Big Data Analysis: Techniques, Challenges, and Business Support

This report explains the concept of big data, its characteristics, challenges, and techniques for analysis. It also explores how big data technology can support businesses with effective decision-making. The report includes a poster and accompanying paper on the topic of big data analysis.

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
1

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
5
How Big Data technology could support business, an explanation
with examples 6
Poster 9
References 10
2

Introduction
Big data refers to those data set which are huge in number and tough to manage
with traditional manner of data management (Deepa and et. al., 2022). These data
can be structured, unstructured and semi- structured. It is essential for every
organisation to manage their big data effectively for effective functioning of their
organisation. In the year 1990's big data was first used by John R. Mashey who is
also known as father of big data. The following report consist of explanation of big
data, characteristics of big data, challenges and techniques to manage big data. This
report also consist of the ways in which big data will support business.
What big data is and the characteristics of big data
Big data refers to collecting, storing and using those data sets which are huge in
numerical quantity (Hariri, Fredericks and Bowers, 2019). There are few
characteristics of big data which is explained below-
 Volume- It consist of size of big data which is managed by companies. It is
considered that big data are in huge number. Hence, that's why companies
use updated software to manage their millions of data effectively.
 Value- It consist of that how important is the data for the company to enhance
their productivity and operational performance. The company is required to
analyze that which data will help them to grow and which will not so that they
will only collect and manage those data which are essential for them.
 Variety- Big data is available in various types such as structured,
unstructured and semi- structured. It is essential for a company to analyze
that which data type will help them to achieve their organizational goal
effectively.
 Velocity- It consist of the speed at which company collect and manage their
data effectively (Ghasemaghaei, 2021). It is considered that the company
must maintain their high speed of collecting and managing data for making
effective decision. Slow speed of collecting data will impact their overall
performance.
3

 Veracity- It consist of collecting true and fair data which means a company is
required to store those data which is actually occurred. The collection of
hypothetical data will impacts negative upon overall performance of company.
 Variability- It consist of changing nature of data. The company must be ready
to modify their data in case they found any change in big data.
The challenges of big data analytics
there are various challenges of big data analytics which are mentioned below-
 Lack of knowledge professional- The companies are required to use high
quality software and tools to manage their big data (Hasanin and et. al.,
2019). Hence, their normal employees are unable to run such software and
tools. Therefore, it can be run by only data professionals such as data
scientists and data engineers. Hence, a company is required to hire new
professionals if they started using big data concept within their organisation.
 Lack of proper understanding of massive data- Sometimes company
made wrong decision due to collected wrong data and this happen because
employees have no proper understanding that which data is important for their
organizational growth and which is not. Hence, proper training to employees
to understand the importance of big data will help the companies to mitigate
this challenge.
 Confusion while big data tool selection- There are various tools used by
various kinds of companies to manage their big data. Hence, a company can
get confused that which technique and tool they will select to manage their big
data effectively for smooth running of their organisation (Chen, Lin and Wu,
2020). The companies can easily mitigate this issue by analyzing advantages
and disadvantages of each tool and then select that tool only which will
provide them maximum benefit related to their business objective.
 Integrating data from a spread of sources- There are various kinds of
sources from which a company will receive their data like, social media
platforms, customers logs, financial reports and many others. It is not
necessary that all sources of data are true. Sometimes, company collect data
4

from wrong source due to which they have to suffer in degradation of their
organizational performance.
 Securing data- The big data have the risk to get hacked by hackers. Hence,
it is essential for the companies to focus on securing their big data effectively.
They are required to use updated version of the software which they use to
mange their big data.
 Expensive- It is considered that big data is expensive because for managing
big data some special kinds of tools and machines as well as software are
required by the company which are expensive to purchase. Secondly, the
software used by companies to manage their big data are required to get
updated on regular basis which further need additional charges for the
company. Hence, overall the management of big data is expensive.
The techniques that are currently available to analyse big
data
 Association learning- This is the method for analyzing correlations between
variables in database (Naqvi and et. al., 2021). This technique is used by
chain of supermarkets to analyze the relation between products and
customers purchase activity. This technique will help the company to analyze
the total number of visitors on their website page.
 Classification tree analysis- It is the technique of big data which is used to
analyze the category of the data. For example, if the data is related to product
then it will automatically filled in product list of file and if the data is related to
financial transactions then it will automatically filled in the files of finance.
Hence, this technique will categorize the data and help the company to
maintain their data in effective manner.
 Genetic algorithm- This is one of the most useful technique of big data which
is used to solve any specific problem related to management of data and it
especially used to solve those problems that require optimization. For
example, if a Doctor need to schedule his time for emergency rooms then he
use this technique.
5

 Machine learning- It consist of software which can learn from big data. It will
help the computers to learn without being explicitly programmed (Coad and
Srhoj, 2020). It will help the company to identify spam and non- spam e-
mails which they receive. Hence, it will only allow those data which are
authorized.
 Regression analysis- This technique is used to identify the relationship
between two variables especially dependent variable and independent
variable. It helps to identify how the value of dependent variable
changes in regard of changing independent variable. For example, a
company can analyze how the level of customer satisfaction affects
customer loyalty.
How Big Data technology could support business, an
explanation with examples
Big data technologies can support the business in effective manner because it will
help them to manage their huge data set in appropriate way. For example, Morrison
which is a popular retailing company within UK are using various kinds of tools and
technology to record and manage the data like about their visitors, their purchase
records, their personal details like e-mail, contact number and many others (Delanoy
and Kasztelnik, 2020). Once they record the e-mail address of their customers then
they share information about discounts offers and information of new product launch
so that their customers will get aware about all such information and create interest
to purchase those items. Hence, big data technology will also help the company to
aware their customers and attract them toward their stores and companies.
On the other hand, Morrison's tools and technology of big data management also
help them to analyze that on which product they can invest more. For example, they
record the data of customers like what they are purchasing at what time and in how
much quantity. Hence, this can be help to analyze that which product is more
consumed by their customers from their stores so that they will make sure that they
6

will invest high to maintain inventory of those products which are commonly
purchased by customers.
The big data technology will also help to make decision because the data which are
stored by company are evaluated and reviewed by business executives to analyze
where they are lacking behind to achieve their business objectives and what actions
they can perform to mitigate the risks which they face.
7

Conclusion
From the above information it is concluded that big data is refers to those
data sets which are huge in quantity and which are tough to manage by
traditional manner and outdated software. There are various
characteristics of big data such as volume, variety, value, veracity and
many others. There are various challenges which can be faced by
companies to manage their big data effectively like lack of professionals,
data security and many others. It is also concluded that there are various
techniques of big data management such as regressions analysis, genetic
algorithm, machine learning and many more. Big data technology is also
used to support business and their effective growth by making effective
decision.
8

Poster
9

References
Chen, P.T., Lin, C.L. and Wu, W.N., 2020. Big data management in healthcare:
Adoption challenges and implications. International Journal of Information
Management, 53, p.102078.
Coad, A. and Srhoj, S., 2020. Catching Gazelles with a Lasso: Big data techniques
for the prediction of high-growth firms. Small Business Economics, 55(3),
pp.541-565.
Deepa, N. and et. al., 2022. A survey on blockchain for big data: approaches,
opportunities, and future directions. Future Generation Computer Systems.
Delanoy, N. and Kasztelnik, K., 2020. Business open big data analytics to support
innovative leadership and management decision in Canada. Business Ethics
and Leadership, 4(2), pp.56-74.
Ghasemaghaei, M., 2021. Understanding the impact of big data on firm
performance: The necessity of conceptually differentiating among big data
characteristics. International Journal of Information Management, 57,
p.102055.
Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data
analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1),
pp.1-16.
Hasanin, T. and et. al., 2019. Severely imbalanced big data challenges: investigating
data sampling approaches. Journal of Big Data, 6(1), pp.1-25.
Naqvi, R. and et. al., 2021, June. The nexus between big data and decision-making:
A study of big data techniques and technologies. In The International
Conference on Artificial Intelligence and Computer Vision (pp. 838-853).
Springer, Cham.
10