SUSS ANL203e Analytics for Decision Making Group Assignment

This assignment solution addresses a data analytics problem for TranSUSS, exploring the integration of social media data with business data. The solution begins by defining the characteristics of big data, including volume, variety, velocity, and variability. It then details various technologies that support big data, such as Apache Hadoop, Microsoft HDInsight, Hive, Sqoop, PolyBase, Microsoft Excel, and NoSQL. The assignment emphasizes the importance of understanding these concepts and technologies for effective data management and analysis in a business context. The provided solution also includes references to support the analysis and recommendations.

Running head: DATA ANALYTICS
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Question 3: TranSUSS engages its customers actively via their mobile app and Facebook.
TranSUSS management team is keen to explore the plan to integrate Social Media data
with their business data, especially with their competitors’ information, when customers
evaluate their options openly between TranSUSS and its competitors with other users.
Hence, TranSUSS CIO felt that this plan seems like a Big Data application and had raised
the following questions:
(a): What are the characteristics of Big Data? Give a definition
The characteristics of big data are as followings:
Volume: Huge volume of business data can be effectively managed and stored using the
big data analytics. Both the global as well as the small and medium sized industries can use the
big data for the management of their different categories of data.
Variety: The content of an application can be from both homogeneous and
heterogeneous sources considering both the unstructured data as well as the structured data. Thus
the concept of big data can be used for all the business data which are circulated in the form of
text messages, emails, videos and photos, pdf format and audio files.
Velocity: The speed of the generation of data are very much important for the user of this
technology as the generation speed is important for the e-commerce businesses where data needs
to be obtained quickly by the users as new data are readily available in the other e-commerce
platforms (Cai & Zhu, 2015). Data generation speed is important for all the inline business as the
business processes such as the online payment options works according to the data generation
speed. The loading time of the application is very important for the businesses.

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Variability: The inconsistency of the data falls under this category as it may have a
straight impact on the management of the data.
Big data can be defined as the type of data management technique which can be used for
personal purpose and also for an organization for the management of complex data. The concepts
of big data can be successfully used both in unstructured data and also the structured data. The
complex data can be managed in a simpler manner with the help of the big data analytics. The
incorporation of this technology have numerous advantages and disadvantages which has to be
understood by the team who are planning to incorporate this technology so that the desired
results are obtained. Visualization of the data and the query which are used to call or have any
function with the data are the prime challenges of the use of big data in the business sectors.
All the business data can be managed effectively with the help of the big data, which are
very much helpful to increase the business reach of the organization. Enhanced customer
engagement is one of the prime contributions of big data. The growth and sales of the business
organizations can be improved significant if the flow of data can be effectively monitored with
the concepts of the big data. The predictive analysis concepts such as data mining, machine
learning and predictive modelling can be used by the customers of the business organization to
know about the recent facilities provided by the management of the organization (De Mauro,
Greco & Grimaldi, 2016). Transfer and sharing of essential data can be easier with the
implementation of this technology. Thus complex data sets are analyzed in a better way using the
big data in different types of business organizations.

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(b): What are the technologies available to support big data? List the Big Data
technologies clearly.
There are different categories of technologies available to support the big data technology
which are increasingly been used in the business organizations such as the Apache Hadoop,
Microsoft HDInsight, traditional SQL, PolyBase, Sqoop, Microsoft Excel and Presto.
Apache Hadoop: This framework is designed using Java which can be effectively used
in the storage and the processing of the data which are performed by the big data analytics. The
application of big data analysis in business organizations can be replaced by Apache Hadoop.
Both unstructured data and the structured data can be managed using the Hadoop Distributed file
system.
Microsoft HDInsight: Microsoft HDInsight is an alternative technology of Big Data.
Being a product of the Microsoft Corporation and powered by Apache Hadoop, this big data
solutions are used to store the data in the cloud services (Erevelles, Fukawa & Swayne, 2016).
The windows assured blob storage are used by this technology. The loss of this technology is
lower than the application of big data analytics.
Hive: The challenges and the complexities of the bigger organizations regarding the
managements of the datasets can be done using this software facility. Distributed storages of the
business organization can be managed with the help of Hive software.
Sqoop: The data which are stored in the relational databases are required to transfer as
required, this transaction is done with the help of the Sqoop. Bulk data can be transferred
between the structured databases and Apache Hadoop is done using this tool.

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PolyBase: It is defined as a distributed system which runs on Hadoop. The location and
categorization of the data which are stored in a database can be traced with the help of this high
performance tool which can be used as an alternative to the big data technology. The transaction
of the queries from the Apache Hadoop is done with the help of the PolyBase. Both the non-
relational as well as the relational databases can be used by this new feature of the SQL server
2016. Every manufacturing industry have a data warehousing unit where both relational and non-
relational databases are required, PolyBase helps in the movement of the data between the
databases and the users.
Microsoft Excel: The application of the Microsoft Excel for the storage and the
management of the business data is a very primitive and conventional way. Large chunks of data
can be stored, edited, deleted, added and other arithmetic calculations can be performed on the
numeric data stored in the Microsoft Excel (Grover et al., 2018). Data analysis is one of the
prime data management activity which are done in MS Excel.
NoSQL: The structured data of the business organizations can be effectively stored using
the traditional structured query language. The unstructured or the raw data which are found in
huge chunks can be purposefully managed and modified with the help of NoSQL. All the
available schema of the data and the complex data can be managed well with the help of the
NoSQL. There are numerous open source NoSQL available which can be used as an alternative
to big data analytics.

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Reference
Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big
data era. Data Science Journal, 14.
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its
essential features. Library Review, 65(3), 122-135.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the
transformation of marketing. Journal of Business Research, 69(2), 897-904.
Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value
from big data analytics: A research framework. Journal of Management Information
Systems, 35(2), 388-423.