Information Systems and Big Data Analysis - BSc (Hons) Business Management

This report explains big data and its characteristics, challenges in big data analytics, techniques to analyze big data, and how big data technology can support businesses. It is relevant to the course BMP4005 - Information Systems and Big Data Analysis for BSc (Hons) Business Management.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
Submitted by:
Name:
ID:
1

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 analyse big data
p
How Big Data technology could support business, an explanation with
examples p
References p
Appendix 1: Poster p
2

Introduction
Information system is a system that is used by business entities to collect, store
and process its data in an efficient manner by using technological tools (Shi and et.al.,
2020). Through this the information can be provided easily to get knowledge about the
work or customers. The following files based on information system and big data that
companies use in their operations. There are different questions in the related matter that
have been answered in the following file. It starts with explaining what is big data and its
characteristics. Then it moves to the challenges that are faced in big data analytics.
Moving further there are different techniques that are used in analyzing big data. At last
there is the explanation of how big data helps business companies.
What big data is and the characteristics of big data
Data can be said as the collection of information down using technological
innovations such as machines and computer. Big data is nothing different but just the
amount of data is very big. The amount of data is so huge and it keep on getting more
and more with the time. This big data cannot be processed and managed by traditionally
used technological tools. The characteristics of big data have been given in five Vs.
These are mentioned in the following points :
 Volume – The volume refers to the amount of data that has been stored in
the big data. The sources through which data comes can be different such as
– manual entries, social media, physical interactions and business network.
The volume must be analyzed on an average basis to keep and use the
technological tools accordingly. The volume can vary from business to
business and its scale (Madan and Goswami, 2020). For example – Facebook
alone uses more than 500 terabytes of data in a single day whereas some
their company may use only 10 terabytes in their whole month.
 Variety – The variety of the data collected in big data can be different from
companies to companies or segment to segment. There is a diversity and
rage of type of data. Different types of forms in which big data is varied are –
structured, semi structured, quasi-structured and unstructured. Each of these
types are elaborated in the following points :
◦ Structured data – This type of data is characterized in a systematic format
through columns and tables.
◦ Semi structured data – The data is not categorized in a much proper
manner in this variety and online transaction processing systems are
used.
◦ Quasi structured data – This type of data variety contains format of
inconsistent data in text that gets formatted with time and effort.
◦ Unstructured data – This variety is very different from others as it contains
audios, pictures, log files and etc. The data is raw and most of the
companies are unable to device such variety of data.
 Veracity – It is the process of managing data in the most effective and way
and keeping it in a reliable way. It has different types of translating and
filtering data.
 Value – It is the particular type of data that is most valuable for the company.
The valuable data is efficiently processed and analyzed. This is considered as
the most important characteristic of big data. The data of each country is very
3

valuable for them as some other companies that can be its competitors can
use the data against it (Mishra and et,al., 2018).
 Velocity – It is the speed at which data is receive, stored and managed in a
business firm. For example – The number of search enquirers revived within a
day can be a parameter of velocity.
The challenges of big data analytics
The development and practice of big data analytics is not a easy task. The
process of maintaining and installing this data is very complex and sometimes it creates
challenges for personnel or business entities that are needed to be solved to work
efficiently. Some of the major challenges that a company faces in the process of using
big data analytics are mentioned in the following points :
 Lack of knowledge professional – The technological machines and
processes are complex that can not be practiced and maintained by normal
employees and people of the organization. The big data analytics need
professionals that are good in it and have learned it from educational
institutions or some other sources. These data professionals can be known as
– data scientists, data engineers and data analytics. To avoid this problem
companies can either hire professionals can even purchase knowledge
analytics solutions that can even be used by people who have basic
knowledge.
 Data growth issues – The data in the companies keeps on getting more and
more with the time. The data is very important for the companies so task of
managing this increasing amount of data effectively is one of the most
effecting challenge for them (Liu and et.al., 2018). The data may be easy to
handle in the starting but with the time it gets challenging to handle. It is hard
to find some particular data in the large amount of data that concluded
documents, videos, files, audios and etc.
 Confusion with big data tool selection – The big data is a important thing
for business entities to keep the data safe and stocked with them. So it
became a industry in itself and there are a lot of tools that have been made for
the similar work by different companies or individuals. These tools have
difference in them that can be in the level of security and features provided.
The selection of the best type of tool for a company according to its work is a
hard task too. So a company needs to compare the options from the market
and select the most effective one for them according to their work.
The techniques that are currently available to analyse big
data
 A/B testing – This is a test in which diffenret groups are comparied thorugh a
variety of fixed tests. These tests are done to analyse ehat changes can be
made to improve and achive the objectives of the firm (Chai and et.al., 2021).
But this type of testing can only be done when there are big ized groups that
can have meaningful differences in them.
 Classification – This technique is used to classify the data of a company in a
systematic way. The classification can be done through differnet parameteres
4

such as date, topic and may be according to the people who does it. For
example – the amount of people that used happy emoji in their reaction of
other people's post.
 Social network analysis – In this tyechnique proper organizing of human
relations is done in the form of graphs. The reason of using this is to identify
trends from the marketplace. The lines and points that are stored in the matrix
are known as sociomatrix whereas the graphs of those human relations are
known as sociograms (Hou and et.al., 2020).
How Big Data technology could support business, an
explanation with examples
The big data technology helps business entites in many different ways which is
the reason for which their use is rapdily increasing in the current market. Some of the
major reasons for which the technology is used widely are mentioned in the following
points :
 Better customer insight – Throguh these technological tools the company
gets help by getting better and clearer customer insight. These innovations
keep the record of the customers that have been interacted, shopped or
interest in the products / service of the company.
 Personalized marketing – The analytics also helps in doing marketing for
their company and connecting with their target customers (Zhu and et.al.,
2019). The target base of customers are analyzed from the market and mode
of marketing which highly effects them is used.
 Improve business operations – The technological tools helps in decreasing
the man power needed in doing the operations that makes the buisness
operation efficient. The increasing of automation also helps in decreasing the
chances of mistakes.
Conclusions
From the above report it has been concluded that information technology is an
essential thing in the today's world for business firms that helps them in collecting,
storing and using their data effectively. But being a complex activity it has also have
many challenges that are faced by the companies and its related people in managing
the tools and big data.
5

References
Shi, M. and et.al., 2020. A privacy protection method for health care big data
management based on risk access control. Health care management science, 23(3),
pp.427-442.
Madan, S. and Goswami, P., 2020. A privacy preservation model for big data in map-
reduced framework based on k-anonymisation and swarm-based
algorithms. International Journal of Intelligent Engineering Informatics, 8(1), pp.38-
53.
Mishra, D. and et,al., , 2018. Organizational capabilities that enable big data and
predictive analytics diffusion and organizational performance: A resource-based
perspective. Management Decision.
Liu, J. and et.al., 2018, May. The future development of traditional Chinese
medicine from the perspective of artificial intelligence with big data. In 2018 IEEE 4th
International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE
International Conference on High Performance and Smart Computing,(HPSC) and
IEEE International Conference on Intelligent Data and Security (IDS) (pp. 204-209).
IEEE.
Chai, N. and et.al., 2021. Role of BIC (Big Data, IoT, and Cloud) for Smart
Cities. Arabian Journal for Science and Engineering, pp.1-15.
Hou, R. and et.al., 2020. Unstructured big data analysis algorithm and simulation of
Internet of Things based on machine learning. Neural Computing and
Applications, 32(10), pp.5399-5407.
Zhu, Y. and et.al., 2019, July. From data-driven to intelligent-driven: technology
evolution of network security in big data era. In 2019 IEEE 43rd Annual Computer
Software and Applications Conference (COMPSAC) (Vol. 2, pp. 103-109). IEEE.
Appendix 1: Poster
6

7