Hadoop-Driven Big Data Digital Marketing Use Cases in Fraud Prevention

The report delves into a specific application of big data analytics within the realm of digital marketing: fraud prevention for credit cards. It highlights how big data's characteristics—particularly value and veracity—are critical in detecting fraudulent activities, thereby safeguarding financial transactions. The discussion includes an analysis of two pivotal Hadoop stack components: the Hadoop Distributed File System (HDFS) and MapReduce. HDFS supports fault-tolerant file storage across distributed environments, enabling efficient data management and processing. Meanwhile, MapReduce provides a framework for parallel computation, breaking down large datasets into manageable chunks for rapid analysis. The combination of these technologies not only facilitates the handling of vast volumes of transactional data but also enhances the accuracy and reliability of fraud detection mechanisms. By leveraging big data's potential through Hadoop technologies, businesses can significantly mitigate risks associated with credit card fraud, ultimately fostering a secure environment for digital marketing activities.

Running head: BIG DATA HADOOP
Big Data - Hadoop-Driven Big Data Digital Marketing Use Cases
Name of the Student
Name of the University
Author’s Note:

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Table of Contents
Introduction......................................................................................................................................2
Discussion........................................................................................................................................2
Marketing Big Data Use Case.....................................................................................................2
Hadoop Stack Components..........................................................................................................3
Conclusion.......................................................................................................................................4
References........................................................................................................................................5

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Introduction
Big data is extremely easy to use and extremely safe for the information (McAfee,
Brynjolfsson and Davenport 2012). Careful usage of big data allows smart decision making of
the modern day marketers and retailers for more effective marketing of services and products.
The following report describes a specific marketing big data use case that involves at
least two of the big data V characteristics like volume, velocity, variety, veracity, value,
variability, vitality and viscosity (Dittrich and Quiané-Ruiz 2012). The two Hadoop stack
components, which can be utilized for the support of management and processing of the datasets
for the generation of useful business analytics.
Discussion
Marketing Big Data Use Case
Fraud Prevention: There are various use cases of Big Data. Fraud Prevention is one of
the most relevant and significant use cases of big data. Fraud incidents are common for credit
cards (Holmes 2012). Previously, they utilized rule based systems for the fraudulent transactions.
This fraud prevention in case of credit cards is an important utilization of big data analytics.
They can easily detect any type of criminal activity and prevents the false positives (Katal,
Wazid and Goudar 2013). The two V characteristics of big data for this particular use case are as
follows:
i) Value: Value is the most important big data characteristic for a business. It helps in
establishing the business environment and sustaining the ongoing investments. The characteristic
value ensures that a particular organization or thing is getting value from the big data (Dittrich

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and Quiané-Ruiz 2012). For the case of fraud prevention, the characteristic ensures that the credit
card is getting the appropriate value from big data. This big data analytics has reduced the
chance of fraud in case of credit card issuers.
ii) Veracity: This is the second characteristic of fraud prevention. This provides the
accuracy or the authenticity of the credit cards. The characteristic veracity eliminates all the
wrong data and detects and prevents the fraud case.
Hadoop Stack Components
The two Hadoop stack components that can be used to support management and
processing of the datasets for the generation of useful business analytics are as follows:
i) Hadoop Distributed File System: The Hadoop Distributed File System is the dispersed
file system, which is designed for running on the hardware commodity (Dittrich and Quiané-
Ruiz 2012). There are various similarities with the file systems that are already existing. It is
extremely fault tolerant and is utilized for the support of managing and processing of the
datasets.
ii) MapReduce: MapReduce in Hadoop is the tool and a program model that is used for
the dispersed computing. The MapReduce algorithm is comprised of the two significant tasks
namely, Map and Reduce (Katal, Wazid and Goudar 2013). The job of Map usually takes up a
dataset and then transforms it into some other dataset. The job of Reduce is taking up of the
output from a specific map as input and combining that data tuples into small set of tuples.

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Conclusion
Therefore, from the above report it can be concluded that, big data are complex set of
data that are utilized for the predictive analytics, user behaviour analytics or any other method of
advanced analytics of data. The above report outlines a brief discussion about the Hadoop driven
big data digital marketing use cases. The report has also described about the two big data V
characteristics like velocity, variety, vitality, volume, variability, veracity, viscosity and value.

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References
Dittrich, J. and Quiané-Ruiz, J.A., 2012. Efficient big data processing in Hadoop
MapReduce. Proceedings of the VLDB Endowment, 5(12), pp.2014-2015.
Holmes, A., 2012. Hadoop in practice. Manning Publications Co..
Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and
good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp.
404-409). IEEE.
McAfee, A., Brynjolfsson, E. and Davenport, T.H., 2012. Big data: the management
revolution. Harvard business review, 90(10), pp.60-68.