Big Data Analytics Research Report: An Examination of Current Trends

This report provides a comprehensive overview of big data analytics, examining its current trends, applications, and the challenges associated with its implementation. The report begins by defining the problem and providing a detailed literature review that covers various aspects of big data analytics, including its utilization in government, social media, and technology sectors. It explores the advantages of big data analytics, such as improved customer insights, fraud identification, and real-time forecasting, while also addressing the challenges of data storage, security, and the need for skilled professionals. The report outlines existing methods, such as positivism philosophy and deductive approaches, used to analyze the topic. The report also mentions the importance of proper selection of tools like NoSQL and Hadoop for efficient data management. The report provides an insightful analysis of big data analytics, focusing on the real-world applications and the ongoing need for advancements in data management and security.

Running head: BIG DATA ANALYTICS RESEARCH
Big Data Analytics Research
Name of the Student
Name of the University
Author note

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Abstract
In the current era, big data analytics is no longer utilized only for experimenting. However,
several organizations started achieving several results with the approach as well as expanding
efforts that are surrounded more data along with models. It is the term utilized in order to explain
the process of collection, availability as well as processing of the streaming data of massive
volumes in real time. In order to ensure accurate decisions, organizations are combining
marketing, customer data, sales and external data. Timely, accessible, trustworthy, relevant and
secure are the major advantages of using big data analytics. However, it requires a research to
investigate and explained in the present study.

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Table of Contents
Statement of the problem.....................................................................................................3
Literature review..................................................................................................................3
Outline of existing methods.................................................................................................8
Outline of the proposed method..........................................................................................9
Outline of validation and comparison examples.................................................................9
Bibliography......................................................................................................................11

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Statement of the problem
In the digitalized world, a massive amount of data is produced. The large business
enterprises are facing challenges in finding out the ways to make the data useful. The amount of
data generated by large business enterprises is growing at 40% to 60% per year. Hence, storing
the massive amount of data is not useful and therefore the organizations are finding for the
options such as data lakes as well as big data analysis tools, which can assist in managing big
data largely (Alsheikh et al., 2016). It is meaningful analysis through the process of using big
data analytics. It is important to understand the major problems faced for using big data analytics
(He et al., 2016). On the contrary, data storage and quality, security as well as privacy of data,
scaling up and down big data as per the present demand are the issues faced in big data analytics.
Literature review
Akterand Wamba (2016) stated that the procedures through proper comprehension of
customer behavior, latest trend along with changing patterns. The major places where big data
analytics is utilized as followed.
Government: Big data analytics proves as useful in the government sector. In addition,
big data analytics is also responsible for political parties and successful election (Singh et al.,
2014). The government uses multiple techniques in order to ascertain the procedure of
responding to action of the governments by electoral and ideas for augmentation of policy.
Hence, it is important to focus on the usage of big data analytics in proper way so that common
people get benefits for this.
Social media analytics: The advent of social media leads to big data outburst. Multiple
solutions have been developed for analyzing the activities of social media such as Cognos

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Consumer Insights of IBM that is helpful to make sense of chatter (Hashem et al., 2015). Hence,
before using big data, the requirements need to be processed on big data for deriving intelligent
as well as valuable outcomes. Hence, in order to understand mindset of the customers regarding
the applications of intelligent decisions that are derived from big data is essential. On the other
hand, it would be helpful to analyze the customer behavior by using big data analytics in social
media.
Technology: The applications are described as followed. EBay.com utilizes data
warehouses at 7.5 petabytes as well as 40PB and 40 PM Hadoop cluster for searching,
recommendations for customers and merchandising (Shiao et al., 2015).
Fraud identification: For the businesses, the operations engage any claims and
transactions processing along with identification of frauds. In most of the cases, fraud is
generally discovered after the process, where the damage is done as well as it is left to reduce
harm as well as adjust the policies in order to prevent the process from happening (Hazen et al.,
2018).
Advantages of big data analytics:
Big data management solutions provide organizations ability adding several types of data
from multiple sources (Hashem et al., 2015). The complete profile of customers at every point
need contact to be developed through the process of organization. Big data management
solutions provide a good idea in managing the process and in-depth knowledge of clients as well
as operations. Big data management solutions can eliminate data niches (Alipourfard et al.,
2017). The enterprises gain a unique view of the consumers that consists of countless descriptive,
industry-specific metrics as well as calculated process that can allow to construct a detailed

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record of behavior of the clients. The profiles can provide the enterprises a global comprehension
of the customers by using in-depth knowledge of clients along with its operations.
The significance of big data does not revolve the extent that an organization uses in big
data analytics. With data hosting as well as technology, the organizations can secure
infrastructures in proper way. Big data analytics is helpful is sales insights that could led to gain
extra revenue. The real-time analytics can tell the amount of sales doing in the case of an internet
retailer that the products are performing extremely well (Alipourfard et al., 2017). In addition, it
helps in keeping up with the process along with the trends of customers through proper
competitive offerings and promotional analysis.
Big data is intimidating with effective data management solution that the enterprise can
identify the data that is required in gaining actionable data as well as increase the value of good
relationship with clients (Hazen et al., 2018). The applications are developed with the help of
smartphone application development organization that can be used in sustaining an effective
relationship and allows for construction of complete customers. On the other hand, the reality is
continuing data volume in order to increase and promise for the organizations that appears to
grow exponentially. It also provides an idea from massive amount of data from several sources
that consists of the process that comes from external sources such as internet, social networks
that are stored in the databases of the organization (Najafabadi et al., 2015). The advantages of
using big data analytics can be included as following
 It provides ideas from the massive amount of data from several sources
including external third-party sources, social network already stored in the
database of the organization.

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 Real-time forecasting as well as monitoring of the occasions may affect
performance and operations of the business.
 The capability is locating, getting, extracting, analyzing as well as changing the
process with multiple tools.
 Detection of significant data can enhance quality of decision making.
 Ability in mitigating risks through optimizing complicated decisions regarding
unplanned events quickly.
 Detecting the causes of failures as well as issues in real time
 Complete understanding of possible data-driven marketing
 Providing offers to the customers based on purchasing habits.
 Enhancement of commitment of clients as well as increasing of loyalty
 Reassessment of risk portfolio quickly
 Customizing experience of the customers
 Addition of the values of interactions with online as well as offline consumers
Big data analytics can be helpful in providing timely, accessible, trustworthy, related and
secure service to the users. Big data analytics is helpful in saving a lot of time for attempting to
search as well as manage data. Keeping the non-related data is considered as curse for database
as it will be helpful in making the filtering procedure complicated. With hosting of data as well
as technology, the organizations can secure infrastructures as an average of security breach of the
organizations that can secure infrastructure and costs $214. The technology is similar as cloud-
based analytics, which will give a substantial cost of the benefits. The conventional architectures
such as data marts and warehouses in specific comparing to the big datais difficult (Raghupathi
& Raghupathi, 2014). There is a difference between functionality as well as manipulative

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process in comparison of price that can decide enhancements of magnitude. It is considered as
the difference of functionality.
In the digitalized world, massive data is generated every minute. The data amount
generated every minute makes the process challenging in order to store, manage, use as well as
analyze the process. The large business enterprises are currently struggling in finding the ways in
order to use the massive amount of data. Currently, the amount of data generated by large
business enterprises is developing as mentioned at the rate of 40 to 60% in every year (Wang et
al., 2016). Real time applications are different from regular applications in time-at-tribute. It can
ensure the response or take decision within a short time. Transportation system is one of the
major areas where real–time data analytics is required
Challenges in big data analytics
As the data volumes are increased day by day and getting more complicated, it becomes
difficult for the big data programmers to take effective decisions and optimize the processes by
proper understanding customer behavior and latest trends along with changing patterns. An
enterprises needs to collect, store and analyze the large number of datasets in various ways. An
enterprise can use robust big data tools for storing, accessing and managing the structured and
unstructured data gathered from various sources in faster as well as efficient way (Alipourfard et
al., 2017). On the other hand, it is important to use big data optimally without detection of the
challenges. Selection of proper NoSQL tools is considered as one of the major issues faced in big
data analytics. The enterprises cannot manage the large volumes of the datasets either in
structured or unstructured data that is efficiently used for traditional database management
systems. The non-relational database or NoSQL tool has several limitations. Hence, the

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businesses find the procedure as challenging in order to select appropriate non-relational
database and use effective data management tool.
However, there are some tools for doing the jobs, where Hadoop is able to process data in
real time. The tools can offer possibility of real time processing of big data. Storm is currently
owned by Twitter (Alipourfard et al., 2017). It is considered as real time distributed system of
computation. Storm is not difficult to use as well as it can works with any type of programming
language (Zhou & Yang, 2016). It is scalable as well as fault-tolerant. On the other hand,
Cloudera can offer the Enterprise RTQ tools. GridGain is considered as one of the open source
based grid computing that is developed for Java (Alipourfard et al., 2017). In addition, it
provides a substitute and scalable data grid.
On the other hand, scaling up and down as per the current demand is one of the
challenging aspects faced while using big data analytics. In addition, collection as well as
integration of huge and diverse datasets and maintenance of data integrity, security as well as
privacy is considered as major challenges (Zhou & Yang, 2016). Apart from these, requirement
for synchronization across disparate data source, acute shortage of professionals who can
understand big data analytics properly are the major challenges faced by the enterprises. Getting
meaningful analysis through the utilization of big data analytics, data privacy is considered as the
major issues faced by an enterprise. It will be helpful to analyze the issues and take proper
actions regarding the use of big data analytics.
Outline of existing methods
In order to analyze the importance and challenges faced in big data analytics, the research
can be carry forwarded by taking some existing approaches such as positivism philosophy.

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Positivism philosophy can be helpful to analyze the topic of the research in scientific way. On
the other hand, deductive approach is taken for analyzing the importance of big data analytics.
On the other hand, the philosophical approach would be helpful to manage the process. The
strategy of the researcher is important as it can be helpful to ensure the process (Raghupathi &
Raghupathi, 2014). On the other hand, the deductive approach will be helpful in the research to
deduce the existing theory and approaches present in the research. It is also important to
understand the process as well as analyzes the existing theories along with models associated
with the research. In the research, descriptive research design in taken. It would assist in
analyzing the data related to big data analytics.
Outline of the proposed method
In order to analyze the significance of big data analytics, it is important to understand the
secondary data collection from relevant articles taken into consideration. This will be beneficial
to understand the advantages and disadvantages for using big data analytics in different fields. In
addition, the use of strategy allows the researcher to understand the various factors that can assist
in making the experiment in proper way (Raghupathi & Raghupathi, 2014). The qualitative data
analysis can assist to analyze the data in proper way and understand the significance. On the
other hand, Spark can be helpful to find the source and parallel processing framework.
Moreover, HBase is considered as column based value store that is developed in order to run the
distributed file system of Hadoop. The clusters and NoSQL systems will be used primarily as
pads of landing and staging areas to get the loaded data.
Outline of validation and comparison examples
The validity is associated with the extent of the procedure of data collection, which can
be helpful to measure proper content needed to measure. It can be referred as the procedure of

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managing the instruments as well as measurements that is intended for process management. On
the other hand, internal as well as external validity of the research meets reality as well as
external validity (Hazen et al., 2018). The applications of big data include the data from internal
and external system.

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Bibliography
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and
agenda for future research. Electronic Markets, 26(2), 173-194.
Alipourfard, O., Liu, H. H., Chen, J., Venkataraman, S., Yu, M., & Zhang, M. (2017, March).
CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data
Analytics. In NSDI (Vol. 2, pp. 4-2).
Alsheikh, M. A., Niyato, D., Lin, S., Tan, H. P., & Han, Z. (2016). Mobile big data analytics
using deep learning and apache spark. IEEE network, 30(3), 22-29.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The
rise of “big data” on cloud computing: Review and open research issues. Information
systems, 47, 98-115.
Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: Operations
research in support of big data analytics for operations and supply chain
management. Annals of Operations Research, 270(1-2), 201-211.
Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: Operations
research in support of big data analytics for operations and supply chain
management. Annals of Operations Research, 270(1-2), 201-211.
He, Y., Yu, F. R., Zhao, N., Yin, H., Yao, H., & Qiu, R. C. (2016). Big data analytics in mobile
cellular networks. IEEE access, 4, 1985-1996.