We looked at several topics on global IT strategies, technologies, models, and networking during this course. As you get closer to starting your own dissertation, you will need to choose a topic in your first dissertation class, DSRT-736, so it is essential to start preparing. This week, let us take a look at some topics to consider, and by the end of the week, we could have several ideas for dissertation topics.
Since you have already examined several research articles, another way would be to examine previous dissertations in these areas. Visit the University of Cumberland’s library, go to the Dissertation Database, and locate an interesting topic on global IT. Here are some pointers that will help critically evaluate some viable topics.
Is the topic attainable for a first-time dissertation student?
Is the problem rooted in the literature?
Discuss the topic, the problem the model has been used in the research, and any present findings. Do not read the entire dissertation, as the abstract and chapter one introduction should give a clear understanding of the research.
Build on something your classmate said
Explain why and how you see things differently
Ask a probing or clarifying question
Share an insight from having read your classmates’ postings
Anuradha Rangreji
Week 8 Course Reflection
Impact of Big Data, Big Data Analytics, and Artificial Intelligence
Big data holds valuable information and insights that can be explored and analyzed using
AI and machine learning technologies. Utilizing big data is essential in advancing AI’s decisionmaking capabilities. Big data analytics is a process that combines and analyzes large datasets to
identify patterns and generate actionable insights. This approach allows businesses to make
faster, more informed, and data-driven decisions to improve efficiency, increase revenue, and
boost profits. Big Data, Big Data Analytics, and Artificial Intelligence (AI) have significantly
transformed decision-making in the business world. These technologies have enabled
organizations to make more informed, data-driven decisions that can lead to improved outcomes
and competitive advantages. On the other hand, Data analytics plays a crucial role in decisionmaking across various industries and domains. It involves examining and interpreting data to
derive actionable insights that inform strategic choices, operational improvements, and overall
business direction.
After analyzing a sample of 5323 records from the Data Scientists database as a part of
the study by Lausell (2023), it was discovered that using Big Data for data-based decisionmaking can significantly influence the business or company. The study measured the
effects of Big Data, Artificial Intelligence, real-time Analysis, and data-driven types of
decisions supported by theories to enable organizations to make better and more
intelligent decisions in real-time. The multivariate analysis and structural equations PLSSEM were applied to measure the effects between the variables studied. The
relationships observed in the Big Data construct of structured and semi-structured
indicators indicate that unstructured data could be more beneficial for decision-making
in this research because of the current era of social technology. However, it is worth
noting that there is a weak positive relationship between the Type of Analysis and the
Selection of ML Methods. This finding indicates that companies and organizations may
doubt adopting other analysis techniques, such as prescriptive analysis, due to their
complexity. Overall, the results show that positive relationships support the selected
hypothesis to help increase competitive advantage in the data-driven decision-making
process. By using real-time prescriptive analytics, organizations can make faster and
better decisions, ultimately increasing their business value.
The influence of real-time data flow versus offline data processing with Big Data and
various types of analytics is significant. It can impact decision-making, insights generation, and
overall business strategies. The research findings show a significant correlation between the
Artificial Intelligence variable and the Analysis Type variable, supporting the second hypothesis.
This suggests that many businesses use Artificial Intelligence techniques with Type Analysis to
make more informed, data-driven decisions. Another observation of the study is the strong
correlation between the type of analysis and decision-making. The analysis type encompasses a
range of techniques such as optimization, simulation, heuristics, and multi-criteria decisionmaking, which are fortified by enablers such as deep learning, cognitive computing, and big data.
A growing number of professionals, particularly data scientists, are leveraging machine learning
technologies to automate tasks and augment decision-making prowess.
Reference
Lausell Lopez, E. A. (2023). The Impact of Big Data and Artificial Intelligence on the
Types of Business Analysis (Order No. 30571072). Available from ProQuest
Dissertations & Theses Global.
(2838913288).https://www.proquest.com/dissertations-theses/impact-big-dataartificial-intelligence-on-types/docview/2838913288/se-2
James Hutchins
Week 8 – Examining a Dissertation
The dissertation selected is Best Practices for Developing Cybersecurity Graduates
for the Global Cybersecurity Workforce by Emeka Ejikeme (2023). The dissertation’s author
discusses the increasing need for cybersecurity professionals in today’s global information
systems, and the lack of qualified persons to fill all the openings. The dissertation attempts to
examine what the needs are, and why there is a gap of available talent. He poses the research
question “What are the best practices for developing university graduates for the global
cybersecurity workforce?” (p. 6).
The dissertation’s research was conducted solely through literature reviews, examining
the findings of other 28 authors and through coding, determining common patterns in what has
been identified as significant causes for these global gaps as well as approaches to address the
gaps. The author presented two frameworks for analyzing the findings, though settled on the
CIMO framework (Context, Intervention, Mechanism, and Outcome) “because it provided
opportunities to examine and test the problem statement and the RQ with its context,
intervention, mechanism, and outcome” (Ejikeme , 2023, p. 6). The author presents three main
findings (Ejikeme , 2023, p. ii): (a) the need to establish organizational learning environments
including apprenticeships, (b) need to establish a global cybersecurity skill set with institutional
support, and (c) need to build an enduring pipeline for cybersecurity professionals.
This is my second class in the Ph.D. program at the University of the Cumberlands and I
have not yet identified my intended dissertation topic, though I have always worked in the field
of information security and intend my topic to be within this area. I chose this dissertation to
discuss because it examines the critical shortfall of information security (cybersecurity)
professionals that currently exists globally. I feel this study is well within the ability of a firsttime dissertation student since it examines already published material to discern new information
about best practices in a specific well-defined area. For my dissertation, I believe I am most
likely to perform a quantitative analysis study, as opposed to the strictly literature search if the
present dissertation.
References
Ejikeme, E. (2023). Best practices for developing cybersecurity graduates for the global
cybersecurity workforce (Order No. 30522439). Available from ProQuest Dissertations
& Theses Global. (2845053395). Retrieved from https://www.proquest.com/dissertationstheses/best-practices-developing-cybersecurity-graduates/docview/2845053395/se-2
