BMP4005 Information Systems: Big Data Analysis Techniques & Business

This report provides a comprehensive overview of big data analysis within the context of information systems. It defines big data, outlining its key characteristics such as veracity, variety, value, velocity, volume, and variability. The report identifies several challenges associated with big data analytics, including a lack of technical knowledge, confusion in selecting the right tools, data growth issues, data security concerns, and difficulties in ensuring data quality. Various techniques currently available for analyzing big data are discussed, such as classification tree analysis, association rule learning, machine learning, and Monte Carlo simulation. Furthermore, the report explains how big data technology can support business objectives, providing examples such as Unilever's use of big data for strategic decision-making. The report concludes that effective management and analysis of big data are crucial for organizations to make informed decisions, improve business operations, and achieve future growth.

BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
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Contents
Introduction p
What big data is and the characteristics of big data
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
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Introduction
Big data is a concept of collecting, managing as well as storing large data and
information of an organization effectively to meet the desired goals on time. Nowadays
almost all big firms used and Implement multiple methods and software to manage and
record their big data (Zhao and Guo, 2018). The data are relating to the inventory
transactions, purchase history of customers, financial records, electricity bill and so on
which the management of a company recorded in specific account. All these data are
stored and used for taking effective decision for the development and growth of business.
This project highlights different types of data or understand principles, challenges
as well as techniques of big data analysis. Further, it covers explanation of how big data
can be used to support business objectives addition to functions.
What big data is and the characteristics of big data
Big data includes all those data that are difficult to manage and are too large in
size with traditional approaches of data management. The various characteristics of big
data that are shown below-
Veracity- It refers to the accurateness and truthfulness of the big data. The
organization are needed to collect and record actual data as well as avoid to gather false
and hypothetical data (Lv and Singh, 2021). As information are collected by employee
are used by top management to take effective decision for organization benefits. So, the
data is collected wrong by them that leads to ineffective decisions which will also hinder
the growth of company.
Variety- It refers to the different types data for all the functions that are needed to
collect by an organization. There are various kinds of data like unstructured, structure as
well as semi structured.
Value- It has been termed as importance and significance of data. Organizations
are needed collect and record those data only which is beneficial for them to increase
their profitability and productivity. Most of the firms are invest their resources and time to
collect, store and manage data which will further helps them to manage strong
relationship with their regular customers.
Velocity- It means the speed and time at which organization collect, manage and
store data for the use of their business operations. It is essential for firms to enhance this
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speed addition to manage high speed consistently in order to get valuable information
before competitors (Lee and Huh, 2019). It helps them to adopt recent trends, technology
in their business operation early which increases their brand value and sales.
Volume- It means the size and amount of big data that is determine and collect by
an organization. Generally big data include all those data that are store in large quantity
such as in million terms.
Variability- it refers to the changing nature of big data and information.
Management of an organization can collect and maintain new data in their records time
to time. As well as modify the current data as per their requirements in order to use it for
specific purpose.
The challenges of big data analytics
There are various challenges related to big data which are as follows-
Lack of technical knowledge –Collecting and recording the data needs technical
skills which is challenging for a manager to develop in less time. They are unable to
manage and store data in new software which delay their planning process and work
(Tao, Yang and Feng, 2020). Many company have to organize proper training time to
time to improve the skills of their employees so they adopt new technology in their
working easily. It also helps them to get proper insight about how they classify, manage
and record big data for decision making.
Confusion about the selection of right tool for big data- There are many
techniques and tools which is used by manager to manage big data. Organizations get
confused to apply which technique for the benefit as well as use it for planning. They
needed to examine benefits and drawbacks of big data tools in order to use most
profitable tool which helps them to foster their growth and productivity.
Data growth challenges- This is related to managing big data year to year as it
increases every year which create difficulties for management to keep it for long time.
Data of a company grow frequently which increase complexity, as the result of this
sometimes management forgot to collect and record valuable data which is important or
beneficial for the future growth of company (Lin and Yang, 2019).
Securing data- It is the biggest challenge for company to manage their big data
as well as keep it safe or secure from cybercrime. Keeping big data into various software
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and internet sources will leads to increase the possibilities of data hacking and theft by
hackers of another company. Further, they use it against the them to destroy their image
and brand value in market which decline customer base and profitability also. It is
essential for a firm to secure their valuable data regarding their employee, customers and
organizations information effectively.
Fixing and finding data quality issues – Management of huge data can be
challenging for an organization if the quality of data is poor that means the data that is
recorded and saved by managers are collected incomplete and form wrong source. This
will decline the profitability and productivity of firm due to they make decisions on the
basis of incomplete and wrong information (Chen, Lin and Wu, 2020).
The techniques that are currently available to analyse big data
Classification tree analyses- It refers to the technique where managers
determine that which data or document is belong to which category. As well as by this
they arrange their data as per their nature and classification to use it for future planning.
These techniques automatically classify the data as per it classification and place it in
specific folder. This technique is used to make students profile who enroll themselves in
online courses.
Association rule learning- This technique help the manager to evaluate the
correlations among independent data as well as dependent data. This technique was first
used by retailing industry among their supermarket to examine connections between
product oe services along with interest of customers.
Machine learning- This technique is related with updated and advance software
which is used by big companies to manage their vast data. It helps managers to
determine whether they collecting accurate information form reliable or right source or
not (Xing, Zhang and Zhang, 2022). Also, it helps the organization to differentiate among
spam as well as non-spam mails.
Monte Carlo simulation- This technique and tool used by management to make
models or strategy for decision making for desired result and achievement of goals
effectively. It will enable them to forecast the results and future possibilities by a decision.
Such as an organization decided to expand their business in new country so the
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management can use these techniques to examine many opportunities addition to
threats of expanding their operations.
How Big Data technology could support business, an
explanation with examples
Technologies of big data can support any organization to meet their goals and
objectives for future growth. In recent times all most all businesses use advance and
updated software like cloud computing, cloud storage for storing their big data in
systematic manner. Further, they will classify and record every type of necessary
information under their data base that can be utilized for decision making. Purchase
history of buyers are recorded by managers through software and tools which they
will analyses to determine the credit and debit balance in order to take their money
(Ye, Zheng and Tu, 2020). Moreover, big data technologies help them to predict the
threats or risk associate with decision that are taken for growth of organization.
With the help of technologies and software they can use past data as a base
for the planning for future operations in order to gain good market position. For
example, In Unilever, management use updated software and technologies for
maintaining and recording their big data effectively. They use their data to examine
the benefits and limitations of their planning to expand their business operation in
order to make effective strategies. Also, all these technology helps them to evaluate
their product and services so they decision form which they invest more and dis
invest to increase their profitability.
Conclusion
As per above presented information, it has been concluded that recording and
managing big data is important for an organization in order to make effective
decisions for future growth and development as well as operate business operations
in systematic manner. The multiple characteristics of big data are velocity, volume,
value, variability and so on. Along with that there are various challenges such as
data growth issue, lack of proper skills and knowledge and other which create
difficulty for management to conduct their practices effectively. The various
techniques are classification tree analysis, machine learning many others to manage
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big data. Advance big data technology can support organizations in various manner
by maximizing their profitability and productivity.
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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.
Lee, S. and Huh, J.H., 2019. An effective security measures for nuclear power plant using big
data analysis approach. The Journal of Supercomputing. 75(8). pp.4267-4294.
Lin, H.Y. and Yang, S.Y., 2019. A cloud-based energy data mining information agent system
based on big data analysis technology. Microelectronics Reliability. 97. pp.66-78.
Lv, Z. and Singh, A.K., 2021. Big data analysis of internet of things system. ACM Transactions
on Internet Technology. 21(2). pp.1-15.
Tao, D., Yang, P. and Feng, H., 2020. Utilization of text mining as a big data analysis tool for
food science and nutrition. Comprehensive reviews in food science and food
safety. 19(2). pp.875-894.
Xing, X., Zhang, X. and Zhang, Q., 2022, April. Construction of Intelligent Student
Management Information System Platform Based on Big Data Analysis. In 2022 IEEE
Conference on Image Processing, Electronics and Computers (IPEC) (pp. 1229-1232).
IEEE.
Ye, Z., Zheng, J. and Tu, R., 2020. Network evolution analysis of e-business
entrepreneurship: big data analysis based on taobao intelligent information
system. Information Systems and e-Business Management. 18(4). pp.665-679.
Zhao, J.C. and Guo, J.X., 2018, April. Big data analysis technology application in agricultural
intelligence decision system. In 2018 IEEE 3rd International Conference on Cloud
Computing and Big Data Analysis (ICCCBDA) (pp. 209-212). IEEE.
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Appendix 1: Poster
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