BSc (Hons) Business Management BMP4005 Big Data Analysis Report

This report provides a comprehensive analysis of big data within the context of business management, focusing on its characteristics, challenges, and available analysis techniques. It begins by defining big data and detailing its five key characteristics: volume, veracity, variety, value, and velocity. The report then explores the challenges associated with big data analytics, including data scarcity, outdated approaches, poor data quality, system defects, messy data, security concerns, high costs, and a shortage of skilled experts. Various techniques for analyzing big data are discussed, such as data integration, A/B testing, statistics, data mining, machine learning, and natural language processing (NLP). Furthermore, the report elucidates how big data technology can support businesses by reducing costs, increasing revenue, enhancing pricing decisions, creating competitive advantages, improving decision-making efficiency, and detecting fraud. The report also briefly touches upon the history of big data and includes a poster presentation on the topic, concluding with a list of references.

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

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

Introduction
Big data refers to togetherness of semi-structured, unstructured and structured collected by the
organisation that can be helpful in machine learning task, advanced analytic model, machine learning
and predictive model. It offers effective competitive advantage to company or organisation as it
ensure faster organisational activities and instant decision making. .Saggi and Jain, 2018.)The report
will cover discussion about big data along with its challenges to maintain the big data. In addition to
this, the tools present for measuring the big data as well as big data asses to organisational business.
What big data is and the characteristics of big data
The big data is a data that contain the large verity, more volume and more velocity. Big data is
large in numerous that can process in application and software so that company easily address the
issues and mange the threat, The big data is an important for company's operational task, create
personalized marketing campaigns and other actions. The big data divided into five characteristics
that will mention below.
Volume: The big data is a wide in volume which generate from many sources such as human
interaction, business process, network, platforms and social media. For example, the Instagram can
generate billions conversation, millions new post and billions times likes are transferred every day.
Veracity: The veracity in big data that define data is more genuine and reliable so that it can
transfer and filtered the informative data.
(Moharm and et.al., 2018)( The veracity is a process in which manger and expert able to manger and
handle the data easily as it is an important for company's growth. For instance, the post upload in
Instagram with hashtags.
Variety: The big data is explained in many form such as structured, semi-structured, quasi-
structure and unstructured which are gather from many sources namely photos, video, SMS post,
email and audio etc.
Value: The value is an essential for big data as it is a precious or valuable data that
organisation analyses, store and process.
Velocity: The velocity in big data creates the speed in informative data in real-time. It
involves the speed of change, data set speed and connecting the incoming activity. (Soares and et.al.,
2021.)The big data velocity manage the speed in the sending from many sources such as sensors,
network, business, mobile device and application logs.
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The challenges of big data analytics
The international companies are exploiting big data analytics that improve the decision
making quality. Even so, the organisations experience difficulties in managing the big data that will
mention below.
Lack of data: The organisations have not adequate source to create new sights because lack
of integration or poor data organising. (Mohandu and Kubendiran, 2021)The small enterprise face
difficulties to identify the different roots of informative data as it may has not professional employees
to mange the data.
Old approaches: The companies face complexity in managing the big data as it require
advanced tools and applications. The start up and small organisation have not advanced system to
transfer the complex data.
Poor quality: Some time company may fail to make decision effectively due to poor quality
and insufficient data source as the organisation's crucial decisions depend on informative data
System defect: The organisation experience challenges due to system defects as it may
contain errors which intervene the company's growth, testing process and verification. Li and
et.al.,2018)In currently, the international organisation are using best quality testing system which
reduce the number of problems.
Messy data: The big data contain wide range of variety that are more typical and complex to
understand because it include unorganized and messy form that create challenges to extract the
crucial data. This issues can be resolved by UX/UI professional that will help to top level manger for
managing the big data.
Securing data: The organisation mainly aim at understanding, storing and analysing the
informative data rather than securing the big data from unethical hacking and misuse of customer's
data.
Expensive: The multi nation companies face difficulty in manage the big data as it require
huge amount of capital for investment in big data. The existing employees have not enough
knowledge of big data so that company hire professional experts to manage the big data.
Shortage of experts: The effective big data process depend on professional employees and
expert as sometime company may fail to recruit the professional expert due to shortage of big data
experts
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The techniques that are currently available to analyse big data
Data integration and data fusion: The organisation analysis as well as integrate data from
many sources in this technique. The insights are more correct and efficient than the single source of
big data.
A/B testing: The organisation compares a control group with numerous test groups so that
identify what change or modification will enhance given goals. Ghani and et.al., 2019.)(Big data
again set into this model because this model examine variety numbers of big data. Yet, it can only
accomplish if the segments are big in size to enhance the value of deviation.
Statistics: In this techniques, the company organise, collect and interpret data from
experiments and surveys. The company manage, process and measure this data are entirely expensive
and many field that similarity evolves over time.
Data mining: The organisation generally exploit this techniques in big data because it pull out
figure and symbols from big data fix by united methods from statistic and machine .
Machine learning: In competitive world, the organisations highly depend on artificial
intelligence through machine learning techniques for managing the big data.(Coad and Srhoj, 2020)
( It operate with computer based formula to render assumption that depend on data and it help in
forecasting that would be impossible for human
Natural language processing (NLP): In this techniques, the company uses formula to
understand the human language with help of linguistics, artificial intelligence and computer science.
How Big Data technology could support business, an explanation with
examples
Reduce cost: The organisation easily reduce operational cost because it enhance the
operational performance so that they can resolve the problems and reduce the unnecessary effort.
For instance, the informative data shown that consumer have low interest in buying the gift wrapper
so that company close the gift-wrappers manufacture in their business.
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Increase revenue: The organisation can acquire the important insights into feeling as well as
costumer's behaviours and taste. The organisation create the product and service in order to offering
to customers according to their likes with help of big data.
Enhance pricing decision: The organisation determine the effective pricing strategy with
help of big data as organisation compare the different pricing strategy with competitors. For example,
TESCO increase and decease their commodities 's price as well as measuring their product and
service from its rivalry company such as Morrison's, Sainsburry 's and ASDA.
Competitive advantage: The organisation aim at native customers, local markets and provide
insight on consumer's behaviours. (Chen, Lin and Wu, 2020.)For example TESCO, the receptive
company analysis the consumer's like and dislike with the help of bid data and enjoy the
competitive advantage.
Improve efficiency In decision making: The organisational success depend on effective
decision making as mangers examine the social media data in which involves promotion and
behaviour. For example, TESCO 's managers examine the social media user account in Facebook,
Instagram, twitter and LinkedIn so that they make effective decision through bid data analysis.
Fraud detection: The organisation examine fraud activities with the help of big data analysis
as it use high artificial learning and machine learning to notice anomalies patterns. This methods is
an important for indicating something not match that given instruction. It basically used in financial
sector to analysis the fraud activities and help to maintain the accuracy.
HISTORY
The first idea of big data was viewed in 1663 when Johan Grault coped with big amount of
information. He was the first person to make the usage of statistical data analysis. He has worked on
accounting principles to invent and enhance the accessibility but he not gained extra ordinary.
Companies and individual understood about much data in 2005 that generated by user's accounts such
as Facebook, Orkut and You Tube etc. so that Hadoop was formed in that year. (Bansal, Chana and
Clarke, 2020)The Hadoop was needful instrument to measure, keep the big data safe and maintain the
big data at nominal price. Cloud computing has expanded the big data prospect as it offer flexibility
where expert can speed up ad hoc to analysis the data along with graph data bases and it has
capability to screening huge amount of data that make fast analytical and understandable
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Poster
(Covered in PPT)
References
Book and Journals
Bansal, M., Chana, I. and Clarke, S., 2020. A survey on iot big data: current status, 13 v’s challenges,
and future directions. ACM Computing Surveys (CSUR), 53(6), pp.1-59.
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.
Ghani, N.A. And et.al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Li, J. and et.al.,2018. Big data in tourism research: A literature review. Tourism Management, 68,
pp.301-323.
Mohandu, A. and Kubendiran, M., 2021. Survey on big data techniques in intelligent transportation
system (its). Materials Today: Proceedings, 47, pp.8-17.
Moharm, K.I., and et.al., 2018. Big data in ITS: Concept, case studies, opportunities, and
challenges. IEEE Transactions on Intelligent Transportation Systems, 20(8), pp.3189-3194.
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to big insights
for value-creation. Information Processing & Management, 54(5), pp.758-790.
Soares, R.V. And et.al., 2021. Handling big models and big data sets in history-matching problems
through an adaptive local analysis scheme. SPE Journal, 26(02), pp.973-992.
Young, J.L., 2018. The long history of big data in psychology. The American Journal of
Psychology, 131(4), pp.477-482.
Zhang, N. and et.al., 2018. Synergy of big data and 5G wireless networks: opportunities, approaches,
and challenges. IEEE Wireless Communications, 25(1), pp.12-18.
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