
make sense of the data collected from the various entities from the
“SensorNet” or the “Internet of Things.” These organizations compile data
from the devices and usage patterns collected by government agencies,
retail stores, and websites. In turn, they can choose to transform and
package the data as products to sell to list brokers, who may want to
generate marketing lists of people who may be good targets for specific ad
campaigns.
•
Data users and buyers
are denoted by (4) in
Figure 1.11
.
These groups
directly benefit from the data collected and aggregated by others within
the data value chain.
Retail banks, acting as a data buyer, may want to know which
customers have the highest likelihood to apply for a second
mortgage or a home equity line of credit. To provide input for this
analysis, retail banks may purchase data from a data aggregator.
This kind of data may include demographic information about
people living in specific locations; people who appear to have a
specific level of debt, yet still have solid credit scores (or other
characteristics such as paying bills on time and having savings
accounts) that can be used to infer credit worthiness; and those
who are searching the web for information about paying off debts
or doing home remodeling projects. Obtaining data from these
various sources and aggregators will enable a more targeted
marketing campaign, which would have been more challenging
before Big Data due to the lack of information or high-performing
technologies.
Using technologies such as Hadoop to perform natural language
processing on unstructured, textual data from social media
websites, users can gauge the reaction to events such as
presidential campaigns. People may, for example, want to
determine public sentiments toward a candidate by analyzing
related blogs and online comments. Similarly, data users may want
to track and prepare for natural disasters by identifying which
areas a hurricane affects first and how it moves, based on which
