Case Study on Information Systems and Big Data Analysis

This case study delves into the realm of Information Systems and Big Data Analysis, emphasizing the transformative role of big data in contemporary business. It highlights the challenges and characteristics associated with managing and analyzing massive datasets, including high volume, velocity, and variety. The study explores various techniques such as A/B testing, data mining, machine learning, and statistics, illustrating how these methods enable organizations to derive valuable insights and improve decision-making. Real-world examples showcase the application of big data technologies across diverse industries, underscoring their potential to enhance customer service, operational efficiency, and risk management. The analysis also references academic sources, providing a comprehensive understanding of the subject.

History on big Data
Big data is a form of technology which is used in storing,
analyzing and managing the massive data. The case study
describe about the combination of a structured and
unstructured or semi-structured part of detail collected
by the firm which is mainly used in many kind of
projections it includes acquisition of machine operations,
forecast modeling etc. They also assists the organization
to manage a big amount of information
Information Systems and Big Data Analysis
Name of the Student
What is big Data
The company big data involves big detail, bunch of tough data,
significantly gathered from innovative or new origins. These
kind of information have a combination of some variety of data;
It also have a high volume and velocity. This case study includes
four V's that shows velocity, volume, value and variety. It gives
power to the firms to create profit based decisions.
Characteristics of Big data
There are some illustrative examples of companies that utilize
big information technologies and it involves agriculture,
pharmaceuticals, etc. The important features of big information
are explained under:
• Velocity: The name Big data is itself related to a size which
means huge or enormous. Size of data plays a very crucial
role in determining the value of data.
• Value: Variety refers to the heterogeneous sources and the
quality of data, both structured or unstructured are
included..
• Volume: The term refers to the generation of speed of the
data how fast a data can be generated or processed to meet
the demands and determines the potential of the data.
• Variety: This refers to the inconsistency which can be
shown by the data in some cases, therefore hampering the
process of being able to handle and management of data
efficiently..
The challenges of big data
analytics
In the today's era where everything is digitalized from
shopping to schooling from education to work, post
pandemic everything is highly digitalized. Big data
analytics is the process of using this data available in
different forms structured, unstructured various sizes in
order to analyze and apply in the organizational uses. Big
data have following characteristics:
• High volume
• High velocity
• Artificial intelligence
• Mobile
• Social
• Internet of things
Alavi, A. and Buttlar, W.G. eds., 2018. Data Analytics for
Smart Cities. CRC Press.
Cooklev, T and et.al., 2018. Enabling RF data analytics
Ghani, N.A and et.al., 2019. Social media big data
analytics: A survey. Computers in Human Behavior, 101,
pp.417-428.
Mayer-Schönberger, V. and Ramge, T., 2018. Reinventing
capitalism in the age of big data. Hachette UK.
Li, J and et.al., 2018. Big data in tourism research: A
literature review. Tourism Management, 68, pp.301-323.
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data
How Big Data technology could
support business & Examples
Big data is an assemblage of data that is enormous in
quantity, still has a potential or power to grow rapidly with
time. It is so vast in size and entangled that none of the
conventional data management techniques and tools can
store it or operate it with efficiency.
Processing Big data carries multiple advantages with it
such as:
•Clear and improved consumer or customer service
•Improved and healthier functional efficiency
•Primal determination of risk to the product or service
•Organizations can utilize external intelligence service
while taking decision.
Techniques that are currently
available to analysis big data
•A/B Testing – This technique helps in comparing the control
group and the test group. In order to determine what changes
brings improvement. Bid data fits in the model and helps to
run test in big data.
•Data Mining – It is most common tool to resource only the
useful data from the huge data and study the data which is
required, saves time and resources and provides results fast.
•Machine Learning – It belongs to the field of artificial
intelligence; it works on computer algorithm to produce
solutions to the problem based on the data available.
•Statistics – The technique used to collect, organize,
interpreted and experiment.