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.
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