Information Systems and Big Data Analysis - Desklib

This report explains the meaning and characteristics of big data, challenges faced by companies in big data analytics, techniques to analyze big data, and how big data technology could support businesses with examples. The report also includes references for further reading.

Introduction
In the dynamic environment a business organization
adopt various techniques in order to achieve competitive
advantage and gain success in the market. Big data
analysis is one of the technique that has been used by
business organizations to gain competitive advantage in
the market so that the sales and profitability of the
company can be enhanced (Novak, Bennett, and Kliestik,
2021). This report contains proper analysis and meaning
of big data and its characteristics full stop there are
several challenges that has been faced in case of Big data
analytics which are also included in this report. Further
this report contains several techniques that are used to
analyze big data and the way in which the technology
support businesses in the market.
Information Systems and Big Data Analysis
Name of the Student
The challenges of big data analytics
In order to analyze big data in the market the companies are facing
several challenges that are essential to be managed by them. The
basic challenges that are faced by companies in case of big data
analytics are given below:
Lack of knowledge Professionals: Companies require trained data
specialists to run these latest technology and massive data tools. To
work with the technologies and make sense of massive data sets,
these experts will include data scientists, data analysts, and data
engineers. A shortage of enormous Data specialists is one of the Big
Data Challenges that any company faces (Huerta, and Jensen, 2017).
This is frequently due to the fact that data processing tools have
advanced fast, but most experts have not. To close the gap, concrete
efforts must be done.
Volume: It is important to consider the amount of data available.
An individual is required to analyze a lot of low-density,
unstructured data with big data. Unknown value data, such as
Twitter data feeds, clickstreams on a web page or a mobile app, or
sensor-enabled equipment, are examples. This might be tens of
gigabytes of data for certain businesses. It might be hundreds of
petabytes for others.
Variety: The numerous different sorts of data that are available are
referred to as variety. Traditional data formats were well-structured
and fit into a relational database with ease. With the growth of big
data, new unstructured data kinds have emerged. To infer meaning
and support metadata, unstructured and semi structured data
formats like text, audio, and video require further preprocessing.
Velocity: The pace at which data is received and (perhaps) acted on
is referred to as velocity. In most cases, data is streamed directly
into memory rather than being written to disc. Some internet-
connected smart devices function in real-time or near-real-time,
necessitating real-time evaluation and response.
What big data is and the characteristics of big
data
Big data refers to the data that are available in large
variety and as increasing continuously in their volumes
and velocities. In order to achieve success in the market it
is essential for every organization to analyze and identify
their big data as it is larger and more complex data sets
which helps to collect data from new sources available in
the market. This big data analysis is used by many
business organizations to address various business
problems that are not been taken before by other
business organizations. In order to understand big data
analysis in more depth it is essential the three main
characteristics of a big data analysis (Adams, and Krulicky,
2021). These three main characteristics of big data
analysis are explained below:
Lack of proper understanding of Massive Data: It is
because of lack of knowledge among management in
the companies that the companies struggle to get
succeed in the projects of their big data analysis. There
are so many employees who does not have knowledge
about the usefulness of big data and even they don't
know what data is how it can be processed and stored
and from where it comes from. That is the reason that
employees do not have a clear picture that what will
company do from the data and do not understand the
need of storage of the knowledge they attain from the
market. This lack of knowledge and proper
understanding of message data among employees
make them enable to save the data correctly in data
bases which results in loss of critical information by the
company.
Data Growth Issues: The most important concern that
has been considered in case of big data analysis is its
storage as it is difficult for a company to correctly store
the data in data bases and requires a lot of knowledge.
The growth of time the data growth issues has also
emerged in organizations which become difficult for the
companies to manage such a large data sets. The data
that has been collected by the company is unstructured
and contains a variety of sources that is really difficult
to manage by the employees and other management.
Securing Data: The major issue that has been identified
in case of big data analysis is the security of this large
amount of data in the company. The companies are
always involved in their collection analysis and
preservation of their data to secure them from being
theft in the market.

Confusion while Big Data Tool selection: Companies
frequently become perplexed while deciding on the most
basic instrument for large-scale data analysis and storage.
There are many companies who get stressed by all these
issues and it became difficult for those companies to find
solutions for these issues. It is because the companies
make a bet selections of data and use wrong technology
to collect the effective data for the company. It results in
waste of resources of the company such as money,
efforts, time and working hours of employees in the
organization (Kamilaris, Kartakoullis, and Prenafeta-
Boldú, 2017).
Information Systems and Big Data Analysis
Name of the Student
The techniques that are currently available to analyze big
data
In order to analyze big data there are several techniques
that has been used by organizations. Data mining: Data
mining is a typical approach used in big data analytics to
uncover patterns from massive data sets using a
combination of statistics and machine learning methods
inside database administration. Natural language
processing (NLP): This techniques is also known as
subspecialty of computer science, linguistics and artificial
intelligence.
Data fusion and data integration: The insights are more
efficient and perhaps more accurate than if they were
created from a single source of data by utilizing a
collection of approaches that analyze and integrate data
from numerous sources and solutions.
A/B testing: This data analysis approach compares a
control group against a range of test groups to see which
treatments or adjustments will enhance a particular
objective variable.
How Big Data technology could support business, an explanation
with examples
Big data helps a business organization to reach maximum
customers at a time and provide some important data that will
help the business organizations to enhance their profitability.
Analyses of big data available with the business organizations help
them to keep out the essential information from the data and
make the other wasted so that proper and effective profits can be
earned from the market (Ju, Liu, and Feng, 2018). Taking an
example of Google and Facebook companies who uses large
number of data of their clients for several purposes and became a
larger company in the market. Companies like Google and
Facebook has several users who logged in their accounts with the
help of their several personal information that will help the
company to use effectively in future for advertisement purpose.
The company always ask to on the Gmail notifications and mobile
notifications received from Google and Facebook which helps the
organizations in a large manner and collecting large information
from the market. This information without analyzing is waste for
the business organizations but if this data is analyzed with proper
tools and an effective manner will help the large organizations to
use this big data in effective and efficient manner and can support
business in achieving their goals and objectives (Matsebula, and
Mnkandla, 2017).
References
Novak, A., Bennett, D. and Kliestik, T., 2021. Product
decision-making information systems, real-time sensor
networks, and artificial intelligence-driven big data
analytics in sustainable Industry 4.0. Economics,
Management and Financial Markets, 16(2), pp.62-72.
Adams, D. and Krulicky, T., 2021. Artificial Intelligence-
driven Big Data Analytics, Real-Time Sensor Networks,
and Product Decision-Making Information Systems in
Sustainable Manufacturing Internet of Things. Economics,
Management and Financial Markets, 16(3), pp.81-93.
Huerta, E. and Jensen, S., 2017. An accounting
information systems perspective on data analytics and Big
Data. Journal of information systems, 31(3), pp.101-114.
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F.X.,
2017. A review on the practice of big data analysis in
agriculture. Computers and Electronics in
Agriculture, 143, pp.23-37.
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F.X.,
2017. A review on the practice of big data analysis in
agriculture. Computers and Electronics in
Agriculture, 143, pp.23-37.
Ju, J., Liu, L. and Feng, Y., 2018. Citizen-centered big data
analysis-driven governance intelligence framework for
smart cities. Telecommunications Policy, 42(10), pp.881-
896.
Matsebula, F. and Mnkandla, E., 2017, September. A big
data architecture for learning analytics in higher
education. In 2017 IEEE AFRICON (pp. 951-956). IEEE.