BSc BMP4005: Big Data Analysis for Business Management (Hons)

This report provides an overview of big data, its characteristics (volume, variety, velocity, and variability), and the challenges organizations face when analyzing it, including synchronization issues, a shortage of professionals, data storage problems, and a lack of understanding among employees. It explores various techniques available for big data analysis, such as classification tree analysis, association rule mining, machine learning, and social network analysis. The report further explains how big data technology can support business by improving sales, profitability, and demand forecasting. It concludes by emphasizing the importance of understanding big data technology for achieving organizational goals and objectives.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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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
Conclusion
Poster p
References p
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Introduction
Big data is defined as the structured, unstructured and semi structured data
that collected by organisation (Surbakti and et.al., 2020). Companies are collecting
data and information so that they can be used in the future projects and decision
making process. It is used in many areas such as predictive modelling, machine
learning projects, and analytics applications. But it is not an easy process for
organisation to analyse big data. This report will be discussing characteristics of big
data and challenges of big data analytics. It will also discussing the techniques that
used for the purpose of analysing big data.
What big data is and the characteristics of big data
Big data is defined as the collection of data in large volume which is growing
time by time (Karau, H., and et.al., 2015). It is identified that there is no traditional
data management tools that is able to store the data in an effective way (Willems
and et.al., 2019). The reason behind this is big data is in huge size which are not
store in traditional tools and techniques. There are some characteristics of big data
are mentioned below:
Volume: It is very important for an organisation that they should understand
the characteristics of big data. It is found that big data has huge volume that means
they are big in size which are not easy to analyse by company (What is BIG DATA?
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Introduction, Types, Characteristics and Examples, 2021). It is important to consider
this characteristic in dealing with the Big data solutions.
Variety: It is concerned with nature of data and heterogeneous sources of
data. There are generally two type of big data found such as unstructured and
structured (Japkowicz and Stefanowski, 2016). It is identified in earlier times that
there are only two sources from which an organisation is collecting data that are
spreadsheets and databases. But after the technology updates, now there are many
sources of data available such as photos, emails, videos, PDFs, monitoring devices,
etc. It is identified that companies are facing problem in storing of unstructured data
variety.
Velocity: It is related to the speed of generation of data. It means that how
data is generate and process in order to meet the demands is comes in velocity. It is
found that the data is massive and the flow is continuous (Broeders and et.al., 2017).
It deals with the speed at which data is flow from various sources such as application
logs, business processes, social media sites, networks, sensors, etc.
Variability: It is concerned with the inconsistency in the data (Daily and
Peterson, 2017). It is necessary for an organisation to hamper the process so that
they are able to manage and handle the data effectively.
The challenges of big data analytics
It is identified that there are many challenges that faced by organisations in
order to deal with the big data analytics (Lee, 2018). It is crucial to identify all
challenges so that they will be able to formulate strategies in order to deal with the
challenges. There are some challenges of big data analytics are mentioned below:
Need For Synchronization Across Disparate Data Sources
It is found that data sets are in big size and more diverse. Due to this, it
becomes challenging for an organisation to incorporate all the data sets into an
analytical platform. If it is identified that they are nit effectively incorporate then it will
create gaps and leads to wrong information.
Shortage of professionals
In organisations, it is found that there are lack of professionals who are able
to analyse all the data (Bag and et.al., 2020). It is crucial for organisations to hire
professionals who analyse and store all the data in an effective way. But in current
scenario, companies are facing shortages of professionals in the market.
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Data Storage And Quality
Big data are in big sizes and due to this companies are unable to store the
data in an effective way as they have not enough storage capacity. Data are in huge
volume which have many chances to reduce the quality of data. Every day the data
is increasing which needs to be store in a space. But it is found that store is less
than data size. This will create problem of storage capacity which is increasing
everyday.
Lack of proper understanding of massive data
The data is in big sizes and due to this, there area many employees who are
not understanding the use of big data (Zhang and et.al., 2019). They are unable to
use in their organisation as they are facing difficulty in order to handle and manage
all data. It is found that organisations are unable to take big data initiatives because
of insufficient understanding. It is very obvious that it is not necessary that all
employees have knowledge about the use of big data and understanding of their
importance. This will results in not effective management of all big data in their
workplaces. They will not be take benefits from the use of big data as they have no
idea that how big data is used in the organisational practices.
The techniques that are currently available to analyse big
data
After analysing of all challenges in big data analytics, now company needs to
identify and evaluate each techniques that helps and support organisation to analyse
big data. There are many techniques which helps an organisation to analyse the big
data which are discussed below:
Classification Tree analysis
This is the technique in which an organisation is categorised data into small
groups with similar characteristics. They make a tree after categorisation so that
they can easily analyse the data effectively.
Association rule thumbing
It is the method that is concerned with discovering of interesting correlation
between different variables in large database (Poel, Meyer and Schroeder, 2018). It
is used in order to help the right placing of products in better proximity that assists in
increasing sales. It also extracts information from the visitor page with the assistance
of web servers logs.
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Machine learning
It is including many software that assists in learning from data. Every
organisation needs this type of software so that they are able to give guidance to the
employees in order to learn the use of data. They are providing computers that have
an ability to learn from data.
Social network analysis
This technique is used first time in telecommunications industry and after that
it is adopted by sociologists in order to study interpersonal relationships. Now this
analysis is used for the purpose of analysing the relationships between people.
How Big Data technology could support business, an
explanation with examples
It is crucial for an organisation to analyse the importance of big data
technology so that they can able to take advantages from big data. Big data
technology is supporting business in order to increase sales and profitability. It is
very important for an organisation to understand the use of big data as there are
many benefits that they can enjoy if company knows their uses. It is found that it
supports an organisation in order to make the predictions related to demand
forecasting. This can be better understand with the help of an example. For
example, an organisation is collecting data from the social media platforms of
customers. They analyse that what are the tastes and preferences of customers by
analysing collected data. They analyse customer behaviour with the assistance of
their data which they collect. This will assists them to take a decision related to the
product or service portfolio. This will also helps them to reduce the chances of
product failure in the market. They can use big data in order to understand the new
market behaviour so that they can make strategies accordingly. There are many
more examples found in the real life situation where an organisation gets support
from big data technology.
Conclusion
It can be concluded from the discussion of big data that it is necessary for an
organisation to understand the importance of big data technology in their
organisation. It provides many benefits to the organisation in order to achieve their
goals and objectives. From this report, it is analysed that there are four important
characteristics found that includes velocity, variety, variability, and volume. These all
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are four characteristics which need to understand by organisation so that they can
achieve their goals and objectives. This report also discussed important challenges
of big data analytics as it is not an easy process for an organisation to use big data
in their workplaces. The reason is that big data is in huge volume so it becomes very
difficult for an organisation to use them. In addition to this, it also covered some
techniques of analysing big data by organisations.
Poster
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References
Bag, S., and et.al., 2020. Big data analytics as an operational excellence approach
to enhance sustainable supply chain performance. Resources, Conservation
and Recycling, 153, p.104559.
Broeders, D., and et.al., 2017. Big Data and security policies: Towards a framework
for regulating the phases of analytics and use of Big Data. Computer Law &
Security Review, 33(3), pp.309-323.
Lee, H.L., 2018. Big data and the innovation cycle. Production and Operations
Management, 27(9), pp.1642-1646.
Poel, M., Meyer, E.T. and Schroeder, R., 2018. Big data for policymaking: Great
expectations, but with limited progress?. Policy & Internet, 10(3), pp.347-367.
Surbakti, F.P.S., and et.al., 2020. Factors influencing effective use of big data: A
research framework. Information & Management, 57(1), p.103146.
Willems, S.M., and et.al., 2019. The potential use of big data in oncology. Oral
oncology, 98, pp.8-12.
Zhang, C., and et.al., 2019. Optimizing the electronic health records through big data
analytics: a knowledge-based view. IEEE Access, 7, pp.136223-136231.
Karau, H., Konwinski, A., Wendell, P. and Zaharia, M., 2015. Learning spark:
lightning-fast big data analysis. " O'Reilly Media, Inc.".
Japkowicz, N. and Stefanowski, J. eds., 2016. Big Data Analysis: New Algorithms for
a New Society. Springer International Publishing.
Daily, J. and Peterson, J., 2017. Predictive maintenance: How big data analysis can
improve maintenance. In Supply chain integration challenges in commercial
aerospace (pp. 267-278). Springer, Cham.
Online:
What is BIG DATA? Introduction, Types, Characteristics and Examples, 2021. [Online]
Available through: https://www.guru99.com/what-is-big-data.html
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