Analysis of Big Data: Techniques, Applications, and Business Use

This report provides a comprehensive overview of big data analysis, covering its historical context, from the early stages of data management in the 1960s and 1970s to its current significance. It defines big data, emphasizing its characteristics of volume, velocity, and variety, and highlights its applications in industries like retail and entertainment. The report also addresses the challenges associated with big data, such as data security and scalability issues. It explores the five key characteristics of big data: velocity, veracity, value, variety, and volume. Furthermore, it outlines various techniques used in big data analysis, including ensemble analysis, association analysis, high-dimensional analysis, and deep analysis. The report also explains how big data supports businesses, particularly in understanding customer preferences and conducting risk analysis, emphasizing its role in modern business operations.

History of big data
The big data concept sounds like a new
concept, but actual origin of the large sets
of data got started since the 1960s and
1970s, when world of the data was just
taking place with its very first centres of
data and relational database development.
In 2005, it came into notice that there
were many data users through the
platforms like youtube, facebook and few
other online platforms.
What is big data
The big data is generally referred to the data that contains wide range of
variety, arrive in increased volumes and with greater velocity. This
wide variety of database allows access to it in real time manner
(Favaretto and et.al., 2020). These are the large and tangled data sets
from the new sources of data and are very voluminous in nature,
making it very difficult for the traditional data software to manage
them. Example of big data using businesses are, the retail industry,
transportation, entertainment and media etc.
Characteristics of big data
analysis
Big data contains large and good amount of data
which is not used by the traditional storage of
data and is used in the big multinational
companies in order to process the business data
of number of organizations. There are 5
characteristics of big data namely: velocity,
veracity, value, variety and volume.The challenges of big
data analytics
Despite big data being voluminous and
used by the number of big companies, it
has some sides where it is not able to
serve the companies after a certain.
Examples of challenges are- Data security,
issues in scaling, lack of understandability
etc.
Techniques available to big data
analysis
There are six techniques available namely: Ensemble
analysis, Association analysis, High dimensional analysis,
Deep analysis, Precision analysis, Divide-and-conquer
analysis.
Ensemble analysis- This is the analysis which is done for
the cause of improving accuracy of applications of data
mining and predictive analysis by simply including related
but various analytical models that synthesize results into
one score card.
Association analysis- It refers to the determination of the
interesting relationships in great datasets.
Big data supporting
business
The data collection in the present
world is increasing day by day and is
also turning out to be a huge problem
for the companies, as the data is
generated from almost every online
platform and is going out of the hands
of the traditional storage.
Customer interactions- Big data
helps the businesses to understand the
customers taste and preferences by
looking at their profiles. It provides
the companies to profile their
customers from a far reach and gives a
benefit to the businesses to come in
contact with customers in a real time
(Grover and et.al., 2018).
Risk analysis- The environment is all
uncertain and there are various risks
which keep on emerging with time.