BSc Business Management BMP4005 Information Systems Big Data Report

This report provides a comprehensive overview of big data, including its definition, characteristics (volume, variety, velocity, variability), and the challenges associated with its analysis. It explores various techniques available for analyzing big data, such as A/B testing, machine learning, statistics, and natural language processing. Furthermore, the report highlights how big data technology can support businesses by enhancing customer acquisition and retention, as well as improving risk management strategies, with relevant examples. The report concludes by emphasizing the importance of big data for businesses and the need to address its associated challenges to leverage its full potential.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
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Contents
Introduction 3
What big data is and the characteristics of big data 3
The challenges of big data analytics 4
The techniques that are currently available to analyse big data
4
How Big Data technology could support business, an explanation
with examples 5
Conclusion 6
References 7
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Introduction
Big data is a large volume data which is not simple to manage. It can be both structured and
unstructured that deluge organisations on a regular business. Big data is useful to examine
insights that works for the attainment of better decision-making for the next move of
companies. It is complex in nature (Aversa, Hernandez and Doherty, 2021). In the recent
times, the concept of big data has reached a momentum in order to access and store big
volume of information for analytics. In the following assignment, an explanation on big data
with its features is discussed. There are many challenges involved in the big data analytics is
also necessary to understand. Moreover, the key techniques which are present to analyse
big data is explained. Lastly, it covers the point which tells how big data technology could
assist organisations.
What big data is and the characteristics of big data
Big data can be understood by a combination of information gathered by businesses in
order to make it precise for useful information and utilised in predictive modelling, machine
learning projects and other modern analytics applications. It can be structured, semi-
structured and unstructured data. It is high in volume, hitherto thriving exponentially with
time. As it is high in volume and involves complexity that none of the old tools of data
management can process it or store it in an efficient way. It contains big amount of data,
social media analytics, capabilities of managing data and real time data. Big data analytics
can be explained as the process of analysing huge volume of data (Castle, Doornik and
Hendry, 2021). There present big volume of heterogeneous digital data. It is all about data
size and large data sets' plumbed in a way of petabytes and terabytes. This concept is all
about Big Data. The data announced as the Big Data analytics when big data is examined.
New York Stock Exchange (NYSE),mobile apps and social media such as Facebook are few
examples of Big data (What is Big Data? Introduction, Types, Characteristics, Examples,
2022).
Characteristics of Big Data
 Volume: Volume of data plays a pivotal role in deciding value out of data. Volume is
one of the main features that required to be ascertained while challenging big data
solutions.
 Variety: It relates with the nature of data or heterogeneous sources of data. It is
structured, semi-structured and unstructured data. Traditionally, the sources of data
were only databases and spreadsheets, but now a days it can be in the form of
photos, emails, PDFs, monitoring devices, audio-visual and many more (Galetsi and
Katsaliaki, 2020). All such are being considered in the applications of analytics.
 Velocity: It relates with the generation speed of data which means how data is
created and processed to fulfil the demands. It also determines actual potential in
the information. The data flows in from the sources such as application logs, social
media sites, business processes, sensors, networks, mobile devices and many more.
The velocity is continuous and massive in nature.
 Variability: It relates with the inconsistency which is present by the data most
often. Thence, shackling the process of being able to manage and tackle the
data in an effective manner.
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The challenges of big data analytics
Big data challenges contains the right way of tackling the big volume of data that covers the
process of storing, examination the big volume of information on several data stores. In
context of big data, the following are some challenges that come into to way while handling
big data:
 Lack of knowledge professionals: With an intent to run advanced technologies and
big data tools into the company, they require skilled data experts (Pei, 2020). These
experts might require data analysts, data scientists and data engineers to handle and
work with such tools and make utilisation of giant sets of data. Hence, one of the big
challenges that any business may face is drag of lack of big data experts.
 Lack of proper knowledge of massive data: Due to insufficient understanding, there
are many organisations who fails in there massive data initiatives. Employees and
workers might not understand what data is, its processing, storing, significance and
sources. Somehow, massive data professionals may understand what is happening,
but others in business might not have a clear picture of it (Ghasemaghaei, 2020).
Suppose, if employees not getting the point of massive data storage or processing,
they could not be able to maintain the backup of erogenous data. As an outcome,
when such knowledge is needed, then it can not be retrieved easily.
 Integrating data from a spread of sources: In organisations, data is generated from
many sources such as ERP applications, social media pages, financial reports,
customer logs, reports made by workers, presentations, emails and many more.
Therefore, integrating all such data into well-organised reports may be a challenging
activity. Combining data is crucial for examination, reportage and business
intelligence.
 Data growth issues: Big data storing is also one of the major challenges. The data
volume being stored in databases and data centres of organisations is booming
rapidly. With times, these data increases exponentially and becomes more complex
to manage. Most of the knowledge is assembled through videos, text files,
documents, audios and many other sources (Hassan and et.al., 2020). It reflects that
one cannot gather information in databases.
The techniques that are currently available to analyse big
data
The following are some techniques to analyse massive data:
 A/B testing: It is a useful data analysis technique which emphasis on the comparing a
control group with a range groups of test. It is done to recognize what changes or
treatments will enhance a provided objective variable. With an example of
McKinsey, it analyses what text, copy, layout, images will increase conversion rates
on a digital sites. Big data comforts in this model as it can analyse big data,
nevertheless, it can only attained if the groups are of a massive enough size to
attempt vital differences.
 Machine learning: In the times of artificial intelligence, machine learning is also
exploited for analysing data. By developing computer science, it deals with computer
algorithms to create assumptions based on the knowledge (Majeed and et.al., 2021).
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It is useful as it indulge predictions that would be unfeasible for human data
professionals.
 Statistics: This technique of massive data analysis deals to gather, organise and
explicate data within experiments and surveys.
 Natural language processing: It is recognised as a sub-speciality of computer
science, linguistics and artificial intelligence. This NLP tool utilises algorithms to
examine human language.
How Big Data technology could support business, an
explanation with examples
As the big data analysis helps in the analysation and examination of data on a larger scale,
companies needs to acknowledge the consumer behaviour patterns to satisfy their needs as
per their choice and big data analytics indulge them with the same. In the following manner
the big data analytics could help companies:
 It could help company to increase customer acquisition and retention: Whether an
organisation is established or developing, customers are important for them as it
claims success to the company (Hernández and et.al., 2020). Big data analytics
provides support to the company to understand their customer's behaviour.
Through big data, organisations are able to find out the several patterns and trends
of customer. By observing the behaviour of customers, it enables businesses to
trigger loyalty of them. Big data analytics indulges massive information about the
customers of company which is used to acquire and retain customers towards their
brand. For example, big data analytics facilitate information on what certain
consumers are most interested in and that data is used by the company to target
them with more certainty in their emails campaigns or any other campaigns.
 Big data analytics also helps in risk management: Big data analytics immensely
support businesses to create risk management solutions. In order to create and
make an effective strategy for the businesses or to cope up with the situation, big
data analytics acts as a supporter that helps to find out the possible risks and make
plans accordingly. Most important part of this is that, both risks and solutions are
done from the massive data collected. For example, the situation of Covid-19
pandemic, it creates a risk for many businesses and ask for creating better risk
management (Li, Hu and Li, 2020). Big data indulges a potential risk management
plan and helps in reducing or minimizing the negative impact of it. It helps
businesses to remain lucrative in their zone.
Conclusion
From the above report, it can be said that big data is important for the businesses. It
provides a big and massive information to the businesses that are useful to them. It can be
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in any form, structured or unstructured and have complexity in nature. There are various
tools that are available for the businesses to analyse the massive information or knowledge.
Somehow, big data involves challenges that cannot be neglected and are discussed earlier in
the assignment. Big data analytics supports companies in an immense manner.
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References
Books and Journals:
Aversa, J., Hernandez, T. and Doherty, S., 2021. Incorporating big data within retail
organizations: A case study approach. Journal of retailing and consumer
services, 60, p.102447.
Castle, J.L., Doornik, J.A. and Hendry, D.F., 2021. Modelling non-stationary ‘big
data’. International Journal of Forecasting, 37(4), pp.1556-1575.
Galetsi, P. and Katsaliaki, K., 2020. A review of the literature on big data analytics in
healthcare. Journal of the Operational Research Society, 71(10), pp.1511-1529.
Ghasemaghaei, M., 2020. The role of positive and negative valence factors on the impact of
bigness of data on big data analytics usage. International Journal of Information
Management, 50, pp.395-404.
Hassan, M.M. and et.al., 2020. A hybrid deep learning model for efficient intrusion detection
in big data environment. Information Sciences, 513, pp.386-396.
Hernández, G. and et.al., 2020. Hybrid neural networks for big data
classification. Neurocomputing, 390, pp.327-340.
Li, H., Hu, M. and Li, G., 2020. Forecasting tourism demand with multisource big
data. Annals of Tourism Research, 83, p.102912.
Majeed, A. and et.al., 2021. A big data-driven framework for sustainable and smart additive
manufacturing. Robotics and Computer-Integrated Manufacturing, 67, p.102026.
Pei, J., 2020. Big data mining in the control of epidemic. Basic and Clinical Pharmacology and
Toxicology, pp.429-430.
Online:
What is Big Data? Introduction, Types, Characteristics, Examples, 2022. [Online] Available
Through: <https://www.guru99.com/what-is-big-data.html>
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