2 cissp ® Official Study Guide Eighth Edition

Data Analytics
Data analytics
is the science of raw data examination with the focus of extracting useful 
information out of the bulk information set. The results of data analytics could focus on 
important outliers or exceptions to normal or standard items, a summary of all data items, 
or some focused extraction and organization of interesting information. Data analytics is a 
growing field as more organizations are gathering an astounding volume of data from their 
customers and products. The sheer volume of information to be processed has demanded 
a whole new category of database structures and analysis tools. It has even picked up the 
nickname of “big data.”
Big data
refers to collections of data that have become so large that traditional means 
of analysis or processing are ineffective, inefficient, and insufficient. Big data involves 
numerous difficult challenges, including collection, storage, analysis, mining, transfer, dis-
tribution, and results presentation. Such large volumes of data have the potential to reveal 
nuances and idiosyncrasies that more mundane sets of data fail to address. The potential 
to learn from big data is tremendous, but the burdens of dealing with big data are equally 
great. As the volume of data increases, the complexity of data analysis increases as well. Big 
data analysis requires high-performance analytics running on massively parallel or distrib-
uted processing systems. With regard to security, organizations are endeavoring to collect 
an ever more detailed and exhaustive range of event data and access data. This data is col-
lected with the goal of assessing compliance, improving efficiencies, improving productiv-
ity, and detecting violations.

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Chapter 9 
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Security Vulnerabilities, Threats, and Countermeasures