Mock Dissertation Chapter One- Introduction

Mock Dissertation Chapter One- Introduction

Overview

Big data concepts and big data analytics popularity have increased over time, not only in research but also in organizations handling data. Heightened adoption of information technology assets by organizations, access to the internet, and growth of internet-oriented functionalities provide vast data that firms can critically analyze to improve effectiveness in predicting the future and mitigating possible risks and uncertainties. However, while big data concepts provide businesses with competitive tools, there is tremendous growth in concern about data and information security that firms collect, store, and analyze to derive insights. As such, protecting firms’ information and data systems has become paramount even amidst the booming trend in big data and big data analytics. According to Knapp (2006), data security attacks have increased over time, resulting in heightened losses for the affected firms.

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Many organizations that focus on utilizing big data concepts and analytics to set out a robust competitive edge or improve operational efficiency within the organization have prioritized data security (Matturdi et al., 2014). An analytical report from the National Security Administration posits that data collected by telecommunication networks, the internet, and huge firms such as Facebook, Google, and YouTube has harmful effects on the users if inappropriately handled. As such, data security techniques adopted by an organization should guarantee secure transmission of information for the benefit of the organization without jeopardizing the user’s privacy regardless of their social strata level, which can increase their vulnerability to misuse by tech-savvy individuals (Bertino, 2016). Therefore, it is crucial to evaluate the effectiveness of the various data security techniques adopted by firms to determine their effectiveness in serving the organization’s data security needs.

Background and Problem Statement

Organizations’ competitiveness in the market is a crucial aspect of improved performance. To achieve higher competitiveness against rival companies in the industry, organizations establish strategic goals that define the operations and activities that must be completed in a given period. Among these strategic goals or tools adopted by firms include the use of big data concepts and analytics and the greater realization of the need to establish information-supporting systems. This allows the firms to establish leverage on information-based resources and competence strategically. However, with the rising trend in information technology and the indispensability of data and information aspects of the business, numerous firms have been challenged in maintaining and achieving data security and privacy. Accordingly, various data security techniques have been adopted by firms to protect user data while optimizing the benefits accrued from the use of the data and other IT functionalities (Sun et al., 2016). Thus, the mock study seeks to outline how well the different data security techniques prove effective for firms that have adopted them. Subject to the prevailing cybersecurity attacks, techniques such as the use of passwords are challenged with improved technologies that can bypass simple security systems. In this study, a close analysis will focus on outlining the various reasons why various data security techniques are ineffective and the proven level of effectiveness in optimizing data security in an organization.

Purpose of the Study

The purpose of this study is to explore the various data security techniques that have been researched and implemented to prove their effectiveness. Ultimately, the researcher seeks to identify how data science techniques have been applied to data security aspects of the organization from twofold: the security optimization aspects and threat position perspectives of the firm. As such, the researcher will evaluate the possible threats, such as malware and unauthorized data access, including the most common malware threats on data security, to outline insights related to how effective data security techniques are in serving the needs of the firm. Additionally, the researcher aims to investigate the rate of adoption of the identified data security techniques based on their popularity and effectiveness in enhancing data security in the firm. To achieve this goal, the research will systematically review existing literature, establish specific research questions, and collect data for analysis to draw more robust conclusions for the study, including suggesting possible future research gaps that can be explored.

Significance of the Study

Big data and data security are indispensable aspects of a business operation; therefore, as technological advancement provides effective tools for strategic management, ensuring data security is paramount for an organization. This research seeks to inform business managers on how well various data security techniques at their disposal can prove effective based on other firms’ experiences. The research findings will outline the various data security techniques that various firms have researched and implemented, including how effective they have proven to be for these firms. The insights are crucial for businesses and researchers to develop competitiveness, improve performance, and identify existing research gaps that can be explored in the future.

Research Questions

Main Research Question: Which are the Data Security Techniques that have been researched and implemented across industries with proven effectiveness?

To fully explore the dynamics of the main research question, the research will utilize four additional specific research questions that include;

What are the effects of different malware on Data Security? (Exploratory)

How do Data Science Security Techniques compare from a popularity standpoint? (Exploratory)

Are organizations more willing to implement certain Data Science Techniques that have been proven effective within their industry? (Predictive)

How do various Data Science Techniques compare to one another across industries? (Ethnography).

Limitations of the Study

The scope of this study is to identify the various data security techniques that have been researched and implemented in various industries based on their effectiveness. As such, the research is quantitative in nature, posing a major limitation to data collection in various organizations based on firms’ internal information-sharing policies. Additionally, the study is limited by aspects of sample sufficiency to generalize the research findings for generalizability.

Assumptions of the Study

The study assumes that research instruments adopted for the study will effectively identify the various data security techniques that have been researched on and implemented with proven effectiveness. Additionally, the study assumes that the selected sample size will provide sufficient insights to draw conclusions on the entire target population, especially businesses adopting various data security approaches.

Definitions

Big data: It is composed of enormous data sets that are complex to collect, store, and analyze, usually beyond the capabilities of conventional data processing software and approaches.

Big data analytics: It is the application of tech-advanced analytical tools and techniques to analyze varied and complex data sets from different sources, which are usually enormous in size (Zettabytes).

Data security: These are processes adopted by firms to secure digitally available information to avoid data corruption and unauthorized access.

Data security infrastructure: It is a collection of computers, networking systems, and the cloud that are integrated to interact to protect critical information systems and assets against cyber and physical threats.

Summary

Heightened threats related to data security indicate the greater need to establish more profound data security policies and infrastructure. In understanding this indispensability of data security, it is crucial to evaluate the effectiveness of various data security techniques adopted by firms. In the analysis, identifying the popularity of the tech advancement threats, such as the most common malware, is crucial to evaluating how effective various data security techniques are for firms where they are employed. In an effort to answer the research questions, the study will also delineate why the growing need to establish a robust data security infrastructure using the best data security techniques is indispensable for firms on a global scale.

References

Bertino, E. (2016). Data Security and Privacy in the IoT. . Proc. 19th International Conference on Extending Database Technology (EDBT). https://doi.org/http://dx.doi.org/10.5441/002/edbt.2016.02

Ernest Chang, S., & Ho, C. B. (2006). Organizational factors to the effectiveness of implementing information security management. Industrial Management & Data Systems, 106(3), 345–361. https://doi.org/10.1108/02635570610653498

Matturdi, B., Zhou, X., Li, S., & Lin, F. (2014). Big Data Security and Privacy: A Review. China Communications, 11(14), 135–145. https://doi.org/10.1109/cc.2014.7085614

Sun, Y., Zhang, J., Xiong, Y., & Zhu, G. (2016). Data security and privacy in cloud computing. . International Journal of Distributed Sensor Networks, 10, 190903. https://doi.org/https://doi.org/10.1155%2F2014%2F190903

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Overview: As you observed in the LIVE session, there is a connection between chapter three and chapter one. Therefore, as an extension of our week in the mock chapter three from last week, we will write a mock chapter one.

***DETAILS***

Develop a 3-4 page (more is fine) mock chapter one to include the following expectations from the university:

Overview (1-2 well developed paragraphs)

Background and problem statement (1-2 well-developed paragraphs)

Purpose of the study (1 well-developed paragraph)

Significance of the study (1 well developed paragraph)

Research Questions (numbered list)

Limitations of the Study (1 short paragraph)

Assumptions (1 short paragraph)

Definitions (list)

Summary (1 well developed paragraph)