Big data is the new
frontier in consumer analytics. As organizations transact and interact with
customers, they are generating a tremendous amount of digital exhaust data — a
by-product of business activities. Over the years, this has resulted, in the
explosion of business data and its management, along three dimensions — volume,
variety and velocity (Laney, 2001).
According to the folks at IBM (Zikopoulos
et al., 2012), some enterprises are generating “terabytes of data every
hour of every day of the year”; the volume of information stored in the world
would grow from hundreds of exabytes (EB 10006) of data today to an
estimated 35 zetabytes (ZB 10007) by 2020.
As more and more data is generated every hour,
data velocity has grown tremendously. Much of the data is kept for short
duration, and must be analysed as it flows.
Furthermore, with proliferation of smart devices,
cameras, microphones, sensors, RFIDs, data has grown in terms of variety and
complexity.
Today much of the world’s information comprises of
huge, fast moving, unstructured data sets that cannot be processed or analysed
by means of the conventional methods that apply to structured data. These data
sets are collectively referred to today as big data. Dealing effectively
with them requires new ways of data handling and analysis.
