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Higher Nationals
Internal verification of assessment decisions – BTEC (RQF)
INTERNAL VERIFICATION – ASSESSMENT DECISIONS
BTEC Higher National Diploma in Computing
Programme title
Assessor
Internal Verifier
Unit 16: Computing Research Project (Pearson Set)
Unit(s)
Research Proposal – Big Data
Assignment title
K.A. Thilini Dilhara / RAT00176975
Student’s name
List which assessment criteria Pass
the Assessor has awarded.
Merit
Distinction
INTERNAL VERIFIER CHECKLIST
Do the assessment criteria awarded match
those shown in the assignment brief?
Y/N
Is the Pass/Merit/Distinction grade awarded
justified by the assessor’s comments on the
student work?
Y/N
Has the work
accurately?
Y/N
been
assessed
Is the feedback to the student:
Give details:
Y/N
• Constructive?
• Linked to relevant assessment criteria?
• Identifying
opportunities
improved performance?
Y/N
for
• Agreeing actions?
Does the assessment decision need amending?
Y/N
Y/N
Y/N
Assessor signature
Date
Internal Verifier signature
Date
Programme Leader signature (if required)
Date
2
Confirm action completed
Remedial action taken
Give details:
Assessor signature
Date
Internal
signature
Date
Verifier
Programme
Leader
signature (if required)
Date
3
Higher Nationals - Summative Assignment Feedback Form
Student Name/ID
K.A. Thilini Dilhara / RAT00176975
Unit Title
Unit 16: Computing Research Project (Pearson Set)
Assignment Number
Assessor
Submission Date
Date Received 1st
submission
Re-submission Date
Date
Received
submission
2nd
Assessor Feedback:
LO1 Examine appropriate research methodologies and approaches as part of the research process
Pass, Merit & Distinction
Descripts
Grade:
P1
☐
P2 ☐
M1
☐
D1
☐
Assessor Signature:
Date:
Assessor Signature:
Date:
Resubmission Feedback:
Grade:
Internal Verifier’s Comments:
Signature & Date:
* Please note that grade decisions are provisional. They are only confirmed once internal and external moderation has taken place and
grades decisions have been agreed at the assessment board.
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Assignment Feedback
Formative Feedback: Assessor to Student
Action Plan
Summative feedback
Feedback: Student to Assessor
Assessor
signature
Date
Student signature
Date
5
Pearson
Higher Nationals in
Computing
Unit 16: Computing Research Project
(Pearson Set)
Research Project Proposal
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I hereby, declare that I know what plagiarism entails, namely to use another’s work and to present
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Assignment Brief
Student Name /ID Number
K.A. Thilini Dilhara / RAT00176975
Unit Number and Title
Unit 16: Computing Research Project (Pearson Set)
Academic Year
Unit Tutor
Assignment Title
Final Research Project Proposal -Big Data
Issue Date
Submission Date
IV Name & Date
Submission Format:
Research Project Proposal
 The submission is in the form of an individual written report.
 This should be written in a concise, formal business style using single spacing and font size 12.
 You are required to make use of headings, paragraphs and subsections as appropriate, and all
work must be supported with research.
 Reference using the Harvard referencing system.
 Please provide a referencing list using the Harvard referencing system.
 The recommended word limit is minimum 2000 words.
Unit Learning Outcomes:
LO1. Examine appropriate research methodologies and approaches as part of the research process.
Assignment Brief and Guidance:
Big Data
Big data is a term that has become more and more common over the last decade. It was originally
defined as data that is generated in incredibly large volumes, such as internet search queries, data
from weather sensors or information posted on social media. Today big data has also come to
represent large amounts of information generated from multiple sources that cannot be processed
in a conventional way and that cannot be processed by humans without some form of
computational intervention.
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Big data can be stored in several ways: Structured, whereby the data is organised into some form
of relational format, unstructured, where data is held as raw, unorganised data prior to turning
into a structured form, or semi-structured where the data will have some key definitions or
structural form but is still held in a format that does not conform to standard data storage models.
Many systems and organisations now generate massive quantities of big data on a daily basis, with
some of this data being made publicly available to other systems for analysis and processing. The
generation of such large amounts of data has necessitated the development of machine learning
systems that can sift through the data to rapidly identify patterns, to answer questions or to solve
problems. As these new systems continue to be developed and refined, a new discipline of data
science analytics has evolved to help design, build and test these new machine learning and
artificial intelligence systems.
Utilising Big Data requires a range of knowledge and skills across a broad spectrum of areas and
consequently opens opportunities to organisations that were not previously accessible. The ability
to store and process large quantities of data from multiple sources has meant that organisations
and businesses are able to get a larger overall picture of the pattern of global trends in the data to
allow them to make more accurate and up to date decisions. Such data can be used to identify
potential business risks earlier and to make sure that costs are minimised without compromising
on innovation.
However, the rapid application and use of Big Data has raised several concerns. The storage of such
large amounts of data means that security concerns need to be addressed in case the data is
compromised or altered in such a way to make the interpretation erroneous. In addition, the ethical
issues of the storage of personal data from multiple sources have yet to be addressed, as well as
any sustainability concerns in the energy requirements of large data warehouses and lakes.
The theme will enable students to explore some of the topics concerned with Big Data from the
standpoint of a prospective computing professional or data scientist. It will provide the opportunity
for students to investigate the applications, benefits and limitations of Big Data while exploring the
responsibilities and solutions to the problems it is being used to solve.
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Choosing a research objective/question
Students are to choose their own research topic for this unit. Strong research projects are those
with clear, well focused and defined objectives. A central skill in selecting a research objective is
the ability to select a suitable and focused research objective. One of the best ways to do this is to
put it in the form of a question. Students should be encouraged by tutors to discuss a variety of
topics related to the theme to generate ideas for a good research objective.
The range of topics discussed on Big Data, could cover the following areas:
 Storage models
 Cyber security risks
 Future developments and driving innovation.
 Legal and ethical trade-offs
Project Proposal should cover following areas.
1. Definition of research problem or question. (This can be stated as a research question,
objectives, or hypothesis)
2. Provide a literature review giving the background and conceptualisation of the proposed area
of study. (This would provide existing knowledge and benchmarks by which the data can be
judged)
3. Examine and critically evaluate research methodologies and research processes available.
Select the most suitable methodologies and the process and justify your choice based on
theoretical/philosophical frameworks. Demonstrate understanding of the pitfalls and
limitations of the methods chosen and ethical issues that might arise.
4. Draw points (1–3, above) together into a research proposal by getting agreement with your
tutor.
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Useful links
Useful resources for underlying principles, examples of articles and webinars on the theme:
Resource
Number
Type of
Resource
Resource Titles
Links
1
Article
6V’s of Big Data
https://www.geeksforgeeks.org/5vs-of-big-data/
2
Article
Business Ethics and Big Data
https://www.ibe.org.uk/resource/business-ethics-and-big-data.html
3
Article
What is Big Data Security? Challenges & Solutions
https://www.datamation.com/bigdata/big-data-security/
4
Article
What is Big Data?
https://www.oracle.com/uk/bigdata/what-is-big-data/
5
Magazine
Information Sciences
https://www.sciencedirect.com/jou rnal/information-sciences
6
Magazine
Big Data Research
https://www.sciencedirect.com/jou rnal/big-data-research
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Report
Big Data & Investment Management:
The Potential to Quantify Traditionally Qualitative factors
https://tinyurl.com/yff4uenz
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Webinar
Big Data Sources & Analysis Webinar
https://tinyurl.com/2p85d7mb
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Video
Big Data In 5 Minutes | What Is Big Data?| Introduction To
https://www.youtube.com/watch?v =bAyrObl7TYE
Big Data |Big Data Explained
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Video
Challenges of Securing Big Data
https://www.youtube.com/watch?v =3xIuIcPzMVs
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Video
The Importance of Data Ethics
https://www.youtube.com/watch?v =gLHMhCtxEYE
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Book
A Bite-Sized Guide to Visualising Data
https://tinyurl.com/38d6thsk
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Resource
Number
Type of
Resource
Resource Titles
Links
13
Book
Business Intelligence Strategy and Big Data Analytics
https://www.sciencedirect.com/bo ok/9780128091982/businessintelligencestrategy-and-big-data-analytics
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Book
Principles and Practice of Big
https://www.sciencedirect.com/book/9780128156094/principles-andpractice-of-big-data
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Book
Systems Simulation and
Modelling for Cloud Computing and Big Data Applications
https://tinyurl.com/2s3wkehn
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Journal
Big Data in Construction: Current Applications and Future
https://www.mdpi.com/25042289/6/1/18
Opportunities
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Journal
Big Data with Cloud Computing: Discussions and Challenges
https://www.sciopen.com/article/pdf/10.26599/BDMA.2021.9020016.pdf
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Journal
Mobile Big Data Solutions for a better Future
https://tinyurl.com/hpk2zvvw
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Journal
The social implications, risks, challenges and opportunities https://tinyurl.com/yw593svk
of big data
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Journal
Policy discussion – Challenges of big data and analytics
https://tinyurl.com/kyb3j6x7
driven demand-side management
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Journal
Explore Big Data Analytics Applications and Opportunities:
A Review
https://tinyurl.com/597j8nd3
22
Journal
What is Big Data?
https://www.oracle.com/cl/a/ocom/
4421383.pdf
23
Journal
Towards felicitous decision making: An overview on
https://www.sciencedirect.com/science/article/abs/pii/S002002551630 4868
challenges and trends of Big Data
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Journal
Critical analysis of Big Data challenges and analytical
methods
docs/what-is-big-data-ebook-
https://www.sciencedirect.com/science/article/pii/S014829631630488X
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Resource
Number
Type of
Resource
Resource Titles
Links
25
Journal
Big Data Security Issues and Challenges
https://tinyurl.com/wabx7zya
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Journal
IoT Big Data Security and Privacy Versus Innovation
https://ieeexplore.ieee.org/abstract /document/8643026
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Journal
Big Data Security and Privacy Protection
https://www.atlantis-press.com/proceedings/icmcs18/25904185
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Journal
Big data analytics in Cloud computing: an overview
https://journalofcloudcomputing.springeropen.com/articles/10.1186/
s13677-022-00301-w
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Grading Rubric
Grading Criteria
P1 Produce a research proposal that clearly defines a research
question or hypothesis, supported by a literature review.
P2 Examine appropriate research methods and approaches to
primary and secondary research.
M1 Analyse different research approaches and methodology
and make justifications for the choice of methods selected
based on philosophical/theoretical frameworks.
D1 Critically evaluate research methodologies and processes
in application to a computing research project to justify
chosen research methods and analysis
Achieved
Feedback
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Research Proposal Form
Student Name
K.A. Thilini Dilhara
Student number
RAT00176975
Centre Name
ESoft Metro College - Ratnapura
Unit
Unit 16: Computing Research Project (Pearson Set)
Tutor
P.P. Piyumal
Date
2024/02/01
Proposed title
Big data analytics for Business Intelligence
Section One: Title, objective, responsibilities
Title or working title of research project (in the form of a question, objective or hypothesis): Research
project objectives (e.g. what is the question you want to answer? What do you want to learn how
to do? What do you want to find out?): Introduction, Objective, Sub Objective(s), Research
Questions and/or Hypothesis
Big data analytics for Business Intelligence
This study will therefore attempt to ascertain the influence and effects that Big Data
Analytics has on decision-making and operational efficiency. Other areas that will be
identified are the benefits and challenges of implementation, cost implications, and the return
on investment and strategies to enhance the quality of data integration. The duties will be
conducted by the use of structured questionnaires and interviews, while appropriate methods
will be employed in analysing the data by both statistical and qualitative methods, and
findings reported clearly and concisely together with the main insights and
recommendations.
Section Two: Reasons for choosing this research project
Reasons for choosing the project (e.g. links to other subjects you are studying, personal interest,
future plans, knowledge/skills you want to improve, why the topic is important): Motivation,
Research gap
This research project was chosen because Big Data Analytics is taking a central role in
changing business intelligence and enhancing the process of decision making. As
organizations begin to rely more and more on data-driven insight to stay competitive, it
becomes very important to understand the benefits, challenges, and cost implications
involved in the implementation of Big Data Analytics. This work allows considering options
for improving data quality and its integration, which is very useful in making suggestions to
businesses that seek to exploit advanced analytics for operational efficiency and innovation.
Section Three: Literature sources searched
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Use of key literature sources to support your objective, Sub Objective, research question and/or
hypothesis: Can include the Conceptual Framework
Objective – User Testing (Site Name) (Anon., n.d.)
Sub objective - Editage (Site Name) (Anon., n.d.)
Hypothesis- engo (Site Name) (Anon., 2024) etc.
Section Four: Activities and timescales
Activities to be carried out during the research project (e.g. research, development, analysis of ideas,
writing, data collection, numerical analysis, tutor meetings, production of final outcome, evaluation,
writing the report) and How long this will take:
Milestone
Propose completion date
Project Initiation
2 weeks
Literature Review
2 weeks
Research approach
1 week
Data Collection
3 weeks
Data Analysis
2 weeks
Conclusion and Recommendations
2 weeks
Final Review and Editing
2 weeks
Submission
2 weeks
Section Five: Research approach and methodologies
Type of research approach and methodologies you are likely to use, and reasons for your choice:
What your areas of research will cover: Research Onion; Sample Strategy/Method; Sample Size
Information was collected by sending google forms through WhatsApp, Facebook
messenger. In addition, user reviews on google were taken into consideration.
It was decided to use semi-structured interviews as the primary data collection method for
this study.
Comments and agreement from tutor
Comments (optional):
I confirm that the project is not work which has been or will be submitted for another qualification
and is appropriate.
Agreed Yes ☐ No ☐
Name
Date
Comments and agreement from project proposal checker (if applicable)
Comments (optional):
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I confirm that the project is appropriate.
Agreed Yes ☐ No ☐
Name
Date
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Research Ethics Approval Form
All students conducting research activity that involves human participants or the use of data
collected from human participants are required to gain ethical approval before commencing
their research. Please answer all relevant questions and note that your form may be returned
if incomplete.
Section 1: Basic Details
Project title:
Big data analytics for Business Intelligence
Student name:
K.A. Thilini Dilhara
Student ID number:
RAT00176975
Programme:
Software Engineering
School:
ESoft Metro College- Ratnapura
Intended research start date:
2024/02/01
Intended research end date:
2024/05/31
Section 2: Project Summary
Please select all research methods that you plan to use as part of your project








Interviews:
Questionnaires:
Observations:
Use of Personal Records:
Data Analysis:
Action Research:
Focus Groups:
Other (please specify):
☐
☒
☐
☐
☐
☐
☐
☐ ...........................................................
Section 3: Participants
Please answer the following questions, giving full details where necessary.
Will your research involve human participants?
Who are the participants? Tick all that apply:
Age 12-16 ☐
Young People aged 17–18 ☐
Adults ☒
How will participants be recruited (identified and approached)?
With the support of friends, relatives and acquaintances, communication was made
through social media.
Describe the processes you will use to inform participants about what you are doing:
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Here, sends the Google form to the relevant group using social media, WhatsApp and
send a description about the research at the same time.
Studies involving questionnaires:
Will participants be given the option of omitting questions they do not wish to answer?
Yes ☒
No ☐
If “NO” please explain why below and ensure that you cover any ethical issues arising from this.
Studies involving observation:
Confirm whether participants will be asked for their informed consent to be observed.
Yes ☒
No ☐
Will you debrief participants at the end of their participation (i.e. give them a brief explanation of
the study)?
Yes ☒
No ☐
Will participants be given information about the findings of your study? (This could be a brief
summary of your findings in general)
Yes ☒
No ☐
Section 4: Data Storage and Security
Confirm that all personal data will be stored and processed in compliance with the Data Protection
Act (1998)
Yes ☒
No ☐
Who will have access to the data and personal information?
No one other than the researcher would have access to the data.
During the research:
Where will the data be stored?
Entered into an Excel file and stored in Google Drive.
Will mobile devices such as USB storage and laptops be used?
Yes ☒
No ☐
If “YES”, please provide further details:
After the research:
Where will the data be stored?
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Data would be stored on Google drive.
How long will the data and records be kept for and in what format?
The data would be kept for a time period of four months and would be in excel format.
Will data be kept for use by other researchers?
Yes ☐
No ☒
If “YES”, please provide further details:
Section 5: Ethical Issues
Are there any particular features of your proposed work which may raise ethical concerns?
If so, please outline how you will deal with these:
Section 6: Declaration
I have read, understood and will abide by the institution’s Research and Ethics Policy:
Yes ☒
No ☐
I have discussed the ethical issues relating to my research with my Unit Tutor:
Yes ☒
No ☐
I confirm that to the best of my knowledge:
The above information is correct and that this is a full description of the ethics issues that may arise
in the course of my research.
Name:
K.A. Thilini Dilhara
Date:
05/20/2024
Please submit your completed form to: ESOFT Learning Management System (ELMS)
THE RESEARCH PROPOSAL
Big data analytics for Business Intelligence
By
K.A. Thilini Dilhara
RAT00168536
Research Proposal Submitted in accordance with the requirements for the
COMPUTING
RESEARCH
PEARSON’S
HND
IN
PROJECT
SOFTWARE
MODULE
OF
ENGINEERING
PROGRAMME
at
ESOFT METRO CAMPUS
Name of research Tutor: P.P. Piyumal
the
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ACKNOWLEDGMENT
Success and the outcome of this assignment required much guidance and assistance from
people, whom I am extremely lucky to have got all along with the completion of my
assignment. Whatever I did is only due to such guidance and assistance, and I shall not forget
to thank them. I respect and thank Mr. Piyumal for providing me with the platform to do this
assignment. He provided all support and guidance due to which I could complete the
assignment on time. We are really very thankful to you for such nice support and guidance.
We again thank Mr. Piyumal.
2
ABSTRACT
The following research work shall focus on how Big Data Analytics influences Business
Intelligence with a view to establishing how advanced techniques of data analysis could help
improve decision-making, operational efficiency, and generally business performance. This
is a mixed-method approach using structured questionnaires and interviews among the
banking sector professionals to explore the practical challenges and benefits that come along
with the implementation of Big Data Analytics. Results show that these benefits within such
cost implications and integration issues are overwhelming in value-added decision-making,
cost savings, and competitive advantages. The study points to the need for improved data
quality, sophisticated analysis methods, and a good data governance framework to ensure Big
Data Analytics can be exploited to its full potential in business environments.
3
EXECUTIVE SUMMARY
The impact of Big Data Analytics on Business Intelligence is investigated in terms of practical
applications, benefits, and challenges associated with them. For this current research, a mixedmethod approach will be harnessed wherein quantitative data will be sought through a
structured questionnaire from 150 experts, while the qualitative insights will be culled from 25
banking professionals' interviews. Results show that Big Data Analytics has colossal power in
enhancing BI by improvement in the accuracy of decision-making, operational efficiency, and
market insights. Indeed, organizations that apply these analytics derive very many benefits,
which include cost reduction and lead over their competitors. However, there are challenges
associated with poor data quality, complexities of integration, and a need for highly skilled
personnel. Equally important are the ethical and legal considerations on the privacy and
security of data. The research thus identifies that Big Data Analytics offers huge advantages,
but unlocking its full potential for business intelligence requires strict adherence to ethical
standards and resolution of these challenges.
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CONTENTS
Contents
ACKNOWLEDGMENT .................................................................................................................. 1
Abstract ...................................................................................................................................... 2
CONTENTS .................................................................................................................................. 4
LIST OF TABLES ........................................................................................................................... 7
LIST OF FIGURES ......................................................................................................................... 8
1.1.
Introduction ................................................................................................................. 9
1.2.
Purpose of research ................................................................................................... 10
1.3.
Significance of the Research ..................................................................................... 10
1.4.
Research objectives ................................................................................................... 11
1.5.
Research Sub objectives ............................................................................................ 12
1.6.
Research questions .................................................................................................... 12
1.7.
Hypothesis ................................................................................................................. 12
LITERATURE REVIEW ................................................................................................................ 13
2.1.
Literature Review ...................................................................................................... 13
2.2.
Conceptual framework .............................................................................................. 13
METHODOLOGY ....................................................................................................................... 16
3.1.
Research philosophy ................................................................................................. 17
3.2.
Research approach..................................................................................................... 17
3.3.
Research strategy....................................................................................................... 17
3.4.
Research Choice ........................................................................................................ 17
3.5.
Time frame ................................................................................................................ 17
3.6.
Data collection procedures ........................................................................................ 18
3.6.1.
Type of Data ...................................................................................................... 18
3.6.2.
Data Collection Method .................................................................................... 18
3.6.3.
Data Collection and Analyse Tools ................................................................... 18
3.7.
Sampling.................................................................................................................... 19
3.7.1.
Sampling Strategy ............................................................................................. 19
3.7.2.
Sample Size ........................................................................................................ 19
3.8.
The selection of participants ..................................................................................... 20
reseach report format .............................................................................................................. 21
4.1 Section 1 – Introduction ..................................................................................................... 22
Purpose of research .............................................................................................................. 23
5
Significance of the Research ................................................................................................ 24
Research objectives .............................................................................................................. 24
Research Sub objectives....................................................................................................... 28
Research questions ............................................................................................................... 28
Hypothesis ............................................................................................................................ 29
LITERATURE REVIEW ................................................................................................................ 33
5.1 Literature Review ........................................................................................................... 33
5.2 Enhancing Business Intelligence with Big Data Analytics: Effectiveness of Risk
Identification and Mitigation, and Implementation Challenges .......................................... 36
5.3 Decision-Making Efficiency .......................................................................................... 38
5.4 Organizational Risk Posture........................................................................................... 38
5.5 Technological, Organizational, and Cultural Barriers, Minimising Barriers ................. 39
5.6 Cost-Effectiveness and Cost Implications ..................................................................... 40
5.7 Research GAP ................................................................................................................ 41
5.8
Conceptual framework .............................................................................................. 41
METHODOLOGY ....................................................................................................................... 45
6.1 Research methodology ................................................................................................... 45
6.2
Research philosophy ................................................................................................. 48
6.3
Research approach..................................................................................................... 49
6.4
Research strategy....................................................................................................... 50
6.5
Research Choice ........................................................................................................ 51
6.6
Time frame ................................................................................................................ 51
6.7
Data collection procedures ........................................................................................ 52
Type of Data..................................................................................................................... 52
6.8
Data Collection Method........................................................................................ 53
6.8.1
Data Collection and Analyse Tools ................................................................... 55
6.9
Sampling.................................................................................................................... 56
6.9.1
Sampling Strategy ............................................................................................. 56
6.9.2
Sample Size ........................................................................................................ 60
6.10 The selection of participants ..................................................................................... 61
Research approach ............................................................................................................... 62
PRESENTATION OF RESULTS .................................................................................................... 62
7.1 Demographic Analysis ................................................................................................... 62
7.2 Correlation Analysis ...................................................................................................... 64
8.1 Discussion ...................................................................................................................... 64
6
8.2 Limitations ..................................................................................................................... 64
9.1 Future Improvements ..................................................................................................... 65
10.1 Personnel Reflection .................................................................................................... 65
References................................................................................................................................ 66
7
LIST OF TABLES
Table 1: Hypothesis ........................................................ Ошибка! Закладка не определена.
Table 2: Time Frame ................................................................................................................ 16
Table 3: Questionnaire structue ............................................................................................... 58
Table 4: Gannt chart data table ............................................................................................... 60
8
LIST OF FIGURES
Figure 1: The Research Onion Model (Anon., n.d.) ...... Ошибка! Закладка не определена.
Figure 2: Research objectives ( Jain, 2023) ............................................................................. 25
Figure 3: Types of Research Objectives ( Jain, 2023) ............................................................. 26
Figure 4: Conceptual framework ............................................................................................ 43
Figure 5:Deductive process in research appoach (Anon., n.d.) ............................................... 48
Figure 6:Inductive process in research appoach (Anon., n.d.) ................................................. 48
Figure 7: Data collection (Anon., n.d.) ................................................................................... 53
Figure 8: Gantt chart ............................................................................................................... 59
9
INTRODUCTION
1.1.
Introduction
With the current digital transformation, a lot of platforms have experienced an explosion in
data generation, and Big Data has emerged to be very critical to organizations. Big Data refers
to huge, complex, and voluminous data beyond what traditional data-processing applications
can efficiently manage. Such data is derived from several sources, such as social media, web
queries, transaction data, and sensor data from the IoT. This may be structured or unstructured
data.
Big data analytics for business intelligence has been emerging as the most critical tool for an
organization that wants to get the most out of this sea of data. Equipped with sophisticated
algorithms, machine learning models, and advanced analytical tools, businesses can now
extract meaningful insights from these large data sets. These same factors are very important
in decision making, trend identification, outcome prediction, and carrying out any strategic
initiatives.
Big Data Analytics embedded within the systems of Business Intelligence gives organizations
a broader view of business operations, market conditions, and consumer behaviour. This gives
an all-rounded perspective on enhancing operational efficiency, optimizing marketing
strategies, and improving customer experiences. In addition to that, it identifies future risks and
opportunities that give a business a basis for proactive decision-making and innovation.
Though rich in various advantages of Business Intelligence, Big Data Analytics is not able to
combat some of the challenges. The challenges to this technology are related to data privacy,
security, and ethical considerations. Dealing with such volumes of data requires strong
frameworks for data governance, tight measures of cybersecurity to prevent leakage and
mismanagement of information, and due consideration of the ethical considerations regarding
data collection, storage, and usage in order to remain under the legal standards and be able to
keep pace with the growing public concerns and mistrust.
It is research directed at probing various applications, benefits, and limitations of Big Data
Analytics in relation to Business Intelligence. Such research will be revealing of organizations'
potential in the use of data-driven insight to gain competitive advantage, thus revealing the
transformative potential of Big Data in shifting business operations and strategies.
10
1.2.
Purpose of research
This study aims at the following objectives: main objective of this research is to establish how
Big Data Analytics can improve BI in organizations. This paper’s objective is to explore the
ways Big Data Analytics can be effectively leveraged for business purposes to inform
implementation strategies for the industry.
Specifically, the research will:

Identify Key Applications: Discuss how Big Data Analytics is being applied in Business
Intelligence depending on the different business requirements like; Customer Analysis,
predictive analysis, and process optimization.

Evaluate Benefits: Examine the papers that discuss the aggregate advantages realized by
organizations as they adopt Big Data Analytics into their BI systems. This entails better
accuracy in decision making, better market knowledge, amongst others and increased
flexibility in operations.

Assess Challenges: Research on the real issues that managers face when adopting Big Data
Analytics for BI. These will include aspects like quality of the data, challenges of
integration and that the matters will require special skills and other implements.

Explore Case Studies: Also include typical case studies of various organizations that have
integrated Big Data Analytics for Business Intelligence. These case studies will look at
success stories, new ideas, and future possibilities based on their stories and experiences.

Recommend Strategies: Resolved recommendations and actionable plans should be offered
to companies interested in utilizing BD Analytics in BI. The recommendations will align
on the strategies of how to minimize the major challenges, how to secure data and privacy,
how to optimize the return on investment.

Address Ethical and Legal Considerations: Analyse the broader issues of Ethics and Law
when it comes to the use of Big Data Analytics in BI; important issues such as Data
Protections, Laws, and Ethical uses of Data.
1.3.
Significance of the Research
The importance of this study on Big Data Analytics for Business Intelligence, BI is therefore
seen in its ability to shift the business dynamics. The study’s importance can be articulated
through
several
key
dimensions:
11

Enhanced Decision-Making: Thus, focusing on the concept of Big Data Analytics and its
usefulness in enhancing decision-making, this research demonstrates the potential of
businesses to make more sound, accurate, and timely decisions. Awareness of how data
analysis is to be applied assists organizations change from the use of gross best guess
estimations to the use of definitive statistical information.

Operational Efficiency: They help to reveal opportunities for the use of Big Data Analytics
in optimization, reduction of costs and increase in efficiency. The study enlightens the
actual practices of managing and improving the supply chain processes, as well as
increasing the efficiency of resources usage.

Competitive Advantage: In the current business environment, presumably, the capability
of exploiting information to create competitive edges is of paramount importance. Thus,
the described research proves the idea that Big Data Analytics can be used by businesses
for anticipating competitors, nurturing their knowledge about market trends and customers’
behaviour, as well as the potential opportunities on the market.

Risk Management: This paper focuses on the importance of Big Data Analytics in risk
management processes. Big data, therefore, allows organizations to identify trends or
patterns that may pose a threat, thus enabling the organization to prepare or prevent any
adverse effect from happening or if it happens, its impact is greatly reduced.

Innovation and Growth: In this context, it is necessary to explain how Big Data Analytics
can boost innovations and support organizational growth; the examples discussed in the
framework of the research reveal this cooperation. Information generated from data assists
a firm in designing new products, services or operations and business models, thus creating
new layers of income.
1.4.
Research objectives
It is, therefore, the aim of the present research to review the sources and application of Big
Data Analytics in the field of Business Intelligence and determine the changes the are in the
process of bringing to organisational improvement of decision-making and efficiency, and
gain in competitive advantage. The purpose of this research is to determine the major
applications of BDA for BI; assess the quantifiable advantages of its application while
categorizing the potential difficulties that may occur when applying the BDA for BI. Further,
the research aims at identifying the best practices through literature review and disseminating
real-world examples while providing strategic plan, and this study will consider the ethical
and legal issues of the practice. Thus, achieving these objectives, the research will provide
businesses with the necessary knowledge and tools in the field of Big Data Analytics
application for proper decision-making and further development.
12
1.5.
Research Sub objectives
The research sub-objectives are as follows: Understand the nature and type of applications that
have experienced a positive impact by big data analytics in BI Recognize the tangible value or
the ROI by measuring better decision-making and operational worth Analyse the practical
implementation issues belonging to various industries. Furthermore, there will be a qualitative
study of specific good and, at the same time, inspiring cases of VR implementation,
identification of real-life challenges of VR projects, and recommendations on how to solve
them and get the most out of VR investments, as well as crucial issues of ethical and legal
compliance in managing VR data. Consequently, these sub-objectives will seek to present a
holistic view of big data analytics in improving BI plus facilitating the achievement of strategic
development goals.
1.6.
Research questions
1. What is a Big Data Analytical solution, and how it helps Business Intelligence?
2. BI and Big Data Analytics have emerged to become main components within most
organizations' information systems nowadays. However, what has the organization
concretely benefited from the use of Big Data Analytics in its BI system?
3. The following is a question that enumerates common challenges organizations have in the
implementation of Big Data Analytics for Business Intelligence.
4. Precisely, which of the organizations has succeeded in implementing big data analytics for
business intelligence?
5. What are the moral and legal demands that need to be met with regard to the application of
Big Data Analytics for Business Intelligence?
1.7.
Hypothesis
H1
Decision Making: Big Data Analytics enhances the accuracy and timeliness of
decision-making in any organization.
H2
Operational Efficiency: Integrating Big Data Analytics will increase
operational efficiency while reducing costs.
H3
Competitive Advantage: The proper functioning of Big Data Analytics offers
a competitive advantage to the business by deeply understanding the market
trends and behaviour of customers more closely.
H4
Implementation Challenges: Organizations face considerable challenges—
.data quality and integration issues—in implementing Big Data Analytics for
Business Intelligence.
Table 1: Hypothesis
13
LITERATURE REVIEW
2.1. Literature Review
There are many articles and publications on Big Data Analytics for Business Intelligence, and
literature reviews show it has had a remarkable impact on organizational strategies within
modernity. Various studies suggest that Big Data Analytics is thoroughly used to increase
business intelligence to make better decisions, operate more effectively, and hold a
competitive position within organizations. Big Data Analytics has been applied in aspects
such as customer segmentation, predictive modelling, and real-time analytics in
organizations, enabling an organization to understand the behaviours of consumers towards
its product and also to achieve effective operation of its works. Research on this topic shows
that if Big Data Analytics is integrated correctly, it is likely to bring in the following key
benefits: more accurate decisions, as well as reduced costs and increased efficiency. At the
same time, the literature reflects major challenges related to data quality, integration
problems, and requirements of specialized skills. Thirdly, real case examples and studies
bring insight into best practices and successful implementations. The debate, therefore,
emphasizes more on ethical and legal issues so that it puts a premium on data privacy,
security, and regulation compliance. Broadly speaking, the literature underlines both the
potential of Big Data Analytics for transformation and the difficulties involved in its
application within Business Intelligence.
2.2.
Conceptual framework
The conceptual framework of Big Data Analytics in Business Intelligence captures the flow
from generation of data to actionable insights. It all begins with these various Big Data sources,
like social media and transaction records; it proceeds to store and manage them with the help
of data lakes and cloud solutions. Extraction of this data from these sources is worked on by
analytical tools and techniques such as machine learning and data mining to come up with
insights. These insights are fed into BI systems for visualization and reporting that support
informed decisions. Amongst others, it also considers data quality and ethical considerations
that need to be addressed if Big Data is to serve well the intentions of using it effectively to
gain strategic business advantage.
P4. Apply appropriate analytical tools, analyse research findings and data
Only with thе hеlp of sеvеral analytical tools and tеchniquеs will onе bе ablе to accomplish a
systеmatic analysis of rеsеarch findings and data rеlatеd to BI applications basеd on big data
analytics. Such tools еxtract mеaningful insights from thе information, thus driving stratеgic
dеcision-making for improvеd pеrformancе in businеss.
Analytical Tools and Tеchniquеs
Dеscriptivе Analytics: This is thе first phasе, which mеrеly summarizеs historical data to
еxtract pattеrns and trеnds. Thе most common tools in businеss intеlligеncе appliеd in this arеa
14
arе SQL and Excеl, еspеcially in thе arеas of writing rеports and gеnеrating charts and graphs.
Dеscriptivе analytics givеs insight into what has happеnеd, thus bеing a stеpping stonе for
advancеd analytics.
Prеdictivе Analytics: It makеs usе of statistical modеls and machinе lеarning algorithms in
prеdicting thе occurrеncе of futurе еvеnts basеd on past еxpеriеncеs in thе form of data. During
thе procеss, somе vеry important tеchniquеs arе involvеd, such as rеgrеssion analysis, timе
sеriеs analysis, and machinе lеarning modеls likе dеcision trееs and nеural nеtworks. Also in
widе usе for this purposе arе tools such as R, Python, and SAS. For instancе, prеdictivе
analytics can bе usеd by a rеtailеr to dеtеrminе futurе salеs from past trеnds and othеr factors
likе sеasonality and еconomic conditions.
Prеscriptivе Analytics: This is thе most advancеd rеcommеndation stagе, basеd on prеdictions
coming from prеdictivе analytics. It typically involvеs optimization algorithms and simulation
tеchniquеs in ordеr to dеtеrminе thе bеst coursе of action. Tools commonly usеd includе IBM's
CPLEX and Gurobi. Application of prеscriptivе analytics in optimizing supply chain
managеmеnt can hеlp in rеcommеnding thе bеst lеvеl of invеntory and dеlivеry schеdulе,
which will lеast cost whilе maximizing thе sеrvicе lеvеls.
Data Mining: It involvеs thе implеmеntation of tеchniquеs for clustеring, classification, and
association rulе lеarning to allow thе еxtraction of pattеrns and rеlationships from largе
datasеts. Extraction of hiddеn insights to a grеat dеal is aidеd by data mining tools availablе
likе Wеka, RapidMinеr, and KNIME. For instancе, basеd on thеir transaction bеhaviors, a bank
may sеgmеnt its customеrs using data mining and adopt suitablе markеting stratеgiеs.
This tеchnology aids businеss systеms in handling big data еfficiеntly and еxеcuting complеx
computations in a distributеd computing еnvironmеnt. For instancе, an е-commеrcе company
might bе using Hadoop to analyzе clickstrеam data from its wеbsitе in an attеmpt to undеrstand
customеr bеhavior and bеttеr thе usеr еxpеriеncе .
Analysis of Rеsеarch Findings
Studiеs havе also shown that businеssеs using big data analytics for businеss intеlligеncе
actually pеrform bеttеr in most opеrations. For instancе, according to Davеnport and Dyché,
2013, businеssеs utilizing big data analytics havе morе than doublе thе chancеs of bеing top
pеrformеrs within thеir rеspеctivе sеctors comparеd to thosе that did not. In thе procеss, thеy
bеnеfitеd through incrеasеd rеvеnuеs, rеducеd costs, and grеatеr customеr satisfaction.
15
Casе studiеs furthеr indicatе rеal-world applications and thе rеlatеd advantagеs of big data
analytics. For еxamplе, Walmart dеploys big data analytics to еnhancе supply chain еfficiеncy
and invеntory managеmеnt, saving hundrеds of millions of dollars whilе also improving
product availability. In a similar way, Nеtflix makеs usе of prеdictivе analytics to providе
customizеd contеnt rеcommеndations, which contributеd grеatly to kееping subscribеrs and
thus motivating thеm to spеnd morе timе watching contеnt .
Conclusion:
Big data analytics intеgratеd into thе procеssеs of BI has substantial advantagеs that accruе to
businеssеs. Dеscriptivе, prеdictivе, and prеscriptivе analytics, whеn usеd togеthеr with data
mining and big data tеchnologiеs, companiеs arе ablе to dеrivе intеnsе insight into thеir
opеrations, forеsее futurе trеnds, and еnablе thеm to makе informеd dеcisions. Furthеr
еvolution of thе tools and tеchniquеs of analysis holds еvеn grеatеr potеntial for an еnhancеd
businеss intеlligеncе driving innovation.
Figure 1: Conceptual framework
16
METHODOLOGY
One may define research methodology as the procedures or techniques involved in the
identification, selection, processing, and analysis of information regarding a topic. Through the
methodology section, a research paper enables the reader to make a critical evaluation
regarding the overall validity and reliability of any study. Should you be designing a research
methodology for your dissertation, thesis, or whatever formal research effort, Saunders'
Research Onion displays the various choices one will have to make. You'll need to make a raft
of decisions as you move inwards from the outside of the onion, ranging from high-level and
philosophical to tactical and practical in nature. Furthermore, it follows basically the same
format as the methods Section does. Saunders' research onion is ordinal, but it's a useful tool
nevertheless for considering methodology holistically. It at least makes clear the choices you
have to make about your research design.
Research onion contain six layers;






Philosophy
Approach
Strategy
Choice
Time Horizon
Data collection tools
Figure 1: The Research Onion Model (Anon., n.d.)
17
3.1. Research philosophy
A research philosophy is a belief about the way in which data about a phenomenon should be
gathered, analysed and used. (Anon., n.d.)
The guiding research philosophy of this research on big data analytics in Business Intelligence
is majorly Pragmatism. This method deals with real practical outcomes and effective
solutions, thereby combining both quantitative and qualitative methods to understand how
big data analytics enhances decision making and operational efficiency. This will also make
use of Positivism to provide empirical evidence and measurable insights, while Interpretivism
provides context and understanding of how organizations use data. Finally, it is Critical
Realism that informs the study in assessing the deep structures and mechanisms that influence
Big Data Analytics in application. This integration provides a superlative view of Big Data
in BI in practice and theory.
3.2.
Research approach
It was decided to use semi-structured interviews as the primary data collection method for
this study.
3.3.
Research strategy
Quantitative survey
3.4.
Research Choice
Quantitative research is considered the best option as compared to qualitative because it's more
scientific, objective, quicker, focused, and acceptable. The qualitative may be the best option
when one is not even sure what to expect, for instance, in cases where one does not know the
problem or how to fix it. It's a tool for figuring out what the problem is and how to solve it.
3.5.
Time frame
18
Table
Time
Frame
Table
1: 2:
Time
Frame
3.6.
Data collection procedures
3.6.1. Type of Data
Primary data
The structured questionnaire is administered to a pre-selected group of people. A preliminary
survey was carried out to avoid mistakes. In addition, random interviews were conducted with
a group of people working in the banking sector to collect more accurate information.
Secondary Data
Theories and empirical evidence were gleaned from Researchers refer to books, academic
publications and journal articles.
3.6.2. Data Collection Method
Information was collected by sending google forms through WhatsApp, Facebook messenger.
In addition, user reviews on google were taken into consideration.
3.6.3. Data Collection and Analyse Tools
Data was collected using Google forms and analysed using SPSS.
19
3.7.
Sampling
3.7.1. Sampling Strategy

Purposive Sampling: A predetermined group of individuals who are experts in Big Data
Analytics and Business Intelligence was chosen to answer the structured questionnaire to
ensure that it gets relevant and informed responses for the same.

Pilot Testing: A small sample preliminary survey was conducted for testing the
effectiveness of the questionnaire, allowing for refinements and making sure everything is
clear before actual data collection.

Sampling Through Random Means: Random interviews were conducted among banking
professionals. These would represent a cross-section of experiences and opinions
concerning Big Data Analytics.

Combination Approach: Purposive sampling gave a target of knowledgeable response,
while the random sampling added broad perspective and helped to increase the validity and
depth of research findings.
3.7.2. Sample Size
A group of people over 25 years of age and adults were selected.

Structured Questionnaire: This structured questionnaire utilized a sample size of 150. This
value was deliberate for ensuring the results obtained from Big Data Analytics for Business
Intelligence are statistically significant and reliable.

Preliminary Survey: This preliminary survey had 20 respondents, which were used to test
and fine-tune the questionnaire so that when released to the general population, it shall be
clear and effective.

Random Interviews: In relation to this, 25 interviews were conducted among professionals
within the banking area. This number has still guaranteed a wide range of views while at
the same time going in-depth into practical aspects of Big Data Analytics.
20
3.8.
The selection of participants
A group of people over 25 years of age and adults were selected.

Questionnaire Development: The participants were experts in Big Data Analytics and
Business Intelligence, so that they could be relevant respondents.

Pre-Pilot Survey: A sample of 20 persons was considered for piloting the questionnaire,
which consisted of both experts and a few normal users with the purpose of checking the
flawlessness, ambiguity, and efficacy of the survey tool.

Random Interviews: Professionals from the banking sector were randomly chosen. To
ensure a wide range of views regarding the practical application of big data analytics.
P5 Communicate research outcomes in an appropriate manner for the intended audience
Big data analytics is a crucial part in running businеss intеlligеncе, whеrе hugе volumеs of
data arе utilizеd in еxtracting usеful insights to makе informеd dеcisions. Thеrеforе, for a
businеss profеssional looking to еxploit thе powеr associatеd with big data analytics, it will
bе vеry important to undеrstand thе rolе of data-drivеn dеcision-making within a compеtitivе
еnvironmеnt. A Gartnеr study еxplainеd, "By 2022, morе than 80% of еntеrprisе data will bе
storеd in data lеaks, up from 30% in 2019. " Thеrе is a growing importancе attributеd to data
lеaks as bеing at thе corе of thе infrastructurе of big data analytics.
Anothеr study by McKinsеy supports big data analytics as a way to dеlivеr high valuе in
businеss: "Companiеs that lеvеragе big data and analytics еffеctivеly arе 5% morе productivе
and 6% morе profitablе than thеir compеtitors. " This indееd paints tangiblе bеnеfits that will
bе rеalizеd by organizations applying advancеd analytics tеchniquеs.
A Forbеs Insight survеy goеs furthеr to еstablish that "74% of businеssеs bеliеvе that data
analytics hеlps thеm gеnеratе valuablе insights for dеcision-making. " This cеmеnts thе
accеptеd viеw that data analytics rеally doеs еnablе transformation in businеss opеrations.
This is why big data analytics as a procеss nееds no еxplanation for its intеgration into
businеss intеlligеncе procеssеs morе than еvеr if an organization has to gain a compеtitivе
еdgе, drivе innovation, and improvе opеrational еfficiеncy. Entеrprisеs can unlock nеw
avеnuеs of growth and succеss with a data-drivеn approach and advancеd analytics tools in
today's digitizеd, data-drivеn world.
21
RESEACH REPORT FORMAT
Section 1- Introduction
The impact of Big Data Analytics on business intelligence, with a view on practical
applications, gains, and challenges, is probed in this paper. In the present research, both
quantitative and qualitative methods will be combined to probe how Big Data Analytics could
make improvements in decision-making, operational efficiencies, and market insights across
different industries, more so in the banking sector. The primary data will be based on the
response from a structured questionnaire and random interviews. This gives an all-rounded
view of the current status and future possibilities of Big Data Analytics in BI.
Section 2- Literature Review
The literature review aims to explore the available literature about Big Data Analytics and its
application in enhancing Business Intelligence. It takes the reader through various concepts,
technological advancements, and approaches applied in the field. Previous studies express the
benefits of Big Data Analytics in raising the accuracy of decision-making and operational
efficiency, along with challenges associated with data quality, integration, and ethical
considerations. This review provides a foundation to understand current trends and gaps in
research that can guide the direction of this study.
Section 3- Methodology
It has followed a mixed-method approach with the quantitative data emanating from
structured questionnaires administered to 150 industry experts and qualitative insights that
are generated from 25 random interviews conducted amongst the banking professionals.
Sampling strategies include purposive selection for getting specific insights and random
sampling for a bigger view. Data collection has involved a preliminary survey for refining the
questionnaire, followed by the main survey and interviews. Statistical methods that will be
applied to the quantitative data analysis will be combined with the thematic analysis to be
conducted on the qualitative data in order that the research achieves a comprehensive
understanding of the impact of Big Data Analytics on BI.
Section 4- Analysis of Data and the Findings
The analysis of the data shows that Big Data Analytics has a very strong influence on Business
Intelligence, especially regarding decision accuracy, operational efficiency, and market
insights. Quantitative results reflect measurable advantages in cost reductions and competitive
advantages. Qualitative insights refer to concrete applications and common obstacles like data
quality and shortage of personnel skills. This means that even though Big Data Analytics does
bring considerable value, addressing integration complexities and ethical concerns is what
will lead to success in using it.
22
Section 5- Discussion
The discussion interprets the findings obtained in this research in the light of existing literature
and points out the ways Big Data Analytics changes Business Intelligence practices. It looks
into the practical implications of the results, which relate to improved decision making and
operational processes. The identified challenges are also discussed, with recommendations on
solving issues relating to data quality, integration, and ethical practices. This section places
in evidence the requirement of a strategic approach to leveraging Big Data Analytics in BI
and points out areas of future research.
4.1 SECTION 1 – INTRODUCTION
With the current digital transformation, a lot of platforms have experienced an explosion in
data generation, and Big Data has emerged to be very critical to organizations. Big Data refers
to huge, complex, and voluminous data beyond what traditional data-processing applications
can efficiently manage. Such data is derived from several sources, such as social media, web
queries, transaction data, and sensor data from the IoT. This may be structured or unstructured
data.
Big data analytics for business intelligence has been emerging as the most critical tool for an
organization that wants to get the most out of this sea of data. Equipped with sophisticated
algorithms, machine learning models, and advanced analytical tools, businesses can now
extract meaningful insights from these large data sets. These same factors are very important
in decision making, trend identification, outcome prediction, and carrying out any strategic
initiatives.
Big Data Analytics embedded within the systems of Business Intelligence gives organizations
a broader view of business operations, market conditions, and consumer behaviour. This gives
an all-rounded perspective on enhancing operational efficiency, optimizing marketing
strategies, and improving customer experiences. In addition to that, it identifies future risks and
opportunities that give a business a basis for proactive decision-making and innovation.
Though rich in various advantages of Business Intelligence, Big Data Analytics is not able to
combat some of the challenges. The challenges to this technology are related to data privacy,
security, and ethical considerations. Dealing with such volumes of data requires strong
frameworks for data governance, tight measures of cybersecurity to prevent leakage and
mismanagement of information, and due consideration of the ethical considerations regarding
data collection, storage, and usage in order to remain under the legal standards and be able to
keep pace with the growing public concerns and mistrust.
23
It is research directed at probing various applications, benefits, and limitations of Big Data
Analytics in relation to Business Intelligence. Such research will be revealing of organizations'
potential in the use of data-driven insight to gain competitive advantage, thus revealing the
transformative potential of Big Data in shifting business operations and strategies.
Purpose of research
This study aims at the following objectives: main objective of this research is to establish how
Big Data Analytics can improve BI in organizations. This paper’s objective is to explore the
ways Big Data Analytics can be effectively leveraged for business purposes to inform
implementation strategies for the industry.
Specifically, the research will:

Identify Key Applications: Discuss how Big Data Analytics is being applied in Business
Intelligence depending on the different business requirements like; Customer Analysis,
predictive analysis, and process optimization.

Evaluate Benefits: Examine the papers that discuss the aggregate advantages realized by
organizations as they adopt Big Data Analytics into their BI systems. This entails better
accuracy in decision making, better market knowledge, amongst others and increased
flexibility in operations.

Assess Challenges: Research on the real issues that managers face when adopting Big Data
Analytics for BI. These will include aspects like quality of the data, challenges of
integration and that the matters will require special skills and other implements.

Explore Case Studies: Also include typical case studies of various organizations that have
integrated Big Data Analytics for Business Intelligence. These case studies will look at
success stories, new ideas, and future possibilities based on their stories and experiences.

Recommend Strategies: Resolved recommendations and actionable plans should be offered
to companies interested in utilizing BD Analytics in BI. The recommendations will align
on the strategies of how to minimize the major challenges, how to secure data and privacy,
how to optimize the return on investment.

Address Ethical and Legal Considerations: Analyse the broader issues of Ethics and Law
when it comes to the use of Big Data Analytics in BI; important issues such as Data
Protections, Laws, and Ethical uses of Data.
24
Significance of the Research
The importance of this study on Big Data Analytics for Business Intelligence, BI is therefore
seen in its ability to shift the business dynamics. The study’s importance can be articulated
through
several
key
dimensions:

Enhanced Decision-Making: Thus, focusing on the concept of Big Data Analytics and its
usefulness in enhancing decision-making, this research demonstrates the potential of
businesses to make more sound, accurate, and timely decisions. Awareness of how data
analysis is to be applied assists organizations change from the use of gross best guess
estimations to the use of definitive statistical information.

Operational Efficiency: They help to reveal opportunities for the use of Big Data Analytics
in optimization, reduction of costs and increase in efficiency. The study enlightens the
actual practices of managing and improving the supply chain processes, as well as
increasing the efficiency of resources usage.

Competitive Advantage: In the current business environment, presumably, the capability
of exploiting information to create competitive edges is of paramount importance. Thus,
the described research proves the idea that Big Data Analytics can be used by businesses
for anticipating competitors, nurturing their knowledge about market trends and customers’
behaviour, as well as the potential opportunities on the market.

Risk Management: This paper focuses on the importance of Big Data Analytics in risk
management processes. Big data, therefore, allows organizations to identify trends or
patterns that may pose a threat, thus enabling the organization to prepare or prevent any
adverse effect from happening or if it happens, its impact is greatly reduced.

Innovation and Growth: In this context, it is necessary to explain how Big Data Analytics
can boost innovations and support organizational growth; the examples discussed in the
framework of the research reveal this cooperation. Information generated from data assists
a firm in designing new products, services or operations and business models, thus creating
new layers of income.
Research objectives
25
A research objective, also known as a goal or an objective, is a sentence or question that
summarizes the purpose of your study or test. In other words, it’s an idea you want to
understand deeper by performing research. Objectives should be the driving force behind
every task you assign and each question that you ask. (Anon., n.d.)
What is a Research Objective?
A research objective is defined as a clear and concise statement of the specific goals and aims
of a research study. It outlines what the researcher intends to accomplish and what they hope
to learn or discover through their research. Research objectives are crucial for guiding
the research process and ensuring that the study stays focused and on track.
Figure 2: Research objectives (Jain, 202 1
Figure 2: Research objectives (Jain, 2023)
Key characteristics of research objectives include:
Clarity: Research objectives should be clearly defined and easy to understand. One should
ensure there is no space for ambiguity or misinterpretation.
Specificity: Objectives should be specific and narrowly focused on the aspects of
the research topic that the study intends to investigate. They should answer the question of
“what” or “which” rather than “how” or “why.”
Measurability: Research objectives should be formulated in a way that allows for
measurement and evaluation. This means that there should be a way to determine whether the
objectives have been achieved or not.
Relevance: Objectives should be relevant to the research topic and align with the overall
research question or hypothesis. They should address important aspects of the subject matter.
Realistic: Objectives should be attainable within the constraints of the study, including time,
resources, and feasibility.
Time-bound: Research objectives may have associated timelines or deadlines to indicate
when the research aims should be accomplished.
26
Research objectives help researchers stay focused on the purpose of their study and guide the
development of research methods, data collection, and analysis. They also serve as a basis for
evaluating the success of the research once it’s completed. In the context of a research project,
research objectives typically follow the formulation of a research question or hypothesis and
serve as a roadmap for conducting the study. (Jain, 2023)
Types of Research Objectives
Figure 3: Types of Research Objectives (Jain, 2023)
Research objectives can be categorized into different types based on their focus and purpose
within a research study. Here are some common types of research objectives:
1. Descriptive Objectives
These objectives aim to provide a detailed and accurate description of a phenomenon, event,
or subject. They focus on answering questions about what, who, where, and when.
Example: “To delineate the demographic attributes of the study’s participants.”
2. Exploratory Objectives
Exploratory objectives are used when researchers seek to gain a better understanding of a
topic, especially when there is limited existing knowledge. They often involve preliminary
investigations.
Example: “To investigate the possible determinants impacting consumer inclinations towards
sustainable products.”
3. Explanatory Objectives
27
Explanatory objectives are designed to identify the relationships between variables and
explain the causes or reasons behind certain phenomena.
Example: “To examine the causal relationship between smoking habits and the development
of lung cancer.”
4. Comparative Objectives
These objectives involve comparing two or more variables, groups, or situations to identify
similarities, differences, patterns, or trends.
Example: “To compare the performance of two different marketing strategies in terms of their
impact on sales.”
5. Predictive Objectives
Predictive objectives aim to forecast or predict future outcomes or trends based on existing
data or patterns.
Example: “To forecast customer attrition rates within an online subscription service by
utilizing historical usage patterns and satisfaction data.”
6. Normative Objectives
Normative objectives involve establishing standards, guidelines, or recommendations for a
specific area of study.
Example: “To develop industry-specific ethical guidelines for the responsible use of artificial
intelligence.”
7. Qualitative Objectives
Qualitative objectives are used in qualitative research to explore and understand experiences,
perceptions, and behaviors in-depth.
Example: “To reveal the latent motivations and emotions of participants within a qualitative
interview investigation.”
8. Quantitative Objectives
Quantitative objectives involve the collection and analysis of numerical data to measure and
quantify specific phenomena.
Example: “To ascertain the relationship between income levels and the availability of
educational resources among a selected group of households.”
9. Longitudinal Objectives
Longitudinal objectives involve studying the same subjects or entities over an extended period
to track changes or developments.
Example: “To assess the cognitive development of children from kindergarten through high
school graduation.”
10. Cross-Sectional Objectives
Cross-sectional objectives involve the study of a sample at a single point in time to gather
data about a population’s characteristics or attitudes.
Example: “To assess the present employment situation and job satisfaction levels among
healthcare sector employees.” (Jain, 2023)
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 It is, therefore, the aim of the present research to review the sources and application of Big
Data Analytics in the field of Business Intelligence and determine the changes the are in
the process of bringing to organisational improvement of decision-making and efficiency,
and gain in competitive advantage. The purpose of this research is to determine the major
applications of BDA for BI; assess the quantifiable advantages of its application while
categorizing the potential difficulties that may occur when applying the BDA for BI.
Further, the research aims at identifying the best practices through literature review and
disseminating real-world examples while providing strategic plan, and this study will
consider the ethical and legal issues of the practice. Thus, achieving these objectives, the
research will provide businesses with the necessary knowledge and tools in the field of Big
Data Analytics application for proper decision-making and further development.
Research Sub objectives
As may be clear from these examples, sub-objectives are the steps required to attain the
main objective. Also, they need to be concrete and specific, whereas the objective can be
somewhat broader. Finally, they should point out the paths that lead to the objective. (Anon.,
n.d.)
 The research sub-objectives are as follows: Understand the nature and type of applications
that have experienced a positive impact by big data analytics in BI Recognize the tangible
value or the ROI by measuring better decision-making and operational worth Analyse the
practical implementation issues belonging to various industries. Furthermore, there will be
a qualitative study of specific good and, at the same time, inspiring cases of VR
implementation, identification of real-life challenges of VR projects, and recommendations
on how to solve them and get the most out of VR investments, as well as crucial issues of
ethical and legal compliance in managing VR data. Consequently, these sub-objectives will
seek to present a holistic view of big data analytics in improving BI plus facilitating the
achievement of strategic development goals.
Research questions
1. What is a Big Data Analytical solution, and how it helps Business Intelligence?
2. BI and Big Data Analytics have emerged to become main components within most
organizations' information systems nowadays. However, what has the organization
concretely benefited from the use of Big Data Analytics in its BI system?
3. The following is a question that enumerates common challenges organizations have in the
implementation of Big Data Analytics for Business Intelligence.
29
4. Precisely, which of the organizations has succeeded in implementing big data analytics for
business intelligence?
5. What are the moral and legal demands that need to be met with regard to the application of
Big Data Analytics for Business Intelligence?
Hypothesis
What is a Research Hypothesis?
Research hypothesis is a statement that introduces a research question and proposes an
expected result. It is an integral part of the scientific method that forms the basis of scientific
experiments. Therefore, you need to be careful and thorough when building your research
hypothesis. (Anon., 2024)
Importance of Hypothesis in Research
Hypotheses are really important in research. They help design studies, allow for practical
testing, and add to our scientific knowledge. Their main role is to organize research projects,
making them purposeful, focused, and valuable to the scientific community. Let’s look at
some key reasons why they matter:

A research hypothesis helps test theories.
A hypothesis plays a pivotal role in the scientific method by providing a basis for testing
existing theories. For example, a hypothesis might test the predictive power of a psychological
theory on human behavior.

It serves as a great platform for investigation activities.
It serves as a launching pad for investigation activities, which offers researchers a clear
starting point. A research hypothesis can explore the relationship between exercise and stress
reduction.

Hypothesis guides the research work or study.
A well-formulated hypothesis guides the entire research process. It ensures that the study
remains focused and purposeful. For instance, a hypothesis about the impact of social media
on interpersonal relationships provides clear guidance for a study.

Hypothesis sometimes suggests theories.
In some cases, a hypothesis can suggest new theories or modifications to existing ones. For
example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration
of current medical theories.

It helps in knowing the data needs.
A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the
necessary information—a hypothesis guiding the collection of demographic data to analyze
the influence of age on a particular phenomenon.
30

The hypothesis explains social phenomena.
Hypotheses are instrumental in explaining complex social phenomena. For instance, a
hypothesis might explore the relationship between economic factors and crime rates in a given
community.

Hypothesis provides a relationship between phenomena for empirical Testing.
Hypotheses establish clear relationships between phenomena, paving the way for empirical
testing. An example could be a hypothesis exploring the correlation between sleep patterns
and academic performance.

It helps in knowing the most suitable analysis technique.
A hypothesis guides researchers in selecting the most appropriate analysis techniques for their
data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead
to the choice of statistical analyses best suited for educational research.
Types of Research Hypotheses
The research hypothesis comes in various types, each serving a specific purpose in guiding the
scientific investigation. Knowing the differences will make it easier for you to create your own
hypothesis. Here’s an overview of the common types:
1. Null Hypothesis
The null hypothesis states that there is no connection between two considered variables or that
two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking
sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable,
verifiable, and can be rejected.
For example, if you’re studying the relationship between Project A and Project B, assuming
both projects are of equal standard is your null hypothesis. It needs to be specific for your study.
2. Alternative Hypothesis
The alternative hypothesis is basically another option to the null hypothesis. It involves looking
for a significant change or alternative that could lead you to reject the null hypothesis. It’s a
different idea compared to the null hypothesis.
When you create a null hypothesis, you’re making an educated guess about whether something
is true or if there’s a connection between that thing and another variable. If the null view
suggests something is correct, the alternative hypothesis says it’s incorrect.
For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative
hypothesis would be “I’m not going to get $1000 or be richer.”
3. Directional Hypothesis
31
The directional hypothesis predicts the direction of the relationship between independent and
dependent variables. They specify whether the effect will be positive or negative.
If you increase your study hours, you will experience a positive association with your exam
scores. This hypothesis suggests that as you increase the independent variable (study hours),
there will also be an increase in the dependent variable (exam scores).
4. Non-directional Hypothesis
The non-directional hypothesis predicts the existence of a relationship between variables but
does not specify the direction of the effect. It suggests that there will be a significant difference
or relationship, but it does not predict the nature of that difference.
For example, you will find no notable difference in test scores between students who receive
the educational intervention and those who do not. However, once you compare the test scores
of the two groups, you will notice an important difference.
5. Simple Hypothesis
A simple hypothesis predicts a relationship between one dependent variable and one
independent variable without specifying the nature of that relationship. It’s simple and usually
used when we don’t know much about how the two things are connected.
For example, if you adopt effective study habits, you will achieve higher exam scores than
those with poor study habits.
6. Complex Hypothesis
A complex hypothesis is an idea that specifies a relationship between multiple independent and
dependent variables. It is a more detailed idea than a simple hypothesis.
While a simple view suggests a straightforward cause-and-effect relationship between two
things, a complex hypothesis involves many factors and how they’re connected to each other.
For example, when you increase your study time, you tend to achieve higher exam scores. The
connection between your study time and exam performance is affected by various factors,
including the quality of your sleep, your motivation levels, and the effectiveness of your study
techniques.
If you sleep well, stay highly motivated, and use effective study strategies, you may observe a
more robust positive correlation between the time you spend studying and your exam scores,
unlike those who may lack these factors.
7. Associative Hypothesis
An associative hypothesis proposes a connection between two things without saying that one
causes the other. Basically, it suggests that when one thing changes, the other changes too, but
it doesn’t claim that one thing is causing the change in the other.
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For example, you will likely notice higher exam scores when you increase your study time.
You can recognize an association between your study time and exam scores in this scenario.
Your hypothesis acknowledges a relationship between the two variables your study time and
exam scores without asserting that increased study time directly causes higher exam scores.
You need to consider that other factors, like motivation or learning style, could affect the
observed association.
8. Causal Hypothesis
A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests
that changes in one variable directly cause changes in another variable.
For example, when you increase your study time, you experience higher exam scores. This
hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you
spend studying, the higher your exam scores. It assumes that changes in your study time
directly influence changes in your exam performance.
9. Empirical Hypothesis
An empirical hypothesis is a statement based on things we can see and measure. It comes from
direct observation or experiments and can be tested with real-world evidence. If an experiment
proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement
more reliable than a wild guess.
For example, if you increase the dosage of a certain medication, you might observe a quicker
recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are
given varying dosages of the medication, and you measure and compare their recovery times.
This allows you to directly see the effects of different dosages on how fast patients recover.
This way, you can create a research hypothesis: “Increasing the dosage of a certain medication
will lead to a faster recovery time for patients.”
10. Statistical Hypothesis
A statistical hypothesis is a statement or assumption about a population parameter that is the
subject of an investigation. It serves as the basis for statistical analysis and testing. It is often
tested using statistical methods to draw inferences about the larger population.
In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor
of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.
For example, let’s say you’re testing a new medicine. Your hypothesis could be that the
medicine doesn’t really help patients get better. So, you collect data and use statistics to see if
your guess is right or if the medicine actually makes a difference.
If the data strongly shows that the medicine does help, you say your guess was wrong, and the
medicine does make a difference. But if the proof isn’t strong enough, you can stick with your
original guess because you didn’t get enough evidence to change your mind. (Anon., n.d.)
33
 This research Hypothesis
H1
Decision Making: Big Data Analytics enhances the accuracy and timeliness of
decision-making in any organization.
H2
Operational Efficiency: Integrating Big Data Analytics will increase
operational efficiency while reducing costs.
H3
Competitive Advantage: The proper functioning of Big Data Analytics offers
a competitive advantage to the business by deeply understanding the market
trends and behaviour of customers more closely.
H4
Implementation Challenges: Organizations face considerable challenges. Data
quality and integration issues in implementing Big Data Analytics for Business
Intelligence.
LITERATURE REVIEW
5.1 Literature Review
Big data refers to the huge volumes of structured and unstructured data that businesses
generate each day. This data may originate in the form of website activity, social media
34
interactions, customer transactions, and a lot more. It’s important to understand that the sheer
volume of this data is something that the average person will likely not be able to fathom.
However, with the right big data tools and components of business intelligence in place,
businesses can find the proverbial needle in a haystack to unearth incredibly valuable insights.
(Wood, 2023)
When it comes to business intelligence, data is the lifeblood of the operation.
Businesses must have access to accurate and timely data in order to make sound decisions.
Data analytics can help to improve BI functions by providing insights that would otherwise
be unavailable.
For example, data analytics can be used to identify patterns and trends in customer behavior.
This information can then be used to make changes to the way that BI processes are carried
out, such as by changing the manner in which data is presented or by adding new features to
BI tools. (Anon., n.d.)
 There are many articles and publications on Big Data Analytics for Business Intelligence,
and literature reviews show it has had a remarkable impact on organizational strategies
within modernity. Various studies suggest that Big Data Analytics is thoroughly used to
increase business intelligence to make better decisions, operate more effectively, and hold
a competitive position within organizations. Big Data Analytics has been applied in aspects
such as customer segmentation, predictive modelling, and real-time analytics in
organizations, enabling an organization to understand the behaviours of consumers towards
its product and also to achieve effective operation of its works. Research on this topic shows
that if Big Data Analytics is integrated correctly, it is likely to bring in the following key
benefits: more accurate decisions, as well as reduced costs and increased efficiency. At the
same time, the literature reflects major challenges related to data quality, integration
problems, and requirements of specialized skills. Thirdly, real case examples and studies
bring insight into best practices and successful implementations. The debate, therefore,
emphasizes more on ethical and legal issues so that it puts a premium on data privacy,
security, and regulation compliance. Broadly speaking, the literature underlines both the
potential of Big Data Analytics for transformation and the difficulties involved in its
application within Business Intelligence.
Business Intelligence Technologies Provide Big Data to Help Progressive Organizations
Companies must take the necessary steps of preparation before investing in business
intelligence technology applications and tools. The combination of human and artificial
intelligence is bound to create powerful results, with data being at the core of everything you
do. Successfully deploying asynchronous messaging tools in your contact center requires a
carefully crafted strategy and deep expertise in customer experience, agent needs, and
technology. At [24]7.ai, our experts know how to help you make the most of the tools you
have, prepare your operations team for a successful roll out, and make life easier for your
customers. Additionally, [24]7 Journey Analytics is a journey discovery tool for simply
exploring omnichannel customer journeys. It uses advanced path analytics for insights that
improve the customer experience (CX) and optimize service operations in three steps.


Understand how customers interact across touchpoints.
Build an understanding of what’s working and what’s not.
35

Identify the root cause of issues. (Anon., n.d.)
Big Data Analytics and BI
With the tremendous growth in business technology and advances in cloud technology and
mobile applications, various business terms such as business intelligence, and associated
concepts, such as big data, data analytics, data mining, etc., are creating a buzz.

Business Intelligence (BI) is a set of technology-driven processes and technologies that
convert raw data into useful information to drive profitable business actions.

Big data involves storing, processing, and visualizing a combination of structured, semistructured, and unstructured data collected by companies to extract meaningful information
and insights.

Big data analytics makes use of various advanced analytic techniques, such as predictive
models, statistical algorithms, etc., to analyze and process large and diverse datasets from
different sources and sizes.

The main goal of Big Data analytics and Business Intelligence is to summarize the data
results so that businesses can uncover real insights and trends, thereby helping them make
informed decisions.
Big Data Applications in Business
Some of the most common big data applications in business include:




Improving customer service: Organizations can use big data analytics to better understand
customer needs and preferences. This can help them improve customer service and provide
a more personalized experience.
Enhancing product development: Big data can be used to help businesses improve product
development processes. By analyzing data from customer interactions and feedback,
businesses can identify trends and understand what products and features customers want.
Enhancing marketing efforts: Big data can help businesses improve their marketing efforts
by providing insights into customer behavior. By analyzing data on customer interactions
and interests, businesses can develop targeted marketing campaigns that are more likely to
be successful.
Improving operations: Big data analytics can be used to improve business operations by
identifying inefficiencies and waste. By understanding how customers interact with the
organization’s products and services, businesses can make changes that improve efficiency
and reduce costs.
Why are Big Data Analytics & Business Intelligence Important?
36
Companies can use extensive data analytics systems and software to make data-driven
decisions to improve business-related outcomes, operational efficiency, revenue generation
opportunities, and get an advantage over the competitors. Similarly, with Business
Intelligence process automation tools and techniques, companies can easily translate the
collected data into valuable insights about their business processes and strategies, helping
them make strategic business decisions that will enhance productivity and revenue
generation.
Without Big Data Analytics and Business Intelligence Tools, companies will not take
advantage of data-driven decision-making. They will have to rely on accumulated knowledge,
intuition, and gut feelings to analyze the data. These methods might lead to potential errors
and incorrect interpretation of data.
Business Intelligence and Big data Analytics: The Business Intelligence Transformation
Big data analytics helps companies collect, process, clean, and analyze large datasets so that
they can uncover trends, patterns, and correlations from a large pool of raw data. This helps
the companies make data-informed decisions, thereby promoting business growth.
Business Intelligence helps companies and businesses gather the necessary data, analyze it
and determine which actions need to be taken to reach their goals. This process also helps
them get answers to their queries and track their performance against these goals.
Business intelligence includes data analytics and business analytics, which help users
conclude from data analysis. The data scientists use the data, along with advanced statistics
and predictive analytics, to uncover patterns and predict future patterns. Business intelligence
then uses these models and algorithms to break down the results into actionable language,
thereby helping companies make the right business-related decisions that are based on the
collected data. (Anon., n.d.)
5.2 Enhancing Business Intelligence with Big Data Analytics: Effectiveness of Risk
Identification and Mitigation, and Implementation Challenges
Effectiveness of Risk Identification and Mitigation
The research finds out that Big Data Analytics greatly improves the risk identification and
mitigation efficiency within Business Intelligence. With the analysis of vast reams of data from
several sources in near real-time, it is easier to accurately and quickly identify potential risk
factors. Predictive analytics and machine learning algorithms help detect a pattern or anomaly
37
that might indicate the emergence of a risk. This proactive approach will help businesses design
mitigation strategies in advance, thereby decreasing the likelihood of an unwanted outcome. In
this way, incorporating Big Data Analytics into the BI framework can improve foreshadowing
and mitigation capabilities against risk events, hence making operations more resilient and
adaptive.
Implementation Challenges
Despite these considerable benefits, there exist quite a good number of challenges to the
implementation of Big Data Analytics in Business Intelligence. Among these, the quality of
data is the biggest challenge since wrong or incomplete data may lead to wrong insights and
decisions. On the integration side, many different sources and systems need to be integrated;
this will require advanced solutions for data management and interoperability. There is also the
requirement of competent personnel with the required competencies and skill sets to deal with
advanced analytics tools and techniques. Such ethical and legal issues as data privacy and
compliance need to be attended to with proper care in order to forestall any misuse and ensure
strict adherence to regulations. Addressing these challenges is very key to attaining full
potential from Big Data Analytics in enhancing BI.
Challenges in implementing big data analytics for business intelligence
While the benefits of leveraging big data analytics for business intelligence are immense,
organizations face challenges in effective implementation.
1. Data privacy and security concerns
With increasing data collection and analysis, organisations must address data security and
privacy concerns.
Protecting customer information and ensuring compliance with data protection regulations is
crucial to maintaining trust and mitigating risks.
2. The issue of data quality and accuracy
Large datasets often contain noise, errors, and inconsistencies, which can affect the quality
and accuracy of the insights derived from them.
Ensuring data integrity and implementing data governance practices are vital to overcoming
this challenge and ensuring reliable results.
3. Overcoming the skills gap in big data analytics
The field of big data analytics for business intelligence requires specialised skills and
expertise.
Organisations face the challenge of finding and retaining professionals with the necessary
technical skills, such as data engineering, data science, and ML, to extract insights from big
data effectively. (Anon., 2024)
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5.3 Decision-Making Efficiency
Enhanced Decision-Making
Big Data Analytics improves the various decision-making processes of organizations by
providing data-driven insights and action. Second, enabling intelligence into organizations,
advanced analytics tools will aid businesses in analysing large structured and unstructured
volumes of data to uncover patterns and trends that the business can use in informing strategic
decisions. Predictive analytics helps in the forecast of future scenarios, hence more proactive
and informed choices. Embed real-time data analytics into decision-making frameworks in
order to respond in time to market changes and new opportunities. This will help ensure better
operational efficiency and a competitive edge.
Cultural and Technical Barriers
Though full of benefits, the implementation of Big Data Analytics can turn out to be
troublesome due to cultural and technical hindrances. On the cultural side, human resistance to
change and less than a robust data-driven mindset are the key factors that affect the ease with
which employees quickly adapt to using analytics tools. Added to this is misgivings about data
privacy and ethical use, which may further engender a lack of acceptance and trust in analytics
efforts. Technical challenges pertain to the integration of different sources of data, betterment
of data quality, and complexity of advanced analytics systems. A shortage of trained people
who have the necessary skills in various techniques and tools of data analytics can result in an
impediment to exploit Big Data Analytics effectively. Overcoming these cultural and technical
barriers is sine qua non for exploiting Big Data to bring about a change in decision-making.
5.4 Organizational Risk Posture
Improvement in Risk Posture.
Big Data Analytics enhances an organization's risk posture by providing cutting-edge tools and
methodologies in the process of identifying, assessing, and mitigating risks. Deep analysis of
large datasets from multiple sources helps identify a pattern or anomaly that might be indicative
of a future risk factor. Predictive analytics models allow organizations to forecast future risks
and, therefore, prepare proactive strategies on how to handle these risks. A data-driven
approach therefore paves the way for a more accurate risk assessment together with timely
interventions and continuous monitoring to make sure that the set-up is robust and resilient.
Barriers to Improvement
39
Despite the potential, various barriers exist to the implementation of Big Data Analytics in
improving risk posture. On one hand, data quality and integration problems may undermine the
accuracy and reliability of risk assessments. From a technical point of view, complexities in
the analytics tools and advanced infrastructure requirements may forestall effective
implementation. Further, skilled data professionals to exploit these tools are a missing
ingredient. Intrinsic cultural resistance to organizations in the form of reluctance to new
technologies and practices is also a handicap. Only an inclusive approach that invests in
technology, training, and a culture steeped in data can overcome these barriers.
5.5 Technological, Organizational, and Cultural Barriers, Minimising Barriers
Technological Barriers
Big Data Analytics embodies business intelligence with various technological issues. First,
there could be an integration problem regarding data. Most organizations combine various
sources and formats of data. Organizing data to ensure quality and consistency may be complex
and time-consuming to run across large data sets. Third, advanced analytic tools demand robust
IT infrastructure and huge computational power; these come at a cost. Analytics technologies
are also fast-changing and require constant updating and maintenance, adding to the
complexity.
Organizational Barriers
These tend to arise from structural and procedural problems within the company. For instance,
lack of strategic alignment: Big Data activities are not integrated into the corporation's general
business strategy. Moreover, inadequate budgeting of analytic projects and a general reluctance
to change by management can slow down progress. Again, there is likely to be a limited supply
of people with the necessary skills to exploit Big Data tools and techniques for their work that
will add to the problems of implementation.
Cultural Barriers
Of all the above, cultural barriers are the most challenging to overcome. Very commonly, there
is resistance to change, and this affects the will of employees to accept new technologies and
methodologies. It might further be found that among the staff, data literacy is lacking, leading
to misinterpretation or mistrust of the results from analytics. This can be evidenced further in
resistance resulting from data privacy and ethical use concerns. A culture of decision-making
data-driven and embracing of technology would have to be cultivated to overcome these
barriers.
Minimising Barriers
Several strategies can be considered for minimizing these barriers, including:
40

Investment in Technology: The first and foremost is to invest adequately in building an
able IT infrastructure and spend sufficiently on advanced analytics tools. Keeping it
updated from time to time will also be of much essence to cope with technological changes.

Skilled Workforce: Spend money on programs for improvement of data literacy and
enhancement of technical skills of employees. Hiring staff or up-skilling them in Big Data
Analytics will help bridge the gap in skills.

Alignment with Business Strategy: Big Data initiatives should be aligned to the overall
Business Strategy. Top-down messaging related to the need for data-driven decisionmaking and incorporating analytics into strategic planning are some of the tasks to be
implemented.

Change Management: Adopt efficient change management practices to overcome such
resistance. This may involve staff in the development and implementation of analytics
projects, or provide incentives for adoption and illustration of Big Data value by pilots and
success stories.

Data Governance: Establish strong structures in data governance to remain responsive to
data quality, integration, and privacy issues. Well-articulated policies and procedures for
managing data will give the integrity and reliability expected of results from analytics.

Cultural Shift: A data-driven culture needs to be cultivated by raising awareness about Big
Data Analytics and encouraging an attitude of curiosity and innovativeness. The leadership
should exhibit data-driven decision-making while opening up platforms for employees to
discuss insights and best practices.
5.6 Cost-Effectiveness and Cost Implications
Cost-Effectiveness.
Big Data Analytics helps a company on two major fronts: operational efficiency and strategic
decision-making. These huge amounts of data analysis would identify the inefficiencies in
the operations so that the resources could be correctly allocated to the much-needed activities;
this would eventually help save tremendous costs incurred by the companies. Predictive
analytics facilitates trend predictions, thereby avoiding potential problems by taking remedial
action ahead of schedule. Automation of data processing and reporting tasks reduces manual
effort and associated labour costs. With Big Data Analytics, the organization can attain the
41
best possible outcome and drive its ROI to the highest level of investment by making very
informed decisions to drive efficiency and cost-savings improvement.
Cost Implications
Big Data Analytics comes with several cost implications. Up-front investments are needed in
advanced analytics tools, an upgraded IT infrastructure, and some data storage solutions. The
ongoing costs include the maintenance and scaling of the technology, software updates, and
actually processing the vast amounts of data produced. Another cost involves hiring the right
people or training existing employees who can really know how to use the analytics tools.
There could also be a significant cost and complexity involved in integrating this with existing
systems. While one-off and on-going costs are very significant, in many cases they are more
than compensated for by the long-term gain in better decision-making, operational efficiency,
and competitive edge. Careful cost management and strategic investment are therefore very
important to ensure a proper cost-benefit balance.
5.7 Research GAP
 Such gaps in Big Data Analytics for BI research are related to practical implementation
challenges that have not yet been well explored and situate the integration of a number of
emerging technologies. Although there is huge research concerning the theoretical
advantages and general applications of Big Data Analytics, studies that in depth address
specific barriers that organizations face in implementation—including technical integration
issues, organizational cultural resistance, and data quality management—are few. In
addition, how business intelligence can become more powerful through emerging
technologies such as artificial intelligence and machine learning is not expounded. This gap
underlines the need for empirical research that focuses on real case studies to provide
actionable insight into the surmounting of implementation challenges and harnessing new
technologies to enable improved BI outcomes.
5.8 Conceptual framework
Definition of a conceptual framework
True research begins with setting empirical goals. Goals aid in presenting successful answers
to the research questions at hand. It delineates a process wherein different aspects of the
research are reflected upon, and coherence is established among them.
A conceptual framework is an underrated methodological approach that should be paid
attention to before embarking on a research journey in any field, be it science, finance, history,
psychology, etc.
A conceptual framework sets forth the standards to define a research question and find
appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs,
and concepts behind your research and presents them in a pictorial, graphical, or narrative
42
format. Your conceptual framework establishes a link between the dependent and independent
variables, factors, and other ideologies affecting the structure of your research.
A critical facet a conceptual framework unveils is the relationship the researchers have with
their research. It closely highlights the factors that play an instrumental role in decisionmaking, variable selection, data collection, assessment of results, and formulation of new
theories.
Consequently, if you, the researcher, are at the forefront of your research battlefield, your
conceptual framework is the most powerful arsenal in your pocket. (Anon., 2023)
Purpose and Importance of a Conceptual Framework in Research
The importance of a conceptual framework in research cannot be understated, irrespective of
the field of study. It is important for the following reasons:





It clarifies the context of the study.
It justifies the study to the reader.
It helps you check your own understanding of the problem and the need for the study.
It illustrates the expected relationship between the variables and defines the objectives for
the research.
It helps further refine the study objectives and choose the methods appropriate to meet
them.
What to Include in a Conceptual Framework
Essential elements that a conceptual framework should include are as follows:




Overarching research question(s)
Study parameters
Study variables
Potential relationships between those variables.
The sources for these elements of a conceptual framework are literature, theory, and
experience or prior knowledge.
How to Make a Conceptual Framework
Now that you know the essential elements, your next question will be how to make a
conceptual framework.
For this, start by identifying the most suitable set of questions that your research aims to
answer. Next, categorize the various variables. Finally, perform a rigorous analysis of the
collected data and compile the final results to establish connections between the variables.
In short, the steps are as follows:


Choose appropriate research questions.
Define the different types of variables involved.
43

Determine the cause-and-effect relationships.
Be sure to make use of arrows and lines to depict the presence or absence of correlational
linkages among the variables.
Developing a Conceptual Framework
Researchers should be adept at developing a conceptual framework. Here are the steps for
developing a conceptual framework:
1. Identify a research question
Your research question guides your entire study, making it imperative to invest time and effort
in formulating a question that aligns with your research goals and contributes to the existing
body of knowledge. This step involves the following:







Choose a broad topic of interest
Conduct background research
Narrow down the focus
Define your goals
Make it specific and answerable
Consider significance and novelty
Seek feedback.
2. Choose independent and dependent variables
The dependent variable is the main outcome you want to measure, explain, or predict in your
study. It should be a variable that can be observed, measured, or assessed quantitatively or
qualitatively. Independent variables are the factors or variables that may influence, explain,
or predict changes in the dependent variable.
Choose independent and dependent variables for your study according to the research
objectives, the nature of the phenomenon being studied, and the specific research design. The
identification of variables is rooted in existing literature, theories, or your own observations.
3. Consider cause-and-effect relationships
To better understand and communicate the relationships between variables in your study,
cause-and-effect relationships need to be visualized. This can be done by using path diagrams,
cause-and-effect matrices, time series plots, scatter plots, bar charts, or heatmaps.
4. Identify other influencing variables
Besides the independent and dependent variables, researchers must understand and consider
the following types of variables:


Moderating variable: A variable that influences the strength or direction of the relationship
between an independent variable and a dependent variable.
Mediating variable: A variable that explains the relationship between an independent
variable and a dependent variable and clarifies how the independent variable affects the
dependent variable.
44


Control variable: A variable that is kept constant or controlled to avoid the influence of
other factors that may affect the relationship between the independent and dependent
variables.
Confounding variable: A type of unmeasured variable that is related to both the
independent and dependent variables. (Singh, 2023)
 The conceptual framework of Big Data Analytics in Business Intelligence captures the flow
from generation of data to actionable insights. It all begins with these various Big Data
sources, like social media and transaction records; it proceeds to store and manage them
with the help of data lakes and cloud solutions. Extraction of this data from these sources
is worked on by analytical tools and techniques such as machine learning and data mining
to come up with insights. These insights are fed into BI systems for visualization and
reporting that support informed decisions. Amongst others, it also considers data quality
and ethical considerations that need to be addressed if Big Data is to serve well the
intentions of using it effectively to gain strategic business advantage.
45
Figure 4: Conceptual framework
METHODOLOGY
6.1 Research methodology
Research methodology is a way of explaining how a researcher intends to carry out their
research. It's a logical, systematic plan to resolve a research problem. A methodology details
a researcher's approach to the research to ensure reliable, valid results that address their aims
and objectives. (Anon., 2024)
Big data involves analysing vast data sets to uncover patterns, trends, and connections that
can inform decisions and strategies. This analysis focuses on data that is too complex or too
large for traditional data-processing software to handle efficiently. (Anon., n.d.)
What is research methodology?
A research methodology describes the techniques and procedures used to identify and analyze
information regarding a specific research topic. It is a process by which researchers design
their study so that they can achieve their objectives using the selected research instruments.
It includes all the important aspects of research, including research design, data collection
methods, data analysis methods, and the overall framework within which the research is
46
conducted. While these points can help you understand what is research methodology, you
also need to know why it is important to pick the right methodology. (Sreekumar, 2023)
Why is research methodology important?
Having a good research methodology in place has the following advantages:







Helps other researchers who may want to replicate your research; the explanations will be
of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly
define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and
data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and
reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by
ensuring optimum usage of their time and resources. (Sreekumar, 2023)
Types of research methodology
There are three types of research methodology based on the type of research and the data
required.



Quantitative research methodology focuses on measuring and testing numerical data. This
approach is good for reaching a large number of people in a short amount of time. This type
of research helps in testing the causal relationships between variables, making predictions,
and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of
people. It collects and analyzes words and textual data. This research methodology requires
fewer participants but is still more time consuming because the time spent per participant
is quite large. This method is used in exploratory research where the research problem being
investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and
qualitative research methodologies in the same study. This method allows researchers to
validate their findings, verify if the results observed using both methods are
complementary, and explain any unexpected results obtained from one method by using
the other method. (Sreekumar, 2023)
47
Quantitative Method
The quantitative approach involves the collection of numerical data to define trends,
correlations, statistical relationships, etc. This technique in Big Data Analytics for Business
Intelligence tends to include:



Structured Questionnaires: Surveys of defined questions with choices in response to gather
measurable data from a large number of respondents;
Statistical Analysis: The methods used, involving analysis of data to determine trends or
relationships, include regression analysis, correlation analysis, and descriptive statistics.
Data Modelling: It uses mathematical models to predict outcomes to determine how
different factors impact business performance.
Quantitative methods provide objective and quantifiable insights into how Big Data Analytics
influences decision-making and operational efficiency.
Qualitative Method
The qualitative method works with understandings of reasons, motivations, and experiences
of individuals by non-numerical data. This includes:



Interviews: Key stakeholders, such as data professionals and business leaders, were
interviewed in-depth and open-ended to garner their experiences and perceptions around
Big Data Analytics.
Focus Groups: To get a spectrum of perspectives and realize shared themes or issues on
using analytics in BI.
Case Studies: Looking into particular cases or organizational executions of Big Data
Analytics to make sense of practical implementation and challenges.
One may define research methodology as the procedures or techniques involved in the
identification, selection, processing, and analysis of information regarding a topic. Through the
methodology section, a research paper enables the reader to make a critical evaluation
regarding the overall validity and reliability of any study. Should you be designing a research
methodology for your dissertation, thesis, or whatever formal research effort, Saunders'
Research Onion displays the various choices one will have to make. You'll need to make a raft
of decisions as you move inwards from the outside of the onion, ranging from high-level and
philosophical to tactical and practical in nature. Furthermore, it follows basically the same
format as the methods Section does. Saunders' research onion is ordinal, but it's a useful tool
nevertheless for considering methodology holistically. It at least makes clear the choices you
have to make about your research design.
48
Research onion contain six layers;






Philosophy
Approach
Strategy
Choice
Time Horizon
Data collection tools
The Research Onion Model (Anon., n.d.)
6.2 Research philosophy
A research philosophy is a belief about the way in which data about a phenomenon should be
gathered, analysed and used. (Anon., n.d.)
 The guiding research philosophy of this research on big data analytics in Business
Intelligence is majorly Pragmatism. This method deals with real practical outcomes and
effective solutions, thereby combining both quantitative and qualitative methods to
understand how big data analytics enhances decision making and operational efficiency.
This will also make use of Positivism to provide empirical evidence and measurable
insights, while Interpretivism provides context and understanding of how organizations use
data. Finally, it is Critical Realism that informs the study in assessing the deep structures
and mechanisms that influence Big Data Analytics in application. This integration provides
a superlative view of Big Data in BI in practice and theory.
49
6.3 Research approach
 It was decided to use semi-structured interviews as the primary data collection method for
this study.
What is research approach?
It is the overall plan of action for conducting a research study, including determination of the
methods of collecting and analysing data and the way of interpreting results. A research
approach, thus, can be treated as a general plan formulated according to the research problem
and objectives in view of attaining research aims.
The research can be divided into three categories:



Deductive approach
Inductive approach
Abductive approach

Deductive approach
If you have formulated a set of hypotheses for your dissertation that need to be confirmed or
rejected during the research process you would be following a deductive approach. In deductive
approach, the effects of labour migration within the EU are assessed by developing hypotheses
that are tested during the research process.
Dissertations with deductive approach follow the following path: (Anon., n.d.)
Figure 5: Deductive process in research approach (Anon., n.d.)

Inductive Approach
50
Alternatively, inductive approach does not involve formulation of hypotheses. It starts with
research questions and aims and objectives that need to be achieved during the research
process.
Inductive studies follow the route below: (Anon., n.d.)
Figure 6: Inductive process in research approach (Anon., n.d.)

Abductive Approach
In abductive approach, the research process is devoted to explanation of ‘incomplete
observations’, ‘surprising facts’ or ‘puzzles’ specified at the beginning of the study. Referring
to the same research topic, you may observe that labour migration within the EU was actually
decreasing the extent of cross-cultural differences within teams in Dutch private sector
organizations. (Anon., n.d.)
6.4
Research strategy
 Quantitative survey
What is research strategy?
A research strategy is simply a predetermined plan that offers an explanation regarding how
the study is to be undertaken in order to answer the research questions or meet the objectives
set forth. It outlines how one is to collect, analyse, and interpret data. Strategies include
descriptive, to describe phenomena; exploratory, the purpose is to explore new areas;
explanatory, to explain relationships and causality; analytical, to analyse existing data; action,
to solve practical problems; and case studies, in order to deeply understand specific instances.
The choice of strategy depends on the goals of the research, the nature of the problem, and
the type of data required.
51
6.5
Research Choice
Quantitative research is considered the best option as compared to qualitative because it's more
scientific, objective, quicker, focused, and acceptable. The qualitative may be the best option
when one is not even sure what to expect, for instance, in cases where one does not know the
problem or how to fix it. It's a tool for figuring out what the problem is and how to solve it.
6.6
Time frame
A time frame refers to a specific period of time that is used as a reference point for indicating
when an action or event has occurred, is currently happening, or will happen in the future.
(Anon., n.d.)
 The time frame table related to this research is below.
Table 2: Time Frame
52
6.7
Data collection procedures
Data collection procedure in research refers to the techniques and tools used to collect data
systematically for analysis and interpretation. It involves the collection of field data to answer
research questions or test hypotheses. (Anon., n.d.)
Type of Data
Primary data
The structured questionnaire is administered to a pre-selected group of people. A preliminary
survey was carried out to avoid mistakes. In addition, random interviews were conducted with
a group of people working in the banking sector to collect more accurate information.
Primary data collection involves the collection of original data directly from the source or
through direct interaction with the respondents. This method allows researchers to obtain
firsthand information specifically tailored to their research objectives. There are various
techniques for primary data collection, including:
a) Surveys and Questionnaires: Researchers design structured questionnaires or surveys to
collect data from individuals or groups. These can be conducted through face-to-face
interviews, telephone calls, mail, or online platforms.
b) Interviews: Interviews involve direct interaction between the researcher and the respondent.
They can be conducted in person, over the phone, or through video conferencing.
Interviews can be structured (with predefined questions), semi-structured (allowing
flexibility), or unstructured (more conversational).
c) Observations: Researchers observe and record behaviours, actions, or events in their natural
setting. This method is useful for gathering data on human behaviour, interactions, or
phenomena without direct intervention.
d) Experiments: Experimental studies involve the manipulation of variables to observe their
impact on the outcome. Researchers control the conditions and collect data to draw
conclusions about cause-and-effect relationships.
e) Focus Groups: Focus groups bring together a small group of individuals who discuss
specific topics in a moderated setting. This method helps in understanding opinions,
perceptions, and experiences shared by the participants. (Anon., 2023)
Secondary Data
Theories and empirical evidence were gleaned from Researchers refer to books, academic
publications and journal articles.
53
Secondary data collection involves using existing data collected by someone else for a
purpose different from the original intent. Researchers analyse and interpret this data to
extract relevant information. Secondary data can be obtained from various sources, including:
a) Published Sources: Researchers refer to books, academic journals, magazines, newspapers,
government reports, and other published materials that contain relevant data.
b) Online Databases: Numerous online databases provide access to a wide range of secondary
data, such as research articles, statistical information, economic data, and social surveys.
c) Government and Institutional Records: Government agencies, research institutions, and
organizations often maintain databases or records that can be used for research purposes.
d) Publicly Available Data: Data shared by individuals, organizations, or communities on
public platforms, websites, or social media can be accessed and utilized for research.
e) Past Research Studies: Previous research studies and their findings can serve as valuable
secondary data sources. Researchers can review and analyse the data to gain insights or
build upon existing knowledge. (Anon., 2023)
6.8 Data Collection Method
 Information was collected by sending google forms through WhatsApp, Facebook
messenger. In addition, user reviews on google were taken into consideration.
What are Data Collection Methods?
Data collection methods are techniques and procedures for gathering information for research
purposes. They can range from simple self-reported surveys to more complex quantitative or
qualitative experiments.
Some common data collection methods include surveys, interviews, observations, focus
groups, experiments, and secondary data analysis. The data collected through these methods
can then be analyzed to support or refute research hypotheses and draw conclusions about the
study’s subject matter.
Understanding Data Collection Methods
54
Data collection methods encompass a variety of techniques and tools for gathering
quantitative and qualitative data. These methods are integral to the data collection and ensure
accurate and comprehensive data acquisition.
Quantitative data collection methods involve systematic approaches, such as




Numerical data,
Surveys, polls and
Statistical analysis
To quantify phenomena and trends.
Conversely, qualitative data collection methods focus on capturing non-numerical
information, such as interviews, focus groups, and observations, to delve deeper into
understanding attitudes, behaviors, and motivations.
Combining quantitative and qualitative data collection techniques can enrich organizations’
datasets and gain comprehensive insights into complex phenomena.
Effective utilization of accurate data collection tools and techniques enhances the accuracy
and reliability of collected data, facilitating informed decision-making and strategic planning.
Importance of Data Collection Methods
Data collection methods play a crucial role in the research process as they determine the
quality and accuracy of the data collected. Here are some major importance of data collection
methods.

Quality and Accuracy: The choice of data collection technique directly impacts the quality
and accuracy of the data obtained. Properly designed methods help ensure that the data
collected is error-free and relevant to the research questions.
 Relevance, Validity, and Reliability: Effective data collection methods help ensure that the
data collected is relevant to the research objectives, valid (measuring what it intends to
measure), and reliable (consistent and reproducible).
 Bias Reduction and Representativeness: Carefully chosen data collection methods can help
minimize biases inherent in the research process, such as sampling or response bias. They
also aid in achieving a representative sample, enhancing the findings’ generalizability.
 Informed Decision Making: Accurate and reliable data collected through appropriate
methods provide a solid foundation for making informed decisions based on research
findings. This is crucial for both academic research and practical applications in various
fields.
 Achievement of Research Objectives: Data collection methods should align with the
research objectives to ensure that the collected data effectively addresses the research
questions or hypotheses. Properly collected data facilitates the attainment of these
objectives.
 Support for Validity and Reliability: Validity and reliability are essential to research
validity. The choice of data collection methods can either enhance or detract from the
validity and reliability of research findings. Therefore, selecting appropriate methods is
critical for ensuring the credibility of the research.
The importance of data collection methods cannot be overstated, as they play a key role in the
research study’s overall success and internal validity.
55
Types of Data Collection Methods
Figure 7: Data collection (Anon., n.d.)
6.8.1 Data Collection and Analyse Tools
 Data was collected using Google forms and analysed using SPSS.
Recommended Data Collection Tools
Choosing the right data collection tools depends on your specific needs, such as the type of
data you’re collecting, the scale of your project, and your budget. Here are some widely used
tools across different categories:
1. Survey Tools
 QuestionPro: Offers advanced survey features and analytics.
 SurveyMonkey: User-friendly interface with customizable survey options.
 Google Forms: Free and easy to use, suitable for simple surveys.


2. Interview and Focus Group Tools
Zoom: Great for virtual interviews and focus group discussions.
Microsoft Teams: Offers features for collaboration and recording sessions.
3. Observation and Field Data Collection
56


Open Data Kit (ODK): This is for mobile data collection in field settings.
REDCap: A secure web application for building and managing online surveys.
4. Mobile Data Collection
 KoboToolbox: Designed for humanitarian work, useful for field data collection.
 SurveyCTO: Provides offline data collection capabilities for mobile devices.
5. Data Analysis Tools
 Tableau: Powerful data visualization tool to analyze survey results.
 SPSS: Widely used for statistical analysis in research.
6. Qualitative Data Analysis
 NVivo: For analyzing qualitative data like interviews or open-ended survey responses.
 Dedoose: Useful for mixed-methods research, combining qualitative and quantitative data.


7. General Data Collection and Management
Airtable: Combines spreadsheet and database functionalities for organizing data.
Microsoft Excel: A versatile tool for data entry, analysis, and visualization.
If you are interested in purchasing, we invite you to visit our article, where we dive deeper
and analyze the best data collection tools in the industry. (Anon., n.d.)
6.9
Sampling
6.9.1 Sampling Strategy

Purposive Sampling: A predetermined group of individuals who are experts in Big Data
Analytics and Business Intelligence was chosen to answer the structured questionnaire to
ensure that it gets relevant and informed responses for the same.

Pilot Testing: A small sample preliminary survey was conducted for testing the
effectiveness of the questionnaire, allowing for refinements and making sure everything is
clear before actual data collection.

Sampling Through Random Means: Random interviews were conducted among banking
professionals. These would represent a cross-section of experiences and opinions
concerning Big Data Analytics.
57

Combination Approach: Purposive sampling gave a target of knowledgeable response,
while the random sampling added broad perspective and helped to increase the validity and
depth of research findings.
Questionnaire structure
Variable
Indicators
Measurement
Mean
STD.
Deviation
Median
58
Demographics
1. What is your age
group?
Questioner
2. What is your
current job title?
3. How many years
of experience do
you have in the
field of Big Data
Analytics
or
Business
Intelligence?
4. In which industry
do you currently
work?
Psychological
well-being
1. How satisfied
are you with
your role in
using Big Data
Analytics for
Business
Intelligence?
2. How often do
you
feel
stressed due to
the demands of
working with
Big
Data
Analytics?
3. How confident
are you in your
ability
to
effectively use
Big
Data
Analytics tools
and techniques?
4. Do you feel you
receive
adequate
support
and
Questioner
59
resources from
your
organization to
perform your
job effectively
in the field of
BI?
1.
How
would you
Physical Wellrate
the
being
ergonomics and
comfort of your
work
environment
where
you
handle Big Data
Analytics tasks?
Questioner
2. How many hours
per week do you
typically spend
working on Big
Data Analytics
tasks?
3. Have
you
experienced any
physical health
issues related to
prolonged use of
computers
or
working
with
large data sets?
Social
being
4. How would you
describe
your
work-life balance
given your role in
Big
Data
Analytics?
Well- 1. How effective is
the collaboration
with your team
when working on
Big
Data
Analytics
projects?
Questioner
60
2. How often do
you engage with
other
professionals in
the field of Big
Data Analytics
for networking
and knowledge
sharing?
3. Do you feel that
your
contributions in
Big
Data
Analytics
are
adequately
recognized and
valued
within
your
organization?
4. How would you
rate
the
workplace
culture in terms
of support for
continuous
learning
and
development in
Big
Data
Analytics?
Table 3: Questionnaire structure
6.9.2
Sample Size
A group of people over 25 years of age and adults were selected.

Structured Questionnaire: This structured questionnaire utilized a sample size of 150. This
value was deliberate for ensuring the results obtained from Big Data Analytics for Business
Intelligence are statistically significant and reliable.
61

Preliminary Survey: This preliminary survey had 20 respondents, which were used to test
and fine-tune the questionnaire so that when released to the general population, it shall be
clear and effective.

Random Interviews: In relation to this, 25 interviews were conducted among professionals
within the banking area. This number has still guaranteed a wide range of views while at
the same time going in-depth into practical aspects of Big Data Analytics.
6.10
The selection of participants
A group of people over 25 years of age and adults were selected.

Questionnaire Development: The participants were experts in Big Data Analytics and
Business Intelligence, so that they could be relevant respondents.

Pre-Pilot Survey: A sample of 20 persons was considered for piloting the questionnaire,
which consisted of both experts and a few normal users with the purpose of checking the
flawlessness, ambiguity, and efficacy of the survey tool.

Random Interviews: Professionals from the banking sector were randomly chosen. To
ensure a wide range of views regarding the practical application of big data analytics
Figure 8: Gantt chart
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Task description
Duration (week)
Project Initiation
2
Literature Review
2
Research approach
1
Data Collection
3
Data Analysis
2
Conclusion and Recommendations
2
Final Review and Editing
2
Submission
2
Table 4: Gantt chart data table
PRESENTATION OF RESULTS
7.1 Demographic Analysis
What is your age group?
63
What is your current job title?
How many years of experience do you have in the field of Big Data Analytics or Business
Intelligence?
In which industry do you currently work?
64
7.2 Correlation Analysis
On this basis, Correlation Analysis is a statistical procedure applied in order to evaluate the
degree and direction of association between two or more variables. It is useful in establishing
if two variables are correlated, or if they are inversely related, so more understanding about
the variables is made. This is always expressed as a value between +1 and -1, +1 indicating a
‘perfect positive correlation,’ (-1) a ‘perfect negative correlation,’ and 0 ‘no correlation’.
Regression analysis plays an essential role in recognizing overall patterns and existing
dependencies and is commonly applied for decision-making and outcomes prediction
belonging to the different fields including financial and marketing, and healthcare.
8.1 Discussion
The discussion interprets the findings obtained in this research in the light of existing literature
and points out the ways Big Data Analytics changes Business Intelligence practices. It looks
into the practical implications of the results, which relate to improved decision-making and
operational processes. The identified challenges are also discussed, with recommendations on
solving issues relating to data quality, integration, and ethical practices. This section places
in evidence the requirement for a strategic approach to leveraging Big Data Analytics in BI
and points out areas of future research.
8.2 Limitations
Research limitations can also be considered as those elements that can affect the accuracy,
reliability, and applicability of the research outcome. Examples include data limitations,
whereby the information is incomplete or biased in a way, thus affecting the validity of the
findings. Other constraints pertain to the method, including the use of surveys, which may not
elicit deep insights, and case studies with poor generalizability. Such issues as small-sized and
nonrepresentative samples can seriously limit the scope and applicability of the conclusions.
Besides, time and resource constraints may set a limit on the depth of data collection and
analysis. Next, comes external validity issues if findings are not applicable elsewhere in other
settings or populations. Last but not least, the subjectivity/bias of researchers may influence
the results. All these limitations need to be taken into account when interpreting the
implications of this study's findings and reorienting further research.
65
9.1 Future Improvements
Big Data Analytics for Business Intelligence in the future can be bettered by improving data
quality through betterment in techniques of collection and validation. The adoption of advanced
methods of analysis, including Artificial Intelligence and Machine Learning, will give deeper
insights and more accurate predictions. Improvement in integration and interoperability with
the already deployed infrastructure will bring efficiency. Ethical and privacy concerns can be
addressed with the implementation of robust data governance to assure compliance and build
trust. While developing expertise through training and creating user-friendly tools is a sure way
to make the analytics more accessible, it will also be more effective. Other gains expected
include improved decision-making, a better view of the business, and operational excellence.
10.1 Personnel Reflection
Benefits for the researcher
These would also include valuable insights gained into the practical applicability of Big Data
Analytics in Business Intelligence and the challenges involved. Experience in developing
their skills in data analysis, problem-solving, and technology integration is built. Research
allows one to contribute to the field by creating new knowledge and possibly leading to
publications or presentations at conferences. The real-world case studies will therefore engage
them, and the opportunity to interact with industry professionals increases their professional
network. Besides, this could create opportunities for collaborations or career growth in the
future. This research experience enriches their skills and knowledge and makes them a more
competent and knowledgeable professional.
Benefits for the Industry/organization
These benefits will also affect the industry or the organization through better decision-making
from more accurate and actionable insights derived from Big Data Analytics. With enhanced
strategies that are more data-driven, operational efficiency can be improved, costs can be
saved, and resource usage can be optimized. Trends, patterns, and risks can be recognized
much earlier to help the organizations take proactive measures for resolution of issues and
capitalizing on new opportunities. Advanced analytics can foster innovation and hence
provide a competitive edge in the market. Additional integration of Big Data Analytics into
business can help enhance overall business performance, driving growth, and improving
customer satisfaction.
66
REFERENCES
Anon.,
Available
at:
[Accessed 29 08 2023].
2023.
age.
[Online]
https://www.aje.com/arc/what-is-a-conceptual-framework/
Anon.,
Available
at:
[Accessed 01 09 2023].
2023.
Simplilearn.
[Online]
https://www.simplilearn.com/what-is-data-collection-article
Anon.,
2024.
inaccessibility.
[Online]
Available at: https://aiandaccessibility.ca/blog/literature-review-on-ai-and-accessibility/
[Accessed 21 03 2024].
Anon.,
2024.
Iago.
[Online]
Available
at:
https://www.enago.com/academy/how-to-develop-a-good-researchhypothesis/#:~:text=What%20is%20a%20Research%20Hypothesis%3F,when%20building%
20your%20research%20hypothesis.
[Accessed 31 01 2024].
Anon.,
2024.
indeed.
[Online]
Available
at:
https://www.indeed.com/career-advice/career-development/researchmethodology#:~:text=Research%20methodology%20is%20a%20way,address%20their%20
aims%20and%20objectives.
[Accessed 09 04 2024].
Anon.,
2024.
institute
data.
[Online]
Available
at:
https://www.institutedata.com/blog/big-data-analytics-for-businessintelligence/
[Accessed 11 03 2024].
Anon.,
n.d.
15writers.
Available at: https://15writers.com/research-onion/
[Online]
Anon.,
n.d.
247.
ai.
Available at: https://www.247.ai/insights/business-intelligence-and-big-data
[Online]
Anon.,
Available
approach/
n.d.
at:
Business
Research
Methodology.
[Online]
https://research-methodology.net/research-methodology/research-
Anon.,
n.d.
cityu.
Available at: http://www.is.cityu.edu.hk/staff/isrobert/phd/ch3.pdf
[Online]
Anon.,
n.d.
domo.
[Online]
Available at: https://www.domo.com/learn/article/how-business-intelligence-and-dataanalytics-enhance-each-other
Anon.,
n.d.
Editage.
[Online]
Available
at:
https://www.editage.com/insights/how-do-i-write-research-subobjectives#:~:text=As%20may%20be%20clear%20from,that%20lead%20to%20the%20obje
ctive.
67
Anon.,
n.d.
questionpro.
[Online]
Available
at:
https://www.questionpro.com/blog/data-collectionmethods/#:~:text=Some%20common%20data%20collection%20methods,about%20the%2
0study's%20subject%20matter.
Anon.,
n.d.
questionpro.
[Online]
Available
at:
https://www.questionpro.com/blog/data-collectionmethods/#:~:text=Some%20common%20data%20collection%20methods,about%20the%2
0study's%20subject%20matter.
Anon.,
n.d.
QuestionPro.
Available at: https://www.questionpro.com/blog/research-hypothesis/
[Online]
Anon.,
n.d.
rightpeoplegroup.
[Online]
Available at: https://rightpeoplegroup.com/blog/what-is-business-intelligence-big-data
Anon.,
n.d.
SCISPACE.
[Online]
Available at: https://typeset.io/questions/what-is-data-collection-procedure-in-research5e771ts0tb
Anon.,
n.d.
SCISPACE.
Available at: https://typeset.io/questions/what-is-time-frame-drb4z1b3ry
[Online]
Anon.,
n.d.
usertesting.
Available at: https://chatgpt.com/c/5026b500-f397-43b3-b708-33bb2bdef928
[Online]
Jain,
N.,
Available
at:
[Accessed 08 10 2023].
2023.
ideascale.
[Online]
https://ideascale.com/blog/what-is-research-objective/
Singh,
S.,
2023.
Researcher.Life.
[Online]
Available at: https://researcher.life/blog/article/what-is-a-conceptual-framework-andhow-to-make-it-with-examples/
[Accessed 20 07 2023].
Sreekumar,
D.,
2023.
paperpal.
[Online]
Available
at:
https://paperpal.com/blog/academic-writing-guides/what-is-researchmethodology
[Accessed 28 08 2023].
Wood,
T.,
2023.
fastdatascience.
[Online]
Available at: https://fastdatascience.com/big-data/big-data-and-business-intelligence-thepoints-of-intersection/
[Accessed 03 10 2023].
Appendix
68
Descriptive Statistics
Descriptive Statistics
Age Group
Valid
Missing
Mean
Std.
Deviation
Minimum
Maximum
Current Job Title Satisfaction with Role
2
3
4
2
3
4
2
3
4
1
0
2
0
1
0
1
0
2
0
1
0
1
0
3.000
2
0
4.000
1
0
5.000
Stress Due
Analytics
2
3
1
0
4.000
0.000
3.000
3.000
to
2
0
3.000
5.000
5.000
4.000
4.000
3.000
3.000
One Sample T-Test
One Sample T-Test
Z
p
12.500
8.000
8.000
80.000
6.000
< .001
< .001
< .001
< .001
< .001
Note. For the Z-test, the alternative hypothesis specifies that the mean is different from 0.
Note. Z test.
Descriptives
Descriptives
Years of Experience
Satisfaction with Role
Support from Organization
Hours Spent on Tasks
Work-Life Balance
Correlation
Data
4
1
0
2.000
0.000
4.000
4.000
Years of Experience
Satisfaction with Role
Support from Organization
Hours Spent on Tasks
Work-Life Balance
Big
N
Mean
SD
SE
Coefficient of variation
4
4
4
4
4
6.250
4.000
4.000
40.000
3.000
2.986
0.816
0.816
4.082
0.816
1.493
0.408
0.408
2.041
0.408
0.478
0.204
0.204
0.102
0.272
2.000
2.000
69
Partial Correlation Table
Years
Experience
Variable
1.
Years
Experience
of
2. Satisfaction with
Role
3. Confidence
Using Tools
in
of
Satisfaction with
Role
Confidence in Using
Tools
Pearson's r
—
p-value
Spearman's
rho
p-value
Kendall's Tau
B
p-value
Covariance
—
Pearson's r
0.410
—
p-value
Spearman's
rho
p-value
Kendall's Tau
B
p-value
Covariance
0.731
—
0.316
—
0.795
—
0.183
—
0.775
1.000
—
—
Pearson's r
0.954
0.816
—
p-value
Spearman's
rho
p-value
Kendall's Tau
B
p-value
Covariance
0.194
0.392
—
0.943
0.816
—
0.216
0.392
—
0.910
0.775
—
0.154
1.250
0.225
0.333
—
—
—
—
—
—
—
Note. Conditioned on variables: Hours Spent on Tasks.
Note. The standard error of effect size (Fisher's z) is currently unavailable for non-parametric partial correlations.
Linear Regression
Model Summary - Satisfaction with Role
Model
R
R²
Adjusted R²
RMSE
M₀
M₁
0.000
1.000
0.000
1.000
0.000
1.000
0.816
2.093×10-16
Note. M₁ includes Support from the Organization, Physical Health Issues
ANOVA
Model
M₁
Regression
Residual
Total
Sum of Squares
df
Mean Square
F
p
2.000
4.383×10-32
2.000
2
1
3
1.000
4.383×10-32
2.282×10+31
< .001
70
ANOVA
Model
Sum of Squares
df
Mean Square
F
p
Note. M₁ includes Support from the Organization, Physical Health Issues
Note. The intercept model is omitted, as no meaningful information can be shown.
Coefficients
Model
Unstandardized
Standard
Error
0.408
M₀
(Intercept)
4.000
M₁
(Intercept)
4.441×10-16
Support
from
Organization
Physical Health
Issues (No)
Standardizedᵃ
t
p
9.798
0.002
0.466
0.723
4.777×10+15
< .001
0.354
0.784
WorkLife
Balance
Support for
Continuous
Learning
9.536×1016
2.093×10-
1.000
16
1.000
2.961×10-
1.047×10-16
16
ᵃ Standardized coefficients can only be computed for continuous predictors.
Bayesian Correlation
Bayesian Pearson Correlations
Years
of
Experience
Variable
1. Years of
Experience
2.
Satisfaction
with Role
3.
Support
from
Organization
4. Work-Life
Balance
5. Support for
Continuous
Learning
Pearson's
r
BF₁₀
Satisfaction
with Role
—
—
Pearson's
r
0.410
—
BF₁₀
0.675
—
Pearson's
r
0.410
1.000
BF₁₀
0.675
∞
0.410
1.000
0.675
∞
Pearson's
r
0.410
1.000
BF₁₀
0.675
∞
Pearson's
r
BF₁₀
ᵃ Posterior is too peaked
Support from
Organization
ᵃ
—
—
ᵃ
1.000
ᵃ
∞
ᵃ
1.000
∞
—
—
ᵃ
1.000 ᵃ
—
∞
—
71
Log-Linear Regression
ANOVA
NULL
Satisfaction with Role
Support from Organization
Satisfaction with Role ✻ Support from
Organization
Residual
df
Residual
Deviance
p
3.846
0.000
3
1
1
4.351
0.505
0.505
0.146
0.000
1
0.505
df
Deviance
2
0
0