Database Reference
In-Depth Information
Although the volume of Big Data tends to attract the most attention, generally the
variety and velocity of the data provide a more apt definition of Big Data. (Big
Data is sometimes described as having 3 Vs: volume, variety, and velocity.) Due to
its size or structure, Big Data cannot be efficiently analyzed using only traditional
databases or methods. Big Data problems require new tools and technologies to
store, manage, and realize the business benefit. These new tools and technologies
enable creation, manipulation, and management of large datasets and the storage
environments that house them. Another definition of Big Data comes from the
McKinsey Global report from 2011:
Big Data is data whose scale,
distribution, diversity, and/or timeliness require the use of new
technicalarchitecturesandanalyticstoenableinsightsthatunlocknew
sources of business value.
McKinsey & Co.; Big Data: The Next Frontier for Innovation, Competition, and Productivity
[1]
McKinsey's definition of Big Data implies that organizations will need new data
architectures and analytic sandboxes, new tools, new analytical methods, and an
integration of multiple skills into the new role of the data scientist, which will
be discussed in Section 1.3.
Figure 1.1
highlights several sources of the Big Data
deluge.
Figure 1.1
What's driving the data deluge
The rate of data creation is accelerating, driven by many of the items in
Figure 1.1
.
Social media and genetic sequencing are among the fastest-growing sources of Big
Data and examples of untraditional sources of data being used for analysis.
