Business Management BMP4005: Big Data Analysis Report and Poster

This report provides a comprehensive overview of big data analysis in the context of business management. It begins by defining big data and outlining its key characteristics, including volume, velocity, variety, veracity, and value. The report then delves into the challenges associated with big data analytics, such as lack of skilled professionals, data growth issues, and security concerns. It explores various techniques used to analyze big data, including association rule learning, classification trees, genetic algorithms, machine learning, and regression analysis. Furthermore, the report illustrates how big data technology supports businesses, using Tesco as a case study, highlighting its applications in areas such as cost control, supply chain optimization, and trend prediction. The conclusion emphasizes the importance of big data for business growth and its effective management using different techniques. A poster is also included to summarize key points. The report is a student submission available on Desklib, a platform offering AI-based study tools and resources.

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
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Contents
Introduction 3
What big data is and the characteristics of big data 3
The challenges of big data analytics 3
The techniques that are currently available to analyse big data
4
How Big Data technology could support business, an explanation
with examples 5
Poster 6
References 7
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Introduction
Big data is the term which is used to collect, store and manage huge data of
companies in such a manner which will help an organization to utilize the data for the
benefit of companies (Hariri, Fredericks and Bowers, 2019). It is essential for the
companies to manage the data related to their customers, business transactions and
many others effectively. There are various tools and machines which are used to
manage the data of companies effectively. The following report covers description of
big data and its characteristics, challenges of big data analytic, techniques of big
data and the explanation that how a big data will support business with example.
What big data is and the characteristics of big data
Big data is the set of data which are too large in size as well as complex to dealt by
traditional way of data processing technique (Hasan, Popp and Oláh, 2020). There are
five basic characteristics of big data which are mentioned below-
Volume- It consist of unimaginable amount of information which is generated every
second. For example, the total amount of likes and dislikes of company's post on social
media, number of phones calls to a company in different branches, entering and exit of
customers within supermarkets and many others.
Veracity- It means the reliability of data (Müller, Fay and Vom Brocke, 2018). There are
various ways to filter or translate the data. Veracity is the process of managing data
effectively by analyzing their reliability.
Value- It is one of the most essential characteristic of big data where consist that the
companies are only storing those data which are valuable and which provide benefit to
company.
Velocity- It is the speed which the data is created in real time. Collecting data and
managing it is essential and useful only when the process takes fewer time to analyze
the data for business use.
Variety- Big data can be in any form structured, semi-structured or unstructured (Khan
and et. al., 2019). Big data is capable to manage any form of data effectively.
The challenges of big data analytic
There are various kinds of challenges which can be faced by big data and few of the
examples are mentioned below-
Lack of knowledge professionals- To run the machines and software for managing the
big data of companies, they need IT professionals and service providing companies are
commonly focused to hire those employees who are having effective soft skills. Hence,
there are lack of IT professional employees within few of the companies which are not
related to IT sector.
Lack of proper understanding of massive data- Inappropriate knowledge of
employees related to data can also create a big obstacle for the data management (Qi
and Luo, 2020). Most of the employees are not aware about how and when to store
which type of data. Data professionals can understand the situation related to data within
a company but the normal employees who is working in operational department of
retailing sector may ignores to gain the knowledge related to big data and hence, due to
the company is unable to use their data in decision making process.
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Data growth issues- This is one of the biggest issue to manage a huge data set within a
company. There are different ways and techniques which can be used by a company to
manage their data effectively but when a company switches from one technique to
another technique than most of the data get impacted negative.
Securing data- when companies collect and manage huge data by using various
software and tools than there is threat of hacking data by hackers and use it for bad
purpose which impacts negative upon organizational image (Boubiche and et. al., 2018).
Therefore, there is always a questions mark for the companies in securing their data
effectively.
Technical challenges- Some of the tools and devices for managing data can hang or
work improper due to any technical error and this can be impacted upon the efficiency of
company's performance.
The techniques that are currently available to analyses big
data
There are various techniques which are available to analysis big data and few of them
are mentioned below-
Association rule learning- It is the method which help to analyze the correlations
between variables in database. This technique is commonly used by the chains of
supermarkets to analyze the interesting relations between products and overall sales of
the companies. Here, the relationship between two or more variables of data will help the
company to analyze various factors for example, in case a product is selling in larger
quantity and a particular age group of customers is showing interest to purchase that
products then it will help the company to analyze their target customer through this data
variables.
Classification trees analysis- Here, the data is automatically sorted and managed by
the software (Huang and et. al., 2021). For example, if a company is mentioning the data
of their customers then the software will automatically mentioned the name of the
customer in alphabetical order. Hence, it is the method which identifies categories which
is related to new observations.
Genetic algorithms- This is the technique where a data set will help companies or
individuals to resolve their issues and problems related to their professional tasks. It will
help in decision making process to make effective decisions. For example, this technique
of big data is used by doctors in the hospitals to decide their schedule for emergency
rooms.
Machine learning- Here, organizations uses computers and software to manage their
big data (Palanivel and Surianarayanan, 2019). It further help the companies to analyze
their spam and non-spam email messages.
Regression analysis- It is a way of managing big data to analyze that how independent
variable influences a dependent variable within a company (7 Big Data Techniques That
Create Business Value, 2022). For example, a companies independent variable is
background music in retaining store and dependent variable is time spend in store.
Hence, this technique of big data will help to analyze the company that how change in
background music will effect the time spending of employees within the store. For
example, boring musics will influence employees as well as customers to leave as early
as possible from the stores.
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How Big Data technology could support business, an
explanation with examples
Big data technologies will support the growth of a business and help them to enhance
their productivity and profitability (Raguseo, 2018). Tesco is one of the most popular
retailing company of UK which is having a huge number of customer base. Millions of
customers arrive their stores every day and purchase products according to their needs
and wants. Company save the data and manage it effectively for analyzing their growth
speed, analyzing the specific product which contribute more to sales and many others.
For example, Tesco uses big data for different purpose to enhance their business value
like they use it for controlling their lightening and heating cost. It is also used for
improving value chain, the company update their data everyday and make sure that
correct and accurate data will be placed in their data sheet so that they will analyze the
requirement of their inventory in their stores to manage it well for enhancing their
business value. Tesco also uses big data for anticipating the future trends (5 ways Tesco
uses Big data Analytics, 2021). Tesco employees review their data and information on
regular basis so that they can predict the needs and wants of their customers and offer
them the same services and products which they prefer to buy more (Wang, Kung and
Byrd, 2018). It can also used in estimating sales of the mentioned company because
Tesco record all the items sold by their stores hence, total sales revenue can also be
calculated by their big data analytic.
Conclusion
From the above information it is concluded that big data is one of the most important
aspect of any business where a company collect, store, manage and use the information for
the growth of their company. Their are few of the main characteristics of big data like
volume, value, velocity and many others. Companies can also face few challenges for
managing their big data such as lack of knowledge professionals, data growth issues and few
others. Various kinds of techniques of big data will help the companies to manage their data
effectively according to their purpose and objectives.
5

Poster
6

References
Boubiche, S. and et. al., 2018. Big data challenges and data aggregation strategies in
wireless sensor networks. IEEE access, 6, pp.20558-20571.
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.
Hasan, M.M., Popp, J. and Oláh, J., 2020. Current landscape and influence of big data on
finance. Journal of Big Data, 7(1), pp.1-17.
Huang, W. and et. al., 2021. An overview of air quality analysis by big data techniques:
Monitoring, forecasting, and traceability. Information Fusion, 75, pp.28-40.
Khan, N. and et. al., 2019, May. The 51 v's of big data: survey, technologies, characteristics,
opportunities, issues and challenges. In Proceedings of the international conference
on omni-layer intelligent systems (pp. 19-24).
Müller, O., Fay, M. and Vom Brocke, J., 2018. The effect of big data and analytics on firm
performance: An econometric analysis considering industry characteristics. Journal
of Management Information Systems, 35(2), pp.488-509.
Palanivel, K. and Surianarayanan, C., 2019. An approach for prediction of crop yield using
machine learning and big data techniques. International Journal of Computer
Engineering and Technology, 10(3), pp.110-118.
Qi, G.J. and Luo, J., 2020. Small data challenges in big data era: A survey of recent progress
on unsupervised and semi-supervised methods. IEEE Transactions on Pattern
Analysis and Machine Intelligence.
Raguseo, E., 2018. Big data technologies: An empirical investigation on their adoption,
benefits and risks for companies. International Journal of Information
Management, 38(1), pp.187-195.
Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and
potential benefits for healthcare organizations. Technological Forecasting and
Social Change, 126, pp.3-13.
Online-
7 Big Data Techniques That Create Business Value, 2022 [Online] available through:
<https://www.firmex.com/resources/blog/7-big-data-techniques-that-create-business-
value/>
5 ways Tesco uses Big data Analytics, 2021 [Online] available through:
<https://www.analyticssteps.com/blogs/5-ways-tesco-uses-big-data-analytics/>
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