Big Data Analytics Applications and Challenges

This assignment delves into the world of big data analytics, examining its wide-ranging applications across sectors like healthcare, education, and business. It analyzes the key technologies and techniques used for processing and analyzing massive datasets, highlighting the transformative potential of big data. The document also explores the challenges associated with big data analytics, such as data privacy concerns, scalability issues, and the need for skilled professionals.

USES OF BIG DATA IN BUSINESS ORGANIZATIONS, A CASE STUDY OF TELSTRA
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Executive summary
The purpose of this project was to determine the use of big data in business organizations. Big
data exist in different forms including structured data, semi-structured and unstructured data.
Once the amount of streaming data becomes voluminous, it goes beyond traditional data
management tools to handle. Specific objectives that need to be met in order to realize the
purpose of the project include; 1) to determine uses of big data in business organizations and 2)
to determine the ways in which big data analysis can present wrong information that can lead to
improper strategic decisions in a business. It can therefore be concluded that managing big data
well and exhausting all the information from them will lead to positive impact in business
organization.
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TABLE OF CONTENTS
Executive summary……………………………………………………………...………………..ii
Introduction…………………………………………………………………………….………….1
Project objectives……………………………………………………………………….…………1
Project scope…………………………………………………………..…………………………..2
Literature review……………………………………………………..……………………………2
Conclusion………………………………………………………………………………….……..6
References…………………………………………………………………………………..……..8
Appendix A: Acronyms………………………………………………………………………….10
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Introduction
Since the emergence of technology and the social network through the internet, many businesses
i.e. big and small businesses have been taking the advantage of such platforms for collecting data
from their respective and targeted customers about their product. Due to large extended family
created by the social media on the internet, those businesses and companies collect more
information in form of responses from their customers in return that can be termed as big data.
Therefore, big data can be described as the enormous volume of data that is either structured or
unstructured and are complicated that they cannot be handled, analyzed or efficiently processed
by traditional management tools. This research study therefore is aimed at looking at the uses of
big data in Telstra business organizations towards improving its overall performance. Different
types of big data will be looked at in this project ranging from structured, semi-structured to
unstructured data. The data that can be stored, is accessible and can be processed in a fixed
format are referred to us structured data. Several challenges are faced in unstructured data being
that they are stored in an unknown format whereas semi-structured data is the data that can exist
in both the known and unknown formats Dean (2014).
Project objectives
Some of the objectives that will have to be met in this project upon its successful completion will
be;
1. To determine the uses of big data in business organizations, a case study of Telstra.
2. To determine the ways in which big data analysis can present wrong information that can
lead to improper strategic decisions in a business.
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Project questions
From the above project research objectives, we have the following research questions that will
help to meet the project objectives.
1. What are the uses of big data in business organizations, a case study of Telstra?
2. What are some of the ways in which big data analysis can present wrong information that
can lead to improper strategic decisions in business?
Project scope
Among other cellular and mobile providers in Australia, Telstra forms part and is one of the
biggest in the country and it is located Melbourne Victoria. The area of study will therefore be
Dandenong Road Clayton Vic in Melbourne, Australia the place where our case study i.e. Telstra
cellular and mobile providers is located.
Literature review
According to Wamba et al (2015), they perceived big data as having the ability to transform the
ways of management in business organizations. Due to this therefore in the current era and
generation, businesses are scrambling and changing from managing small data to big data
(Sagirolgu and Sinanc, 2013). The need of not wanting to lose or taking for granted any of the
information collected from the customers concerning the businesses have compelled the business
management to acquire databases that can handle big data (Hashem et al, 2015). This has further
given birth to big data analytics that help in exhausting information from the big data leaving
none of the information hidden in order to gain market competitive advantage (Zhang et al,
2012). With big data, they portray the characteristics of streaming in every second and each time
as they keep on being created where they need to be stored for later value extraction. In order to
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improve the marketing strategies Telstra is taking into use the big data and analyzing them to
hike their services as per the customers’ demands. Big data once collected, in the storage
operational data system, they are then transmitted to the storage by using Extract Transform
Load (ETL). These ETL are the tools that are used in the extraction of data from the outside
sources so that the data can be changed to fit the needs for operation and then ultimately ensure
that the data is loaded into the database Dean (2014). Furthermore, some non-rational databases
like the No-SQL were designed purposively to manage the unstructured data where data
management and storage are separated this is opposed to the rational databases Dean (2014).
According to He et al, (2011), they stated that big data processing had four critical requirements
where fast data loading is taken to be the first one, the second comes query processing. This
second requirement is responsible for the satisfaction of heavy workload and requests for real
time since most queries are response time critical. Highly efficient utilization of space for storage
forms the third requirement for big data processing, this one help to handle the fast growth of
user activities that also help in managing the available disk space for storage. The last and fourth
requirement is strong adaptivity to highly dynamic workload pattern this is according to (He et
al, 2011). All these four requirements are always put to use by Telstra whenever the company is
collecting large data from their customers. Speed and efficiency by which data is conveyed is
managed through the process out the four different requirements. The company do gather and
manage both structured and unstructured data through social media from their consumers of
goods they sell.
Various functions of big data that are enjoyed by companies and businesses such as Telstra
irrespective of their sizes are, having the privilege to dialogue with the consumers through the
social media. Customers being that they know what they need from their producers, they tend to
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take their time to compare what they are being offered to other products from other producers.
Big data therefore enable the business enterprises to take care of such customers by engaging on
one-on-one talk with the customers in order to maintain their customers and stay relevant in the
competitive market Sin and Muthu (2015). Through managing big data by business
organizations, the panel of business analysts can be able to perform risk analysis about the issues
of the business operation. According to Verburg et al (2013), they stated that the success of the
company is determined by several factors not only how the company is run but also social and
economic factors play vital roles in the success realization of the companies. Further, Luo et al
(2016) ascertained that big data ensure that there is safety of the acquired data in the company
since the entire landscape within the company are allowed to be mapped by big data tools. This
therefore enable the company and all the sensitive business information safe and protected in a
good manner. The scramble for big data therefore by the companies are for them to enjoy the
safety of their stored information in a protected way (Chen et al, 2012). This function of big data
is attractive and vital for organizations that need to keep or store financial information since
there is surety of data safety. By well managing big data, new revenue stream can be created
since the insight for analyzing the consumers and the market is provided for (Leibowitz, 2013).
Companies that are using big data can benefit more from it if they have their employees trained
about the big data.
Bid data analysis also referred to as big data analytics that takes the process of drawing
conclusion from big sets of data. In as much as businesses enjoy the use of big data, it is also in
some cases associated with some limitations in their analysis that can lead to improper strategic
decisions in business. Correlation between variables can be prioritized since when the variables
are linked to one another the analysts tend to teas out the correlation. The link between the
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variables under study does not always mean that there is existence of relationship between the
variables. This therefore bring to the attention of the specialists that not all the relationships are
always important or meaningful. Such inferences when made by the analysts in business may
lead the business to improper decision that could worsen the situation at hand (Boyd and
Crawford, 2012). The analysts when using big data take control of trying to answer all the
questions that might be arising in business for its operation, but it is upon them to discern which
questions are relevant and are therefore answered by the data they are managing (Raghupathi and
Raghupathi, 2014). Sometimes the tools used in data collection can have an effect of resulting to
inconsistency, for example when the internet through google is used, it might result to change in
search experience in several ways, and this will therefore result to variation in the search findings
from time to time. Over reliance of such tools for gathering the information that are used for
analysis, using the dataset from those tools to carry out the correlation test may result to
unreliable results. For the organizations to enjoy the usefulness of big data, they need to know
and understand big data since the use of big data analytics tools to derive information is
complicated (Boyd and Crawford, 2012).
Changes are nowadays experienced with data as people are not just concerned with data, but they
are concerned with what the meaning and importance of data (Raghupathi and Raghupathi,
2014). Better understanding of the collected data is deemed useful to business organization as it
help in coming up with useful and informed decisions for the business. In the process, data
analytics is carried out in order to come up with relationships, unknown patterns and information
Muthu (2015). From big data, carrying out analytics helps further in extraction of hidden patterns
from big data sets and also determining available relationships between variables that could help
in giving surplus information to the business organization. Various additional analysis have been
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found common with large data sets on top of advanced data analytics methods that include
clustering, decision tree, association rule and regression analysis He et al, (2011).
Content sharing have become so easy nowadays through social media as the only problem
remains to be failure of exploitation of the enormous content that is generated from the social
media networks (Zhang et al, 2012). So the most appropriate data analysis method that can be
used to extract information from such data for them to be useful to business is the social media
analytics. This analytics method will tend to exhaust all the information that could be hidden in
the data that could help in prediction and coming up with informed business decision.
Conversations and reaction from people in social media communities can best be understood,
extracted and detect useful pattern and intelligence from such interactions from what is shared
through carrying out social media analytics. As a result therefore, advanced big data
visualization (ADV) is to be done in response to growth in big data analytics (Zhang et al, 2012).
All is not done until data can be presented in a way that it can be effectively consumed by people
so that the business decision makers can be able analyze the available data properly so that a
serious tangible action can be taken.
Conclusion
Well management of big data and exhaustively drawing information from all the available
collected data will result to positive impact to Telstra cellular and mobile providers. This will
enable the cellular and mobile provider company with all the necessary information as they are
able to communicate one-on-one with their customers. Social media will be found to be
providing a good platform that encourage such communication and hence building good
customer relationship thus boosting their sales. This is one of the uses of big data that Telstra
enjoy and that they employ to widen their market and hence giving them higher chances of
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existing in the market. The acquired information from the customers is broken down to the last
end that the customers’ problems are addressed by the company.
Through risk analysis conducted by analysts from the collected big data, the company will be
able to know the future risks the business might face and find the solutions to the current issues
and also come up with appropriate solutions that will solve the future business risks. This will
further smoothen the operation of the business as it will stay free from any risk that can affect its
operations at the moment and any other time in future.
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References
Boyd, D. and Crawford, K., 2012. Critical questions for big data: Provocations for a cultural,
technological, and scholarly phenomenon. Information, communication & society, 15(5),
pp.662-679.
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big
data to big impact. MIS quarterly, 36(4).
Dean, J., 2014. Big data, data mining, and machine learning: value creation for business
leaders and practitioners. John Wiley & Sons.
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The
rise of “big data” on cloud computing: Review and open research issues. Information
Systems, 47, pp.98-115.
He, Y., Lee, R., Huai, Y., Shao, Z., Jain, N., Zhang, X., Xu, Z.: RCFile: A Fast and
Spaceefficient Data Placement Structure in MapReduce-based Warehouse Systems. In: IEEE
International Conference on Data Engineering (ICDE), pp. 1199–1208 (2011)
Liebowitz, J. ed., 2013. Big data and business analytics. CRC press.
Luo, J., Wu, M., Gopukumar, D. and Zhao, Y., 2016. Big data application in biomedical
research and health care: A literature review. Biomedical informatics insights, 8, p.1.
Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), p.3.
Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In Collaboration Technologies
and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
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Sin, K. and Muthu, L., 2015. APPLICATION OF BIG DATA IN EDUCATION DATA
MINING AND LEARNING ANALYTICS--A LITERATURE REVIEW.ICTACT journal
on soft computing, 5(4).
Verburg, R.M., Bosch-Sijtsema, P. and Vartiainen, M., 2013. Getting it done: Critical
success factors for project managers in virtual work settings.International journal of project
management, 31(1), pp.68-79.
Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How ‘big data’can
make big impact: Findings from a systematic review and a longitudinal case
study. International Journal of Production Economics, 165, pp.234-246.
Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S.,
Last, H., Keim, D.: Visual Analytics for the Big Data Era—A Comparative Review of
State-of-the-Art Commercial Systems. In: IEEE Conference on Visual Analytics Science
and Technology (VAST), pp. 173–182 (2012).
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Appendix A: Acronyms
ADV- Advanced Big Data Visualization
CBA- Common Wealth Bank of Australia
ETL – Extract Transform Load
ELT – Extract Load Transform
No-SQL – No Structured Query Language
SQL – Structured Query Language
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