Page 52 - Big Data Analytics for Intelligent Healthcare Management

44 CHAPTER 3 BIG DATA ANALYTICS IN HEALTHCARE: A CRITICAL ANALYSIS

FIG. 3.1
Big data and its 6V’s.

3.2 BIG DATA
Big data can be described as data that grows at a rate so that it surpasses the processing power of con-
ventional database systems and doesn’t fit the structures of conventional database architectures [3, 4].
Its characteristics can be defined with 6V’s: Volume, Velocity, Variety, Value, Variability, and
Veracity [3, 4]. A brief introduction to every V is given below and in Fig. 3.1.
Volume: Volume generally refers to the data size. In this case, data size may be terabytes or TB
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(nearly 1e+12bytes), petabytes or PB (nearly 1e+15bytes), and zettabytes or ZB (10 bytes), etc. [3].
Velocity: Velocity generally denotes the speed at which vast amounts of data is being created,
collected, and analyzed. In case of big data, data is generated at a very high speed.
Variety: Variety can be defined as the different types of data that contribute towards big data.
Data may exist in diverse forms such as structured, unstructured, or semistructured. For example,
structured data are organized in relational tables, semistructured data obtained from key-value
web clicks, and unstructured data are obtained from email messages, articles, and streamed video
and audio, etc. [3].
Value: This is one of the most significant characteristics of big data. Value means how much the
extracted data is worth [5]. Although big data means an endless amount of data, unless they can be
turned into something of value, they are worthless. Big data has a value if, and only if, the collected
data adds knowledge.
Variability: Variability means that data may change during the processing period or any stage in its
lifecycle [3]. If variety and variability increases, then there is a possibility of discovering unexpected,
hidden, and valuable information from big data.
Veracity: Veracity denotes the quality or trustworthiness of the data. By the term quality, we mean
two points: Data consistency (or reliability) and data trustworthiness [3]. There should not be incom-
pleteness, ambiguities, uncertainty, and deception due to data inconsistency, etc. in big data.
The next question is what is healthcare big data? Section 3.3 discusses healthcare data.