Business Information System Report: Big Data and Business Intelligence

This report delves into the application of big data analytics and business intelligence within the context of social media. It highlights how big data shapes social media analytics, providing insights into customer behavior and aiding marketers in targeting audiences. The report defines business intelligence and discusses the advantages of big data in increasing profits and creating brand image. It then explores two software tools, Sisense and icCube, detailing their functionalities, competitive advantages, and applications. The report also examines the ethical, financial, and security issues associated with big data, including data encryption, data access, cybercrime, and privacy concerns. References are provided to support the analysis.

Running head: BUSINESS INFORMATION SYSTEM
BUSINESS INFORMATION SYSTEM
Name of the Student
Name of the University
Author Note:

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Big data analytics and Business intelligence in social media
One of best example of use of big data is the fact how it currently provides shape of
social media analytics. The information of user is collected on various kinds of social media
platform which ultimately helps the various marketers to have a better understanding of customer
behavior. Business intelligence can be easily defined as valuable information to large number of
decision makers (Wixom et al. 2014). It can be also defined as a collection of various
information system and technologies which can easily provide managerial decisions. The biggest
advantage of Big Data is how they come up with the ability to target large group of people (De
Mauro, Greco and Grimaldi, 2015). Big data allow various kinds of marketers to have a direct
connection with the various audience group so that they can easily increase the profit and lowers
the cost involved in advertisement. Creating of various kinds of profile on social will ultimately
increase the brand image (Larson and Chang, 2016). With the growth of technology which
provides power to intelligent homes and devices it can be used for collecting data which is
becoming smaller and more efficient.
Two software tools and information system
Sisense is considered to be one of the leaders in the domain of business intelligence. This
particular solution is capable simplify complex kind of data. Competitive edge of sisence is that
has the capacity to collect data from various kinds of sources without any kind of preparation
(Larson and Chang, 2016). Various users can easily have this tool for efficient of use of in-chip
technology which is present in the database. icCube is a well-known SaaS which is nothing but
end BI platform. It is mainly specialized in embedding of various kinds of technology. It can
easily integrate with various kinds of application because of authentication and authorization. It
comes up with ability to combine any source of data.

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Big data are moving to traditional information system like traditional kind of Rdbms
(Raghupathi and Raghupathi, 2014). They are making use of three important V’s that is volume
of data, variety and lastly velocity. Big is much focusing to make use of structure and
unstructured data. Data variety focus on the fact that big data is not about any kind of text or
number.
Application of tool in social media
The primary advantage of sisense is that it can easily collect data from various sources
like salesforce, google analytics and many other. This tool is working hard to make innovation
regarding Elastic Cube technology which ultimately focus on the fact it can import large set of
data without any kind of layout in the CPU (Kim, Trimi and Chung, 2014). icCube is known to
be tool which based on SaaS. It implementation in the cloud makes use of various kinds of
managed services. It has the capability to provide authentication and authorization. icCube can
be considered as a dream tool of any software developer who needs to easily provide dashboard
or self-service based on business intelligence.
Various kinds of ethical, financial and security issues
Data security in sisense can be easily divided into two broad categories that is data
encryption, access of data. Communication of data easily focus on the fact that how data can be
easily secured by sisense when it imported and written in the disk which is provided. When the
data is imported into sisense the used protocol generally depends on protocol which is provided
by the data source (Xiang et al. 2015). It supports SSL for retrieval of data over the SSL. The
second kind of data security is the kind of data access. This particular kind of data security
comes into action when the provided data is imported into it and after that it is displayed in

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dashboard. Various kinds of security issues in information system is cybercrime. There are many
kind of cybercrime like identification of any kind of theft, tracking of fraud activities, hacking
and computer virus. Privacy can be seen as one of the biggest problem in big data technology.
Hacking can be defined as by pass security control which can be used for gaining unauthorized
kind of access to various systems (De Mauro, Greco and Grimaldi, 2015). Virus in information
system can be defined as unauthorized programs which can easily annoy a large number of users.

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References
De Mauro, A., Greco, M. and Grimaldi, M., 2015, February. What is big data? A consensual
definition and a review of key research topics. In AIP conference proceedings (Vol. 1644, No. 1,
pp. 97-104). AIP.
Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government
sector. Communications of the ACM, 57(3), pp.78-85.
Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence,
analytics and data science. International Journal of Information Management, 36(5), pp.700-
710.
Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), p.3.
Wixom, B., Ariyachandra, T., Douglas, D.E., Goul, M., Gupta, B., Iyer, L.S., Kulkarni, U.R.,
Mooney, J.G., Phillips-Wren, G.E. and Turetken, O., 2014. The current state of business
intelligence in academia: The arrival of big data. CAIS, 34, p.1.
Xiang, Z., Schwartz, Z., Gerdes Jr, J.H. and Uysal, M., 2015. What can big data and text
analytics tell us about hotel guest experience and satisfaction?. International Journal of
Hospitality Management, 44, pp.120-130.