BMP4005: Information Systems and Big Data Analysis in Business

This report provides a comprehensive analysis of big data and its applications in business management, focusing on the characteristics of big data (volume, variety, and velocity), the challenges faced in big data analytics (lack of knowledge professionals, data growth issues, securing data), and the techniques currently available for analyzing big data (data mining, natural language processing, data fusion, A/B testing). It further explains how big data technology can support businesses, using examples like Google and Facebook, by enabling them to extract essential information, enhance profitability, and reach maximum customers, ultimately helping organizations achieve their goals and objectives. Desklib offers a wealth of similar solved assignments and study resources for students.

Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Accompanying Paper
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Contents
Introduction p
What big data is and the characteristics of big data p
The challenges of big data analytics p
The techniques that are currently available to analyze big data
p
How Big Data technology could support business, an explanation with
examples p
References p
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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.
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:
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
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disc. Some internet-connected smart devices function in real-time or near-real-time,
necessitating real-time evaluation and response.
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.
Lack of proper understanding of Massive Data: Companies struggle to succeed in
their Big Data projects due to a lack of knowledge. Employees may not understand what data
is, how it is stored, processed, and where it comes from. Others may not have a clear picture
of what's going on, even if data specialists do. Employees who do not understand the need of
knowledge storage, for example, may not be able to preserve a backup of sensitive material.
They were unable to correctly save data in databases. As a result, when this critical
information is needed, it is difficult to locate.
Data Growth Issues: The correct storage of these vast amounts of knowledge is one
of the most important concerns of big data. The amount of data being saved in data centres
and company databases is continually expanding. It becomes difficult to manage large data
sets as they increase rapidly over time. The majority of the data is unstructured and originates
from a variety of sources, including documents, movies, audio, text files, and other media.
This indicates that they aren't in the database.
Securing Data: One of the greatest issues of enormous data is securing these massive
quantities of knowledge. Companies are frequently so preoccupied with comprehending,
preserving, and analyzing their data sets that data security is pushed to the back burner.
Unprotected data repositories may become breeding grounds for malevolent hackers, thus this
is rarely a wise option. A stolen record or a knowledge breach may cost a company up to $3.7
million.
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Confusion while Big Data Tool selection: Companies frequently become perplexed
while deciding on the most basic instrument for large-scale data analysis and storage.
Companies are bothered by these problems, and they are sometimes unable to find solutions.
They are prone to making bad selections and utilizing ineffective technology. As a result,
resources such as money, time, effort, and work hours are squandered (Kamilaris,
Kartakoullis, and Prenafeta-Boldú, 2017).
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. The techniques that are currently available for the purpose of analyzing big
data are explained below:
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. When consumer data is mined to discover which segments are most
likely to respond to an offer, here is an example.
Natural language processing (NLP): This techniques is also known as subspecialty of
computer science, linguistics and artificial intelligence. This tool uses several algorithm to
understand natural or human language.
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.
Analysis of what content, text, graphics, or style can boost conversion rates on an e-
commerce site is an example given by McKinsey. Big data fits into this paradigm once again
since it can test large numbers (Kamilaris, Kartakoullis, and Prenafeta-Boldú, 2017).
However, this can only be done if the groups are large enough to acquire noticeable
differences.
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
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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).
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References
Books and Journals
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.
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