BSc Business Management BMP4005 - Information Systems & Big Data

This report provides an overview of big data, including its characteristics (5 V's), challenges, and available analysis techniques. It discusses data growth issues, lack of understanding, and tool selection problems. Techniques like association rule mining, classification tree analysis, machine learning, social network analysis, sentiment analysis, and regression analysis are explained. The report also provides examples of how Big Data technology supports businesses like Tesco and the hospitality industry. The document is available on Desklib, a platform offering study tools and resources for students.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
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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 analyse big data
p
How Big Data technology could support business, an explanation
with examples p
Poster p
References p
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Introduction
Big data can be defined as collection of data that is huge in volume, and it is
growing exponentially with time. The large size of data is becoming complexity for
the business organizations and the use of dig data analysis help in proper
management of data (Fu, Liu and Srivastava, 2019). This report includes some of the
characteristics of big data to understand the term better. There are certain
challenges related to big data analysis and that are also part of this report. The
techniques that help to manage data are mentioned in the report. At the end there is
discussion related to examples that support the business organizations to manage
their data.
What big data is and the characteristics of big data
Big data is the field that treats ways to analyze and systematically arrange the
data and information to deal with the same. It is use of software that help to manage
the volume of data in proper manner (Cui, Kara and Chan, 2020). It helps to address
the challenges that are being faced by the business organizations. They are able to
tackle the business problems in effective manner. The 5V's of big data helps to
understand its characteristics and they are as follows:
 Volume: The volume of data collected by business organisation is large. The
big organisations collect data from different sources such as social media, IoT
devices, financial transactions, videos, and customer logs (Ristevski and
Chen, 2018). It was problematic for the business organisations to store big
data but with the help of big data analytics they are able to manage and store
the data effectively. The size of data also varies for example size of videos
are in megabytes and on the other side the size of document is in few
kilobytes.
 Variety: The data is available in different variety and it shows another
characteristic of big data. The data is being collected from variety of sources
and that is the reason it becomes difficult to store the data. The data is being
presented in different formats such as PDF, video, audio as well as photos.
 Velocity: The speed at which the data is being generated is known as
velocity. The speed of processing the data is also being checked under this
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characteristic. Checking the speed of processing the information assist in
managing the same.
 Value: There value of data is being determined under this characteristic of big
data analytics. The value of the information is dependent on usefulness and
reliability of the data and information (Bangui and Buhnova, 2018). The data
collected is in the raw form and it is important to filter the same. The task is to
collect the raw data and change it in useful content. There is need to use the
data and information in effective manner.
 Veracity: It can be understood as trustworthiness of the data and information.
It is related with the above characteristic of the company. The information
being collected by the company is unstructured. It becomes necessary to
structure the information in effective manner.
All these are important characteristics of big data and helps to manage the
information properly.
The challenges of big data analytics
There are certain challenges related to big data analytics and they are
mentioned below:
Data growth issues: It is one of the major challenge that is being faced in
regard to big data analytics. The data is growing at rapid speed and it becomes
difficult to store the large size of day (Baig, Shuib and Yadegaridehkordi, 2020). The
large organisations require large amount of data in order to carry on their operations.
The space is limited and the size of data is enhancing. The business organisations
have to take measures to manage the storage and work in effective manner.
Lack of proper understanding of massive data: The business
organisations are not aware about the ways of dealing with the massive data. This
causes problem for the company and may enhance their time in operations. There is
problem of mismanagement of the information. This causes issue in grabbing
relevant information from the raw data.
Lack of knowledge professionals: At the present time there is lack of
professionals who have ability to manage the data. The big organisations are facing
the issue of getting the professionals who can manage their big data. The available
professionals lack knowledge and are unable to deal with the data in proper manner.
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The causes problems for the business organisation and they are unable to take
effective decisions.
Confusion while Big Data Tool selection: It is another issue that is being
faced in data management. The big organisations are unable to deal with and decide
the best available tools that will help them to manage the data and information
(Naeem and et.al., 2022). The company has to evaluate best tools that will help them
to carry on the work properly and get best results. The best selection tool will help to
manage the work in best available manner.
The techniques that are currently available to analyse big
data
There are some of the techniques that are presently available and helps in
analyzing the data and they are mentioned below:
Association rule thumbing: It is a technique that helps to associate
correlation among variable in large database. The company is able to know about
the factors that will help the company to enhance their sales (Ghani and et.al.,
2019). The business organisation is able to know about the touch points and
enhance the experience of the customers.
Classification Tree analysis: It is a technique that is being used to manage
the data. Various factors are being given consideration so that best decision from the
can be taken. The three statistical consideration that are considered under
classification tree analysis are as follows:
 Assign the documents automatically
 Categorisation
 Develop profiles
Machine learning: This technique works with the help of software. The use of
forecasting is done and predictions are made for the success of business
organisation. Afterwards training is given to the employees so that they are able to
achieve goals and objectives successfully.
Social network analysis: This technique is being used mostly by
telecommunication industry as it helps to enhance the relation with the customers
(Huang and et.al., 2018). The area of improvement can be analysed with the help of
this technique and improvement measures can be known.
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Sentiment analysis: This technique is based on the sentiments of the
speakers or writers. It is necessary to make changes as per the the needs and
preferences of the customers.
Regression analysis: This technique is used to how the independent
variable is manipulated. The influence of dependence variable is being examined.
The business organisation works in effective manner and are able to know the effect
of customer satisfaction on the brand loyalty (Hancock and Khoshgoftaar, 2020).
This makes the company strong and they are able to achieve goals and objectives.
How Big Data technology could support business, an
explanation with examples
Big data plays a critical role nowadays. There are certain changes that take
place in the business environment. There is need to work effectively and manage
data in proper manner. The big organizations must analyses the importance of big
data and use them to achieve their goals and objectives properly. For example,
Tesco is a multinational business organization that is using big data in order to take
effective decisions. It helps the company collect relevant information regarding the
customers and use the same to serve the customers in effective manner (Deepa and
et.al., 2022). The use of big data helps them to gain lead over the competitors. It is
helpful for the company and they are able to manage their work in effective manner.
At the same time, the use of big data is done by hospitality industry. It helps
the company to understand the trend and use the same in effective manner. This
helps the company to bring innovation and cater the needs and wants of the
customers in effective manner (Lv and Qiao, 2020). They are able to work as per the
needs and wants of the target customer group and is able to manage the operations
in proper manner.
Conclusion
From the above report, it is analyses that there is key role of managing big
data. It is technique of managing the overall information of the company to take make
effective decisions. The characteristics of big data are important part of this report.
The challenges being faced by the company at the time using big data analytic is
mentioned in the report. There are various techniques that assist in management of
information. In the end the examples of organizations that are using big data are
mentioned in this report.
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Poster
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References
Baig, M.I., Shuib, L. and Yadegaridehkordi, E., 2020. Big data in education: a state
of the art, limitations, and future research directions. International Journal of
Educational Technology in Higher Education, 17(1), pp.1-23.
Cui, Y., Kara, S. and Chan, K.C., 2020. Manufacturing big data ecosystem: A
systematic literature review. Robotics and computer-integrated
Manufacturing, 62, p.101861.
Deepa, N and et.al., 2022. A survey on blockchain for big data: approaches,
opportunities, and future directions. Future Generation Computer Systems.
Fu, W., Liu, S. and Srivastava, G., 2019. Optimization of big data scheduling in
social networks. Entropy, 21(9), p.902.
Ge, M., Bangui, H. and Buhnova, B., 2018. Big data for internet of things: a survey.
Future generation computer systems, 87, pp.601-614.
Ghani, N.A. and et.al., 2019. Social media big data analytics: A survey. Computers in
Human Behavior, 101, pp.417-428.
Hancock, J.T. and Khoshgoftaar, T.M., 2020. CatBoost for big data: an
interdisciplinary review. Journal of big data, 7(1), pp.1-45
Huang, Yand et.al., 2018. Agricultural remote sensing big data: Management and
applications. Journal of Integrative Agriculture, 17(9), pp.1915-1931.
Lv, Z. and Qiao, L., 2020. Analysis of healthcare big data. Future Generation
Computer Systems, 109, pp.103-110.
Naeem, M and et.al., 2022. Trends and future perspective challenges in big data. In
Advances in Intelligent Data Analysis and Applications (pp. 309-325).
Springer, Singapore.
Ristevski, B. and Chen, M., 2018. Big data analytics in medicine and healthcare.
Journal of integrative bioinformatics, 15(3).
<https://www.upgrad.com/blog/characteristics-of-big-data/>
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