Figure 2: Bringing data together.
The second new type of data comes
from machine data. This is data generated from an increasingly interconnected
world of devices and systems. Examples
range from data generated by sensors,
smart meters, RFID tags, security and
intelligence systems, IT logs (application and Web servers), etc. This data
tends to be largely semi-structured or
unstructured.
Business systems in BI and EDW environments are not architected to handle
the volume and variety of “human information” nor the volume and velocity of
machine generated data.
Today, organizations need to bring all
their data together for advanced analytics. For example, at HP, structured data
from a customer’s purchase history, demographics and warranty data can be
A NA L Y T I C S
combined with unstructured data coming
from customer support records and social media for a more focused customer
engagement strategy.
BIG DATA AND ANALYTICS:
PROCESS, PURPOSE, PRACTICE
As information and data assets of an
organization come together and combine
with external data, analytical techniques
and analysis will have a larger role to
play. In general, the characteristics of
big data that most influence the analytics process are related to the variety and
volume of data. However, velocity, which
is handled through business intelligence
practices, is considered distinct from
core analytics practices for the purposes
of this article. The analytics process is
usually represented as a set of activities
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