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Executive Summary
Big data analytics is a technology that has been recently development to change
the way the world processing data. Big data analytics incorporates several different data
processing applications to collect, process, and then analyze the data. Big data has four
dimensions that makes up big data which are: volume, velocity, variety, and value.
Volume is the amount at which data is acquired. Velocity is the speed the data is
process. Variety is the different types of data that big data analytics analyzes. Finally,
value is the intrinsic value that each data has. Next is the complex process that big data
analytics has to do in order for it to process all the data acquired. However the process
of big data analytics is unique since it is cable of using different data processing
techniques like data mining, predictive analytics, text mining, and in-memory analytics.
Big data analytics also uses database management systems in order to make the
process more efficient for businesses. Different database management systems are
used for different purposes throughout the entire process however each database
management system are connected and help one another. Also this paper will discuss
the advantages and disadvantages of big data analytics. Even though big data analytics
does have disadvantages, the advantages outweigh them since the impact of big data
analytics is greater. The impact big data analytics has on so many industries in the
United States is tremendous especially in the real estate and financial industry. Also the
paper will go into detail about how predictive analytics plays a major role in big data
analytics. In greater detail we’ll discuss the process of predictive analytics and why it is
so crucial to the overall success of the technology of big data analytics.
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The growth of technology over the last century has been like any we have ever
seen in the history of mankind. From the early 1900s of the industrial age to the internet
boom in the late 1990s, technology advancement has continued to grow through the
test of time. Unlike certain industries, the technology has continued to innovate and
development better technology to help advance our global society in the medial,
business, military, retail fields and many others. Big data analytics is one of the major
breakthroughs that have occurred through technology. Big data analytics just recently
started to make a major impact on several industries. Big data analytics is complex and
there are many ways to use it as well. This paper will dissect what big data analytics is,
how to use big data analytics, what industries it affects currently and in the future, the
advantages and disadvantages of big data analytics, and how predictive analytics is
used in big data analytics.
What is big data analytics? The term “Big Data” refers to an enormous amount of
data information whether it be qualitative or quantitative that is organized. With the
assistance of predictive analytics, user behavior analytics, or any other advance
analytics, the data becomes organized into specific data sets. Big data analytics uses
database management systems to help process the enormous amount of data. What
makes big data analytics unique and different from other data processing applications is
that big data analytics has the capacity to handle the volume and complexity of all and
any data while others cannot. The way big data is able to handle it all is because big
data analytics uses several different type of data processing applications all at once.
The major ones associated with big data are: data mining, text mining, predictive
analytics, and in-memory analytics. This is probably the biggest reason on why big data
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analytics popularity has grown over the years. Our society has become extremely
dependent on data that almost every industry relies on some type of data processing
application to sort through all of it. Also, big data has four dimensions to it. The four
dimensions are: volume, velocity, variety, and value. Each dimension is crucial to
identifying big data compared to any of the other data processing applications (Oracle
4).
The first element to big data is volume. Volume is the amount of data that big
data analytics process. While big data analytics can handle more volume than other
data processing applications, the nature of the data is what makes big data unique. Big
data can process an extreme amount of high volume low-density data. Low-density data
is data has an unknown value to it. For example, twitter data feeds, clicking on a web
page, or network traffic are example of low-density data. Once consuming all the low-
density data, big data then converts the low-density data into high-density data, or
basically converting data with unknown value to data with value (Oracle 4).
Big data second element is velocity. Velocity is the speed of data or the rate data
is received and acted. Big data velocity is extremely fast and can be processed just as
fast as the data is received. The highest velocity data normally streams directly into
memory versus being written on a disk. Big data analytics has the capacity to handle
this type of velocity which is remarkable considering that most other data processing
applications cannot (Oracle 4).
The third element to big data is variety, which is unstructured data types.
Unstructured and semi-structured types require additional processing in order to
comprehend the data and to derive the meaning of it. Once the unstructured data is
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understood, it has the same requirements as structured data like summarization,
privacy, and lineage. However, data has complexity to it and sometimes data from a
known source will change without notice which can become difficult. Fortunately, big
data analytics has the capability to handle all three data types and understand each one
with the ability to differentiate between unstructured, semi-structured, and structured
data (Oracle 4).
The final element to big data is value. All data in the world has some sort of
intrinsic value however it must be discovered first. The range of techniques can range
from all types like quantitative to investigative techniques. Big data analytics is capable
of using several different techniques to find the data in the fastest and most efficient
way. Also finding values requires new techniques and discovering new processes which
