Impact of home working on construction employee motivation

Title of Project
Impact of home working on employee motivation. Focus on the
UK construction industry.
Name of Researcher
SECTION A
YES
NO
N/A
1
Will you describe the main research procedure to participants in
advance, so that they are informed what to expect?
·
2
Will you tell participants that their participation is voluntary?
·
3
Will you obtain consent from participants?
·
4
If the research is observational, will you ask participants for
their consent to being observed.
·
5

Will you tell participants that they may withdraw from the
research at any time and for any reason?
·
6
With questionnaires/interviews, will you give participants the
option of omitting questions they do not want to answer?
·
7
Will you tell participants that their data will be treated with full
confidentiality and that, if published, it should not be
identifiable as theirs?
·
If you have ticked No to any of questions 1-7, then your project
is NOT low risk
8
Will your project involve deliberately misleading participants in
any way?
·
9
Is there any realistic risk of any participants experiencing either
physical or psychological distress or discomfort?
·
10
Will it be possible to link identities or trace information back to

individual participants in any way?
·
11
Will the study involve discussion of sensitive topics (e.g. sexual
activity, drug use, ethnicity, political behaviour, potentially
illegal activities)?
·
12
Will financial inducements (other than reasonable expenses,
compensation for time or a lottery / draw ticket) be offered to
participants?
·
If you have ticked Yes to questions 8-12, then your project is
NOT low risk
YES
NO
N/A
13
Does your project involve work with animals?
·
14
Do participants fall into any of the following special groups?
If they do, please outline on page 2 how you will take account

of their needs.
Note that you may also need to obtain satisfactory Disclosure
and Barring Service (DBS) clearance
YES
NO
N/A
a) School Children?
·
b) People with learning or communication difficulties
·
c) Patients
·
d) People in custody
·
e) People engaged in illegal activities (e.g. drug-taking)
·
SECTION B
Please provide full details of your project below

(if insufficient detail is provided and the precise nature of the
study is not clear your supervisor will NOT approve the project
and your form will be returned)
State the aims and objectives of this research:
Research Aim
To investigate the impact of home working on employee
motivation in the UK construction industry.
Research Objectives
· To explore literatures on the concept of home working and its
effect on the level of employee motivation.
· To examine using questionnaires challenges of home working
and its impact on employee motivation.
· To study the participants, view on the context of homeworking
from the data collected from a range of quantitative data sets.
· To offer sensible recommendations on the merits and demerits
of homeworking and how it would underpin employee
motivation across the industry.
How will participants be recruited? Who will they be (i.e.
number, age, and gender?):
Participants would be worker in the construction industry. A
total of 60 individuals would participate in this research and
their ages ranging between 22-55years.
Details of the informed consent process (If required, use the
Informed Consent and Participant Information Sheet templates
provided and submit with this form):
Description of the method (please submit your research
instrument (survey questionnaire, interview questions that you
will be using to collect data with this form if relevant):
The method of data collection for this research would be survey
questionnaires.

Where will this research be conducted?
The research would be carried out in the University.
What steps have been taken or will be taken to ensure
appropriate permissions are obtained? (N.B. provide a copy of
correspondence i.e. letter, emails granting you permission must
be submitted with your ethics submission):
For this research, I ensured that I received an email from my
supervisor stating that I can proceed with my research, and it
does not pose any harm to humans or the environment.
Name (Caps)
Signature of Student
Date:
This project has been
· approved in its current form
declined and will need to be revised and resubmitted
The following required revisions are stipulated.
Print Name
Supervisor:
or
Module Leader:
Signature

Date:
PAGE
3
Abstract
This study focuses on the impact of home working on employee
motivation. Specific emphasis is on the UK’s construction
industry the growing rate of organizations within the sector
moving towards working from home as a normal business
practice. This research examined several academic literatures
that covered the concept of home working from a historical
perspective and its relevance within a contemporary context.
Furthermore, there were reviews on the theoretical frameworks
on motivation such as Maslow's needs theory and Herzberg's
two-factor theory which underpins those factors that could
inspire or demotivate employees' perceptions to work and how it
relates to the construction industry.
The method of data collection adopted for this research is the
questionnaires which are analysed using the quantitative method
of data analysis. To provide logical analysis to the findings
soured from the research, the deductive research design is
adopted as a means of verifying and justifying the data obtained
from the research and reasonable conclusion drawn from the
study. The limitation of this research could be assessed from the
fact that most of the secondary data sourced during the research
are obtained from non-academic sources which would need to be
verified against theoretical frameworks for data validity and
verification.

Contents
Abstract 1
SECTION ONE 3
1.0 Introduction 3
1.1 Research Rationale 3
1.2 Research Aim and Objectives 3
1.2.1 Research Aim 3
1.2.2 Research Objectives 3
1.3 Research questions 4
SECTION TWO 4
2.0 Review of literature 4
2.1 Theoretical perspectives 4
SECTION THREE 5
3.0 Research methodology 5
3.1 Research design 5
3.2 Research context 5
3.3 Procedure 5
3.4 Method of data collection 5

3.5 Method of data analysis 6
SECTION 4 6
4.0 DISCUSSION 6
4.1 Significance of proposed research 6
4.2 Practical implication 6
4.3 Research Limitation 6
5.0 Provisional work schedule 6
References 6
SECTION ONE1.0 Introduction
The recent past events that saw the outbreak of the COVID-19
pandemic brought about some of the most devastating
consequences that left global economies and businesses seeking
strategic measures for survival (Meyer et al. 2021; Strange,
2020). According to a current fact from the Office of National
Statistics (ONS, 2021), the United Kingdom recorded the
highest deficit in gross domestic profit GDP in 2020, which was
8.6% more than those of the G7 countries. Similarly, several
industry sectors across the UK were also hit by the adverse
effect of the pandemic (Allas et al., 2021), most especially the
construction industry which is one of the major contributors to
the economic development of the region (Shibani et al., 2020).
Consequently, while this research focuses on the UK’s
construction industry, it is important to point on the overbearing
impact of the pandemic on the sector. According to Suresh et al.
(2020) the post-COVID-19 episode saw an increase in the
number of organizations promoting the need for home working
for their employees. This initiative has been received with a
varying consensus of opinions by workers in the construction
industry (Jallow et al., 2020), particularly those individuals
whose job roles focuses on managerial and administrative
functions (Pirzadeh and Lingard, 2021). Hence, this research
seeks to review the underlying impact that the context of home
working has on employee motivation within the industry.1.1
Research Rationale
There has been an increase in the number of workers in the UK

construction industry that are critical of their organizations
move to adopt the option of home working against the
traditional basic work activities. The foregoing argument has
inspired this piece of research which is to examine the impact
which home working presently has on employees within the
industry. Thus, the findings from the research would help
provide useful insight into the experiences of workers engaged
in home working and its impact on their level of motivation. 1.2
Research Aim and Objectives1.2.1 Research Aim
To investigate the impact of home working on employee
motivation in the UK construction industry. 1.2.2 Research
Objectives
· To explore literatures on the concept of home working and its
effect on the level of employee motivation.
· To examine using questionnaires challenges of home working
and its impact on employee motivation.
· To study the participants, view on the context of homeworking
from the data collected from a range of quantitative data sets.
· To offer sensible recommendations on the merits and demerits
of homeworking and how it would underpin employee
motivation across the industry.1.3 Research questions
· What are the issues arising from home working in the UK
construction industry?
· How has the strategic approach by organizations in the
industry to adopt home working impacted employee motivation?
· What are the feasible measures for ensuring employees remain
motivated while working from home?SECTION TWO2.0
Review of literatures
The idea of home working has existed for decades throughout
the entire human cycle and is still being practised to date.
Toniolo-Barrios and Pitt (2021) seeks to describe home working
as any form of instituted structure that tends to alter the
traditional idea of where, how and the period for which a
specific job function would take place. Similarly, Waizenegger
et al. (2020) believes home working underpins any work
functionalities that ensure the employee delivers a task outside

the designated working hours. However, the context of home
working Van Der Lippe and Lippényi (2020) argues transcends
beyond the narrative of work duration for which an employee is
expected to deliver a task but should underpin the aspect of
delivering a job role outside an organizational space. A study of
the various scholarly opinions is relevant as they provide useful
narratives that enable the understanding of the concept of home
working as an age-long practise and its relevance in the
modern-day context.
However, there are several dimensions to home working which
can be assessed from the standpoint that most employees still
demand workplace visibility (Leduc et al., 2019). Maskuriy et
al. (2019) goes on to emphasize that while working in a
physical business environment might be perceived as outdated
from a contemporary sense, it remains a major determinate
factor in employee motivation. This is so because most
employees feel the workspace provides them with an
environment that ensures that are both mentally and physically
inspired to achieve over and beyond their current goals
(Dhamija et al., 2019). Furthermore, construction workers who
occupy managerial and administrative positions who are made
to execute their various job functions outside of the natural
work environment are becoming increasingly demotivated as
they enjoy the intricacies associated with the real workspace
(Ayodele et al., 2020). Studies show that workers in the UK
construction industry lack the motivation to work from home
because of associated factors such as distractions, mental health
issues, working transition and adaptability to mention a few
(Bakker et al., 2020). The understanding of these underlying
issues is important to this research as it would help assess the
key issues associated with home working and its impact on
employee motivation. 2.1 Theoretical perspectives
To create useful narratives on the research area two theories
would be studies which are Maslow's theory of needs (El Sayed
et al., 2019; Jesumoroti and Draai, 2021) and Herzberg’s two-
factor theory (Thant and Chang, 2021). These theories would be

tested on their relatedness to the context of home working in the
UK construction industry and how it could affect employee
motivation across the sector. While the needs theory helps to
analyse the very essence of workers attachment to their actual
work environment and their need for accomplishment, the two-
factor theory looks to focus on those elements that sustain an
employee's motivation to work.SECTION THREE3.0 Research
methodology3.1 Research design
This piece of study would seek to adopt a deductive method.
Furthermore, the essence of using a deductive method of this
research is because it enables the author to provide a line of
thought that is underpinned by relevant theories and backed up
with a range of relevant academic sources (Harding, 2018;
Saunders et al., 2019). Similarly, adopting a deductive research
design method would allow for a thorough investigation and
testing of existing theoretical perspectives which eliminates all
forms of speculative assertions (Saunders et al., 2019). Thus,
the researcher can then come up with the assumption that could
help justify if the data gathered during the research are true or
false (Harding, 2018; Saunders et al., 2019).3.2 Research
context
The context of this research focuses on home working and its
impact on employee motivation. The special focus is on the
construction industry in the UK and this study attempts to
critically analyse how the current approach of organizations
within the sector to adopt home working affects the
motivational level of employees. Furthermore, the employees in
the construction industry would be studied given the factors that
contribute to their motivation and demotivation and its
overbearing impact from an industry-wide perspective.
The sampling method would be utilized during this research to
explore the impact of home working on employees and the data
would be sourced from primary (questionnaires and surveys)
and secondary sources such as journal articles, publications,
textbooks, and industry reports (Saunders et al., 2019). 3.3
Procedure

The method for sourcing the samples to be investigated for this
research would be carried out using primary and secondary
sources. For ethical considerations, all sample data collected
from secondary sources would be properly acknowledged and
cited to prevent any form of academic misconduct. Furthermore,
all secondary sources must be correctly imputed without any
data falsification as this would ensure research integrity and
justification.
However, it is important to point out that sample data from
secondary sources may not be verifiable as most of the
information’s may be sourced from web pages and non-
academic publications which could pose some form of research
limitation. 3.4 Method of data collection
The data gathering method for this research would be done
using the quantitative data collection approach that comprises
information sourced from surveys and questionnaires. The
quantitative method of data collection provides a useful tool for
compiling and computing numerical and statistical data that
would help to justify an existing research hypothesis (Saunders
et al., 2019).3.5 Method of data analysis
Here, the method that would be used to analyse data for this
research would be the quantitative data analysis (Saunders et
al., 2019). However, data sourced using the quantitative method
of data collection cannot make a complete or logical sense from
an analytical standpoint. Hence, it is imperative to make use of
quantitative data analysis tools such as charts, graphs and
statistical variable quantities that justify the assumptions in
literature against those data obtained from primary and
secondary sources (Harding, 2018). Furthermore, the
quantitative data collected in this research would be analysed
using data analysis tools such as the excel spreadsheet and the
Statistical Package for the Social Sciences (SPSS) (Saunders et
al., 2019).SECTION 44.0 DISCUSSION4.1 Significance of
proposed research
This study reviews the impact of home working on employee’s
motivation in the UK construction industry, and from the

findings realized in the research reasonable recommendations
are put forward to organizations within the sector on ways to
sustain the level of motivation of the entire workforce. In
particular, the improvements are targeted towards inspiring
home working as a means of employee engagement.4.2 Practical
implication
Based on the findings obtained from this research, future
academic contributions could expand on other ways through
which organizations could make home working more inspiring
for employees especially those who are motivated to work in an
actual work environment.4.3 Research Limitation
Like any research, this research faces several limitations. For
instance, since the study sets out to investigate the impact of
home working on employee motivation, there would be some
adoption of secondary data which would be sourced from
several academic and non-academic authors. The assumptions
and hypothesis would need to be verified and justify using
existing theoretical contexts and their practicability in the real
world of works.5.0 Provisional work schedule
References
ALLAS, T., CANAL, M. and HUNT, V., 2021. COVID-19 in
the United Kingdom: Assessing jobs at risk and the impact on
people and places.
Available: https://www.mckinsey.com/industries/public-and-
social-sector/our-insights/covid-19-in-the-united-kingdom-
assessing-jobs-at-risk-and-the-impact-on-people-and-
places [Accessed on 11/09/2021].
AYODELE, O.A., CHANG-RICHARDS, A. and GONZÁLEZ,
V., 2020. Factors affecting workforce turnover in the
construction sector: A systematic review. Journal of
Construction Engineering and Management, 146(2), pp.
03119010.
BAKKER, A.B., PETROU, P., OP DEN KAMP, EMMA M and
TIMS, M., 2020. Proactive vitality management, work
engagement, and creativity: The role of goal

orientation. Applied Psychology, 69(2), pp. 351-378.
DHAMIJA, P., GUPTA, S. and BAG, S., 2019. Measuring job
satisfaction: the use of quality of work-life factors.
Benchmarking: An International Journal.
EL SAYED, A., ŠPAGO, S., ĆATOVIĆ, F. and NOVALIĆ, A.,
2019. New Approaches and Techniques of Motivation for
Construction Industry Engineers in B&H, International
Conference “New Technologies, Development and
Applications” 2019, Springer, pp. 736-745.
JALLOW, H., RENUKAPPA, S. and SURESH, S., 2020. The
impact of COVID-19 outbreaks on the United Kingdom
infrastructure sector. Smart and Sustainable Built Environment.
JESUMOROTI, C. and DRAAI, W., 2021. Analysis of
construction worker's demotivation that affects productivity in
the South African Construction Industry, IOP Conference
Series: Earth and Environmental Science 2021, IOP Publishing,
pp. 012014.
LEDUC, M., VANCE, B., EGER, T. and GODWIN, A., 2019.
Findings from a visibility survey in the construction
industry. Transportation research interdisciplinary
perspectives, 2, pp. 100056.
MASKURIY, R., SELAMAT, A., MARESOVA, P., KREJCAR,
O. and DAVID, O.O., 2019. Industry 4.0 for the construction
industry: Review of management perspective. Economies, 7(3),
pp. 68.
MEYER, B.H., PRESCOTT, B. and SHENG, X.S., 2021. The
impact of the COVID-19 pandemic on business
expectations. International Journal of Forecasting.
ONS, 2021. International comparisons of GDP during the
coronavirus (COVID-19) pandemic.
Available: https://www.ons.gov.uk/economy/grossdomesticprod
uctgdp/articles/internationalcomparisonsofgdpduringthecoronavi
ruscovid19pandemic/2021-02-01 [Accessed on 10/09/2021].
PIRZADEH, P. and LINGARD, H., 2021. Working from Home
during the COVID-19 Pandemic: Health and Well-Being of
Project-Based Construction Workers. Journal of Construction

Engineering and Management, 147(6), pp. 04021048.
SHIBANI, A., HASSAN, D. and SHAKIR, N., 2020. The effects
of a pandemic on the construction industry in the UK.
Mediterranean Journal of Social Sciences, 11(6), pp. 48.
STRANGE, R., 2020. The 2020 Covid-19 pandemic and global
value chains. Journal of Industrial and Business Economics, 47,
pp. 455-465.
SURESH, S., RENUKAPPA, S. and STRIDE, M., 2020. The
impact of Covid-19 on the UK construction industry.
THANT, Z.M. and CHANG, Y., 2021. Determinants of Public
Employee Job Satisfaction in Myanmar: Focus on Herzberg’s
Two Factor Theory. Public Organization Review, 21(1), pp.
157-175.
TONIOLO-BARRIOS, M. and PITT, L., 2021. Mindfulness and
the challenges of working from home in times of
crisis. Business Horizons, 64(2), pp. 189-197.
VAN DER LIPPE, T. and LIPPÉNYI, Z., 2020. Beyond formal
access: Organizational context, working from home, and work-
family conflict of men and women in European workplaces.
Social Indicators Research, 151(2), pp. 383-402.
WAIZENEGGER, L., MCKENNA, B., CAI, W. and BENDZ, T.,
2020. An affordance perspective of team collaboration and
enforced working from home during COVID-19. European
Journal of Information Systems, 29(4), pp. 429-442.
2 | Page
Kindly see below the meeting details and project breakdown
structure.
Assignment Topic: The Impact of home working on employee
motivation, Focus on the UK construction industry

Project Structure
First Page: Title Page
Second Page: Abstract
Third Page: Acknowledgment
Table of Contents
List of Figures
List of Tables
Abbreviation
Chapter one: Introduction
This chapter should include the following:
Background of the project: why am I doing it?
Research aims and objectives
Include research questions and scope
Methodology on a couple of paragraphs
Data Collection
Ethical consideration
Chapter Two: 1- 2,000 words
Literature review
Talk about the history, concerns definitions
Chapter Three: 3,000 - 4,000 words
Methodology
Take Saunders 2007 onion layer as one: Talk about what they

are, Qualitative/Quantitative, black or white, etc.
Questionnaires Data (to both employees and employers)
Sample size/how many people: Should be sent to 50/60 people
Chapter Four: Results and evaluation
10 to 12 questions and each question should have four to five
variants: Strongly agree, agree, neutral, disagree, strongly
disagree.
Results should be shown via graphs, charts, etc
Chapter Five: Discussion and conclusion
Results from interviews and questions
Results from the literature review in chapter two
What I obtained and also the limitations (another section in the
chapter)
Future recommendation (according to my research)
References
Appendices
21

Impact of Business Intelligence on Project Management
MSc. PROJECT MANAGEMENT
UNIVERSITY OF NORTHAMPTON
Abstract
This study is associated with the assessment of the impacts of
business intelligence in the management of projects. Business
intelligence is an expansive domain- related to the management
of the information related to the various aspects of business
management and functionality. This study has utilised primary
and secondary research methods for the development of the
research findings related to the achievement of the overall aim
of this study. Through the findings of the study, it has been
assessed that business intelligence tends to play a significant
role in the development of optimised business functions and
overall profitable projects. Moreover, the findings of the study
are in conformance with the associated past literature which
also authenticates the notion of business intelligence asserting
influence on the development of overall enhanced approaches of
management of projects. The conclusive findings developed
through the surveys highlight the domains of business project
management augmentation through the effective utilisation of
the business intelligence to render enhanced strategic
management, efficient functionality and overall profitable
business project ventures.
Table of Contents
CHAPTER ONE: INTRODUCTION 4
1.1 Introduction 4
1.2 Background and Rationale 4

1.3 Study Aim and Objectives 5
1.4 Research Questions 6
1.5 Scope and Limitations of Study 6
1.6 Study Layout 7
CHAPTER TWO: LITERATURE REVIEW 8
2.1 Overview and Significance of Business Intelligence 8
2.2 Typical Application of Business Intelligence 10
2.2.1 Product Sales Management 10
2.2.2 Exception Handling (Management by Exception) 10
2.2.3 Management of the Facts (Manage ment by Fact) 11
2.2.4 Customer Relationship Management (CRM) 11
2.2.5 Product Innovation and Promotion 11
2.3 Business Intelligence for Project Risk Management 12
2.4 Development of the Business Intelligence Network
Architecture System 15
2.5 Data Warehousing 17
2.6 Attributes of Data for BI 19
2.6.1 Integrated Data 19
2.6.2 Non-volatile 20
2.6.3 Update Over Time 21
2.6.4 Multi-granularity 21
2.7 Modes of Data Warehousing 22
2.7.1 Virtual Storage Mode 22
2.7.2 Storage Based on Relational Table 23
2.7.3 Classification Mode 23
2.7.4 Regression Mode 24
2.7.5 Time Series Mode 24
2.7.6 Clustering Mode 24
2.8 Discrepancies in Data Warehousing 25
2.9 Current Paradigms of Business Intelligence 28
2.10 Conclusion and Critical Analysis of Literature Review 30
CHAPTER THREE: RESEARCH METHODOLOGY 35
3.1 Introduction 35
3.2 Research Methodologies 35
3.3 Research Design 38
3.3.1 Data Collection 38

3.3.2 Data Analysis 39
3.4 Ethical Considerations 39
CHAPTER FOUR: FINDINGS AND DISCUSSION 41
4.1 Introduction 41
4.2 Findings and Discussions 41
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
56
5.1 Conclusion and Recommendation 56
References 61
Appendix………………………………………………………………
……………………..65
CHAPTER ONE: INTRODUCTION1.1 Introduction
Among the various contemporary concepts related to the
innovative approaches of business management, the utilisation
of business intelligence is considered a necessary norm in the
existing business environments (Cohen, 2017). Business
intelligence refers to the notion of the collective information
and data related to the existing paradigms of the respective
business markets, the consumer trends, the related competition
data and all relative business information required for optimum
functioning of the businesses in the globalised environment of
today (Rouhani & Lecic, 2018). This study will be focused
towards the assessment of the requirement and significance of
business information for the development of optimum project
management approaches and functions.
1.2 Background and Rationale
With the advancements of modern technology and the
increase in the corresponding utilisation in various domains of
business management, the paradigms of the codes of conduct in
this respect have been altered drastically (Rouhani & Lecic,
2018). This includes the introduction of numerous contemporary
approaches for the management of the various business
functions. Moreover, the globalised integration of the
informatics and data related to the development of the
associated paradigms through the means of the internet has

further changed the relative approaches of information
management required for the optimum functioning of the
various operations reflecting towards viable and profitable
business functions (Elbashir et al., 2008; Trieu, 2017).
Business intelligence in the existing paradigms of today’s
modern world includes not only the information associated with
the organisational statistics and information- rather, the
business intelligence in the modern world of today entails the
various unconventional data collection and processing
approaches (Cohen, 2017). This includes features such as big
data analysis and the use of the internet of things for developing
patterns and trends for optimised business functionalities.
Moreover, the introduction of various contemporary fields of
operations- such as game theory analysis and decentralised
economies- have resulted in further complicated systems of
managing the various business functions (Williams & Williams,
2010). However, the development of optimum approaches
resulting in the acquirement and processing of the information
intrinsically related to the business operations tends to reflect
towards the development of optimum approaches for the
rendering of effective and profitable business functions
(Williams & Williams, 2010; Turban et al., 2013).
Business intelligence is currently being applied in numerous
contexts of business management. This includes the domains of
sales management, inventory management, warehouse
management, systems management, operations management,
supply chain management, customer relationships management
and production management, including numerous other related
business management domains (Rouhani & Lecic, 2018). The
major challenge in the paradigms of the current bombarded
information systems is to assess the necessary information to
develop patterns and protocols for using the respective
information for the augmentation of the corresponding business
functions and operations. However, there are numerous existing
businesses and projects which tend to operate on the
conventional approaches of business management- uninformed

of the various advantages and benefits of the effective
utilisation of business intelligence for the optimisation of the
respective operative paradigms (Williams & Williams, 2010;
Trieu, 2017; Rouhani & Lecic, 2018). Hence, this study will be
pertained towards the notion of the impact of business
intelligence on the project management paradigms.
1.3 Study Aim and Objectives
This study will aim to “assess the impacts of business
intelligence on project management”. In order to effectively
attain the relative aim of this study, the following objectives
have been defined:
· To evaluate the various factors associated with the business
intelligence and its optimum utilisation
· To appraise the approaches of business intelligence in the
augmentation of the management of business projects
· To assess the influence of business intelligence on the
development of optimum functioning of project management
1.4 Research Questions
· What are the impacts of business intelligence on management
of projects?
· What are the various approaches used for utilisation of
business intelligence in management of projects?
· What are the factors influencing effective utilisation of
business intelligence for management of projects?
1.5 Scope and Limitations of Study
As this study is associated with the analysis of the impacts
of business intelligence on the management of projects, the
primary focus of this study will be pertained towards the
evaluation of the respective impacts. Although the study will
include the various aspects related to the assessment of the
impacts, the study is limited through the various constraints
related to the study limitations of student academic projects.
Moreover, the inclusion of the limited variables for the study
analysis also present the relative limitations of this research
study.
Furthermore, this study is limited by the availability of the

various literature being included in this study. Being a student
academic researcher, the study is also constrained by the
availability of related literature on the freely available websites
and data bases- in addition to the limited databases provided by
the university. In addition, the selection of the research
methodology also reflects towards the various limitations
related to this particular research study. Overall, the study
limitations are copious, which will also be highlighted in the
final section of this study.
1.6 Study Layout
This study has been divided into five chapter- the foremost
of which is the introduction to the overall topic under research.
This chapter has introduced the relative background as well as
the aim and objectives to be achieved through the completion of
this study. Moreover, the second chapter is related to the
critical review of past literature associated with this study. This
will encompass the related conceptual frameworks as well as the
included terminologies and concepts required for the completion
of this study.
The third chapter of this dissertation will include the
relative research methodology being selected for this study.
This will discuss the related research methodologies in addition
to the enumeration of the exclusive study design being used for
this particular study. Against the mentioned methodology for
this study, the fourth chapter will enumerate the various results
acquired through the effective implementation of the stated
research approach. In addition, the findings of the study will be
discussed against the associated secondary literature in order to
augment and emphasise the various findings acquired through
the implementation of the research methodology for this study.
Finally, the fifth chapter of this study will conclude the various
findings of this study- in addition to the highlighting of the
relative recommendations against the conclusive findings of the
completed study.
CHAPTER TWO: LITERATURE REVIEW

2.1 Overview and Significance of Business Intelligence
In recent years, the traditional domestic business
intelligence field has made the project deep target and lean
management modularisation as an important means to create
high-quality enterprises and implement project management,
and combined with the implementation of various large-scale
business intelligence projects, more prominent lean management
(Turban et al., 2013). Leading role in cost control and
optimisation of deep target management in the field of business
intelligence. The implementation of lean management in
business intelligence enterprises can rationally use enterprise
resources, promote profits and disadvantages, and maximise
economic benefits (Chen et al., 2012; Turban et al., 2013).
In today's society, the network economy characterised by
e-commerce is gradually maturing, and the emergence and
widespread use of the network revolutionises our way of life
(Collier, 2012). E-commerce has made the players on the arena
back to the same starting line – both traditional and emerging e-
commerce companies have the same opportunities in the era of
network economy. Emerging .com companies are launching B2B
or B2C transactions over the Internet, establishing platforms for
direct sales and service between suppliers or between suppliers
and customers, competing for market share from traditional
companies; traditional companies are not willing to show
weakness and have changed strategies while maintaining the
traditional channels, it opened up the second battlefield of e -
commerce (Minelli et al., 2012; Ramakrishnan et al., 2012;
Kimball et al., 2015).
The data comes from the feedback of each application
system within the respective project or organisation where the
BI systems are being integrated, and data mining can discover
new knowledge from the existing data warehouse and
automatically correct the content in the knowledge base at any
time, so the knowledge base in BI is a dynamic structure
(Collier, 2012). But business intelligence also has
shortcomings. The goal of business intelligence is the same as

decision Support System (DSS), in order to improve the
efficiency and accuracy of corporate decision-making (Minelli
et al., 2012). However, BI provides valuable and decision-
making information and knowledge through data analysis and
knowledge discovery tools. Users must use this knowledge and
knowledge to make judgments based on this knowledge and
experience, make decisions, and have little ability to make
intelligent decisions (Collier, 2012; Minelli et al., 2012). It
does not provide functions such as solution generation, program
coordination, and program evaluation, as in a specialised
decision support system, and it does not have the ability to
make group decisions (Minelli et al., 2012).
An obvious feature of the enterprise management model is
the need to undertake the project by means of bidding. First, the
enterprise must undertake the project, and then organise the
construction by project, which has typical project management
characteristics, that is to say the enterprise is based on project
management (IşıK et al., 2013). Combined with business
intelligence, it can extend the information application to the
enterprise's refined management, development strategy,
resource planning and decision support; apply business
intelligence, based on the information subsystem of the
construction enterprise, can be the transactional data of the
construction enterprise Transforming into analytical data,
revealing and excavating the information and knowledge
contained in the data, and playing a role in project management
(Khan & Quadri, 2012; IşıK et al., 2013). The main functions of
business intelligence technology in project management of
construction enterprises include two aspects (IşıK et al., 2013):
1 integrating data, solving information island problems, and
effectively promoting information sharing in project
management;
2 extracting, processing and organically combining massive
structured data and non-structure Data, access and create
information and knowledge that is conducive to decision making
in project management.

2.2 Typical Application of Business Intelligence
2.2.1 Product Sales Management
It includes product sales strategy, sales volume analysis, factor
analysis that affects product sales, and product improvement
forecasting. The sales model is established through system sales
information, and the overall sales model and regional and
departmental sales models are divided (Khan & Quadri,
2012). Analyse sales volume and sales strategy for sales models
that produce different results, analyse and evaluate the factors
affecting sales, and implement product launch and removal
plans for corresponding product sales plans according to
different sales environments to increase the amount of
marketing. Through historical data analysis, it is also possible
to establish a predictive model that increases sales (Debortoli et
al., 2014).
2.2.2 Exception Handling (Management by Exception)
It is a typical example of a business intelligence data mining
application that can monitor its performance goals in real time
and continuously, and the business intelligence system can
monitor its deviation from the planned goals (Debortoli et al.,
2014). When the deviation is too large, the system will notify
the corporate responsibility supervisor of the deviation status by
various communication methods, such as e-mail, at the first
time, thereby reducing the enterprise risk and improving the
company's revenue. Specific applications include credit card
analysis, fraud monitoring in industries such as banking and
insurance (Debortoli et al., 2014).
2.2.3 Management of the Facts (Management by Fact)
Regardless of goal management or exception management, the
power behind the support comes from the facts. E, the system
that maintains business operations, accumulates countless facts
and knowledge in daily transactions (Chiang et al., 2012;
Wixom et al., 2014). The business intelligence system combines

corporate goals, exceptions, and facts to enable managers to
further analyse causes or trends, query and detect relevant
information. In the age of lack of information, management
relies more on personal experience and intuition to manage and
make decisions. In the era of knowledge economy, enterprises
must implement fact management, relying on fantasy and
feelings, and using business intelligence to make scientific
decisions based on understanding the daily business situation of
enterprises (Wixom et al., 2014).
2.2.4 Customer Relationship Management (CRM)
Customer is a key factor in the survival of an enterprise.
Customer relationship management (CRM) is an important task
for enterprises. Through the customer relationship management
subsystem of business intelligence, companies can analyse
customer spending habits and consumption trends, improve
customer satisfaction, and then take corresponding
countermeasures to enhance customer retention, cultivate loyal
customers, and maintain good customer relationships (Debortoli
et al., 2014; Larson & Chang, 2016).
2.2.5 Product Innovation and Promotion
New products promote the development of enterprises, but the
development and promotion of new products must be based on a
certain market (Larson & Chang, 2016). Good corporate history
information can be used to predict market demand trends; data
warehouse technology in business intelligence systems to solve
which products need to be updated, whether it needs to carry
out some kind of advertising campaign; what kind of users are
targeted for advertising campaigns; Most likely, the potential
customers of the company, the direct advertising strategy for
such customers can outweigh the untargeted gain; predict the
future earnings of the project and so on (Debortoli et al.,
2014).
2.3 Business Intelligence for Project Risk Management

The risk is dangerous or damage to the project, it is a
possible event, it may not happen, it may happen, and the
project risk is a potential problem in the process of the event
that may hinder the success of the project or may exist during
the implementation of the project (Wixom et al., 2014). Team's
risk itself is a possible event, and only when the conditions that
trigger the risk or the conditions for the risk of conversion
exist, will the risk actually arise. What is called project risk
management is to control the conditions of conversion and risk
triggering through various methods such as transfer, avoidance
and mitigation. Risk management has certain practical
significance for the scope of the project and the cost of the
project scope and the actual progress of the project as well as
the plan estimation (Chiang et al., 2012; Wixom et al., 2014).
The prevention of risk management is very beneficial to
understanding the nature of the project, so that the employees of
the company can better understand the disadvantages of the
project. And the advantages, do a good job of project risk
prevention, and other knowledge of the integration project
management is also beneficial (Wixom et al., 2014). In addition,
risk management is also a form of insurance for the project,
which can effectively reduce the adverse events of the project,
and risk management is also the project implementation process.
In an investment, there will be a lot of costs involved, but in
any case, the cost management of the project will not exceed the
project revenue (Debortoli et al., 2014; Wixom et al., 2014). IT
project risk management is similar to general project risk
management. The main goal is to reduce the probability of
potential risks, so that the project can achieve the highest
possible profit while completing quality on time and quality.
While business intelligence (BI) can have a profound impact on
leadership decisions, in an increasingly complex environment,
enterprise development, mergers and acquisitions, and business
model changes mean enterprise IT infrastructure It may also
become more complex, and this complexity makes enterprises
more attractive to an architecture that is scalable and flexible,

because users need a new level of information integrity,
integration, execution, and availability (Chiang et al., 2012;
Alhyasat & Al-Dalahmeh, 2013). If the pre-implementation is
not adequate, and the expectations of BI for all industries are
too high, it is likely to lead to huge problems, pressures and
more potential risks in the implementation of a BI project. How
to minimise the risk of the BI project itself is undoubtedly a
problem that every CIO must consider in the implementation of
BI projects (Alhyasat & Al-Dalahmeh, 2013). To ensure
maximum impact from BI, the management of BI projects needs
to take into account all aspects of the business, from strategy to
implementation, project management and delivery. A well-
designed and executed BI strategy must be aligned with the
company's business goals, and technology, data, processes,
organisational structure, customer focus, management
requirements, internal support, etc. should be considered
together (Alhyasat & Al-Dalahmeh, 2013; Moro et al., 2015).
The risk of inaccurate definition of BI user groups and
requirements is undoubted, and the user is the centre of any BI
project. But because BI systems often have different user
groups, it is not an easy task to know who the end user is (Moro
et al., 2015). From the ordinary employees who use daily
reports to the high-end users who are engaged in data analysis
on a full-time basis, each group of people has different
requirements for the use of BI products. Some BI system
implementers have spent a lot of time completing a certain
requirement, but found that users have no interest in the content
displayed by the BI system, which is not what this user needs to
see most (Moro et al., 2015; Debortoli et al., 2014). There are
also many companies that do not have clear goals. Even BI is
only used to make unnecessary comparisons with users in the
same industry, rather than based on the development needs of
enterprises (Alhyasat & Al-Dalahmeh, 2013). This makes the BI
project more difficult and increases the risk. The most common
mistake made by technicians is to treat users as software design
experts (Alhyasat & Al-Dalahmeh, 2013; Wixom et al., 2014).

BI projects are often a wide-ranging work, from data collection,
clean-up, storage, to data calculation, analysis, presentation, to
information release. And monitoring and so on will be
involved. Although from the perspective of the big environment,
various technical means are relatively mature, for a large
amount of data, with modern technology (Demirkan & Delen,
2013). In some enterprises that are preparing to build a new BI
system, business leaders often generate some overly optimistic
ideas. They often set goals very high, and the scope of
implementation is too broad. The system above the line will
immediately solve all management decision problems. This will
not only affect the budget and duration of the project, but often
expand the scope too much and often fail to effectively plan and
utilise people and resources (Demirkan & Delen, 2013; George
et al., 2015).
The risk that the senior leadership does not pay enough
attention to BI is the analysis and summary of the basic
data. Logically speaking, employees are responsible for
providing data, which is then analysed by the BI system. The
final result is seen by the leadership (Wixom et al.,
2014). However, in many practical work, this is not the
case. Some senior leaders basically do not look at the reports of
the BI system, or they have the following employees manually
sort out the reports and show them to the leaders. In this
process of reorganisation, employees who are not responsible
will correct some errors, but the manual changes may be only
the final result, not the basic data. This means that the data in
the BI system is always wrong (Wixom et al., 2014; Moro et al.,
2015). This is obviously not the result that managers want. In
addition, BI projects require the company's spending from top
to bottom. This requires leaders to value BI projects and give BI
administrators greater power. Only in this way can we send staff
from other departments to support the operation of the BI
system (George et al., 2015). It is a pity that many failed
projects or projects with unsatisfactory results are caused by
insufficient power of the B project manager and the inability to

uniformly allocate the resources of the enterprise (Wixom et al.,
2014).
2.4 Development of the Business Intelligence Network
Architecture System
In the implementation strategy and actual implementation of BI,
there is a big disconnect between the current systems. The
existing BI system answers many corporate questions, but the
answers to these questions are usually limited to a specific
environment and department (Wixom et al., 2014; George et al.,
2015). On the contrary, many questions that are specific to the
entire enterprise or market level are not well answered. Another
limitation is that the data warehouse system focuses on the
analysis of historical data, rather than directly operating the
entire enterprise information value chain. Gartner defines BI
networks as: a flattened network that can be interconnected for
knowledge users, through which users of knowledge can share
and exchange data, metadata, data/process models, analysis
results, and decision information (Wixom et al., 2014). Through
this network, knowledge users can make more accurate
decisions faster and better. Gartner had predicted that by 2008,
BI network products will become mainstream BI system
products, and its network products will include collaborative
work components, workflow components and enterprise
application integration (EAI) software (Wixom et al., 2014;
Alpar & Schulz, 2016). These software components are of great
value in nature, and at the same time increase the coverage and
intrinsic value of BI network products. However, current
developments asserts that the technological advancements have
rendered even more enhanced changes (Alpar & Schulz, 2016).
The solution is more open, scalable, and customisable,
providing a customised interface while ensuring core
technology. In response to the unique needs of different
enterprises, BI system makes the system personalised when it
provides the same technology as the data warehouse technology
in the business intelligence system, that is, adding its own code

and solution based on the original solution, enhancing
Customised interfaces and extended features; Provides
enterprises with customised tools based on business intelligence
platforms, giving the system greater flexibility and scope of use
(Demirkan & Delen, 2013; Wixom et al., 2014; George et al.,
2015; Sharda et al., 2016).
From individual business intelligence to embedded business
intelligence is a major trend in business intelligence
applications, namely embedding business intelligence
components in the enterprise's existing application systems,
such as finance, manpower, sales, etc., so that the transaction
processing system in the general sense has the characteristics of
business intelligence (Wixom et al., 2014). Considering a
component of the BI system rather than the entire BI system is
not a simple matter, such as applying OLAP technology to an
application system, a relatively complete business intelligence
development process, such as enterprise problem analysis,
solution design, and prototype system development (Wixom et
al., 2014; George et al., 2015). Processes such as system
applications are indispensable. Transition from traditional
functions to enhanced functions. Enhanced business intelligence
capabilities are business intelligence capabilities that were
implemented in comparison to earlier SQL tools (Alpar &
Schulz, 2016). In addition to the traditional BI system
functions, most of the current BI systems have implemented the
functions of the data analysis layer. Data mining and enterprise
modelling are applications that BI systems should strengthen to
better improve system performance (Thamir & Poulis, 2015).
2.5 Data Warehousing
The concept of data warehouse is proposed by the book
“Building Data Warehouse” published by WH Gongn in 1992
(Thamir & Poulis, 2015). The introduction of data warehouse is
based on the rapid development of relational database, parallel
processing and distributed technology. In order to solve the
problem that information technology has a large amount of data

in the development process, on the other hand, useful
information is very poor solution (Wixom et al., 2014; George
et al., 2015). According to the definition given by W.H. lnmon,
the data warehouse is a subject-oriented, integrated, relatively
stable, time-varying data set to support management
decisions. The data warehouse is developed on the basis of the
original relational data, but it is different from the
organisational form of the database system (Alpar & Schulz,
2016).
The data composition of a general data warehouse
includes: Early detail level data, current level of detail data,
lightly integrated level data, highly integrated level data. After
the source data is integrated, it first enters the current level of
detail, and further synthesis according to specific needs, so as
to enter a light comprehensive level or even a highly integrated
level, the aging data will enter the early level of detail, which
shows that the data warehouse is different (Thamir & Poulis,
2015; George et al., 2015). The level of integration is generally
referred to as "granularity." The larger the granularity, the
lower the level of detail and the higher the level of
integration. The granularity of the data warehouse is a major
design issue because it affects the amount of data stored in the
data warehouse and the types of queries that the data warehouse
can answer (Demirkan & Delen, 2013; Alpar & Schulz,
2016). Data warehouses and databases are not the same
concept. The data warehouse is a comprehensive solution, and
the database is just a ready-made product. The data warehouse
needs a very powerful database engine to drive. Unlike
relational databases, data warehousing does not have a rigorous
mathematical theoretical foundation, and it is more engineering
(Thamir & Poulis, 2015). Due to the engineering nature of the
data warehouse, it can be divided into four aspects: data
extraction, data storage and management, data performance and
technical consultation of data warehouse design according to its
working process. In the field of data warehouse research, the
data warehouse is a system (Alpar & Schulz, 2016).

The data warehouse is enterprise-level. It contains all the topics
related to the enterprise. The data mart is departmental or
process-level. It is oriented to single or several topics and is
split and reorganised (Demirkan & Delen, 2013; George et al.,
2015). After the simple version of the data warehouse, it takes a
certain type of data separately for screening, reprinting and
exploiting, generating reports, and so on. In general, a data mart
is built on top of a data warehouse, meaning that its data comes
from a data warehouse. But there are also data marts that are
directly obtained from the data preparation area. Such data
marts are called independent data marts (Thamir & Pouli s,
2015). The advantages of independent data marts are: the ability
to quickly build a library structure that meets the decision-
making needs of a department, reducing development time and
development costs, but this development approach also
increases data inconsistencies and incomplete possibilities. The
limitations of independent data marts are even more pronounced
when decision analysis is needed across departments
(Wongthongtham & Abu-Salih, 2015). In order to combine the
overall advantages of enterprise data warehouse with the
advantages of rapid development and rapid effectiveness of
independent data marts, we need to take certain measures to
overcome its limitations and realise data sharing among data
marts (Thamir & Poulis, 2015).
2.6 Attributes of Data for BI
The determination of the theme directly affects the
availability and value of the data warehouse. Generally, the
analysis of the subject is determined by establishing an
enterprise data model (Sharda et al., 2016). It is necessary to
repeatedly communicate with relevant business analysts to
establish an analysis topic. Hence, the following attributes have
been defined which are necessary to be maintained and present
for the accomplishment of optimum BI systems:
2.6.1 Integrated Data

Among all the features of the data warehouse, integration is the
most important feature. Due to the independent construction of
the application data warehouse technology in the business
intelligence system, the correlation and consistency with other
systems are not considered in the design, so when the data
enters the data warehouse, some method is used to eliminate the
application problem (Thamir & Poulis, 2015; Alpar & Schulz,
2016). Many inconsistencies in the integration. Common
consistency processes are: consistency of coding, consistency of
units of measure, consistency of description or definition,
consistency of format or type size, and so on. Data integration
and transformation (ETL) is the most difficult, complex, and
time consuming in data warehouse implementation
(Wongthongtham & Abu-Salih, 2015). The data source of the
data warehouse is mainly the application system of the
enterprise. Before the data integration and conversion, the
recording system must be defined. That is, it must be
determined which systems or which data in the system meet the
requirements. For this, it is necessary to understand all the
systems. And the data in the system (Dinter et al., 2015; Alpar
& Schulz, 2016). Since the construction of each system is a
transaction processing service, the data organisation mode and
the data representation form are different from the data
organisation and expression forms used for analysis, and the
forms are various (Dinter et al., 2015). In order to process the
data, the rules must be determined according to the data form,
and the rules are Comprehensive and accurate direct impact on
the data quality of the data warehouse (Alpar & Schulz, 2016).
2.6.2 Non-volatile
In an operational system, data records are generally required to
be added, deleted, and changed one by one. In the data
warehouse, data operations in the operating system are not the
same. In the data warehouse environment, data updates in the
general sense are not performed (Dinter et al., 2015). Usually it
is loaded and accessed once, that is to say, the data is loaded

into the data warehouse once after being integrated. For data
maintenance, new records are generally used. Although an
update can be used in some cases, this is rarely possible
(Wixom et al., 2014). This does not mean that the data in the
data warehouse is always the same. In fact, the data in the data
warehouse needs to be cleaned up, but the clean-up here is not
simply to delete the data from the data warehouse, but to
transfer the early data of the data warehouse to a cheaper
storage device, or to retain the early details of the data. Another
important aspect of cleaning up the data warehouse is the
handling of sleep data and dirty data (Thamir & Poulis,
2015). Hibernate data refers to data that exists in the data
warehouse and that is not currently in use and that will not be
used in the future.
Dirty data refers to those that are erroneous. Although it is said
that we want to ensure the quality of the data, both types of data
will inevitably exist in the data warehouse (Dinter et al., 2015;
Alpar & Schulz, 2016). Identifying and processing these two
types of data is one aspect of improving the performance of the
data warehouse, although it is not so easy to handle. At the
beginning of the data warehouse construction, this problem may
not be particularly obvious, but with the operation of the data
warehouse, this will become an important part of data
warehouse management (Sun et al., 2015). The data
maintenance strategy in the data warehouse is an important part
of the data warehouse implementation. The rationality of the
maintenance strategy directly affects the function and
performance of the entire data warehouse (Wixom et al., 2014;
Alpar & Schulz, 2016).
2.6.3 Update Over Time
The operational database contains data of the "current value",
the accuracy of which is valid at the time of access, and the data
of the current value can be updated. The data in the data
warehouse is just a series of complex snapshots generated at a
certain moment. That is to say, the data warehouse stores the

corresponding historical data storage technology in the business
intelligence system (Alpar & Schulz, 2016). The data in the data
warehouse is always time-dependent. An important part of the
process of building a data warehouse model based on the
enterprise model is to remove the pure operational data and add
time elements (Sharda et al., 2016). The key code structure of a
data warehouse always contains a certain time element. Data
record triggering in the data warehouse is generally a "time-on-
time" type, such as starting a data processing activity at a
certain time (Alpar & Schulz, 2016).
2.6.4 Multi-granularity
Granularity refers to the level of refinement or
comprehensiveness of data stored in the data unit of the data
warehouse. The higher the level of refinement, the smaller the
granularity level, such as inventory-level data; on the contrary,
the lower the degree of refinement, the larger the granularity
level, such as monthly summary data or application summary
data (Wixom et al., 2014). Obviously, granularity is closely
related to the performance and functionality of the data
warehouse. It deeply affects the amount of data stored in the
data warehouse, and affects the type of query that the data
warehouse can answer (Sharda et al., 2016).
2.7 Modes of Data Warehousing
Classification mode and regression mode are the most
common modes used. In addition, the above modes can be
further divided into descriptive mode and predictive mode
(Thamir & Poulis, 2015). Descriptive mode the results of the
data before the establishment of the mode are known and can be
directly used to detect the accuracy of the mode. The generation
of the mode is carried out under supervision, so it is also called
supervised knowledge, such as classification mode, Regression
mode, time series mode, etc.; in the establishment of these
modes, the predictive mode uses a part of the data as a sample,
and uses another part of the data to test and correct the mode

(Olszak & Ziemba, 2012; Wixom et al., 2014). Before the mode
is established, the result is unknown, and the mode is generated
without any supervision. Discussed below are some of the
modes of data warehousing currently being used in businesses
(Wixom et al., 2014).
2.7.1 Virtual Storage Mode
Virtual storage mode is the data organisation form of virtual
data warehouse. It does not have a dedicated data warehouse to
store data. All data is stored in the source database. Only when
it needs to be analysed or inquired depends on certain rules
(George et al., 2015). Extract the corresponding data from the
source database to temporarily compose the required data view
for user analysis or query. This method is less expensive, soft
and flexible, but requires high source data, and the release must
conform to the data warehouse's format and organisational
requirements (Moro et al., 2015; Larson & Chang, 2016).
2.7.2 Storage Based on Relational Table
The storage method based on the relational table is based on the
storage method of the relational table. The data is still stored in
the relational model database, which causes the dimension table
and the fact table to be separated between the tables, and the
virtual multidimensional database is constructed according to
the management of the metadata (Wixom et al., 2014). This
organisation can be divided into a star model and a snowflake
model. The star model is shown in Figure, in the middle is the
fact table, which is the centre of the query by dimension,
surrounded by the information of the dimension related to it,
each dimension table has its own attributes, the dimension table
and the fact table are associated by the dimension keyword
(Alhyasat & Al-Dalahmeh, 2013). Regional dimension table
time dimension table student fact table school category
dimension table star model snowflake model is an extension of
the star model, which adds a layer in the dimension table,
reflecting the difference in dimensions The division of

granularity makes the level of this data warehouse clearer and
the logical relationship more clear, but at the same time
increases the complexity of the system and reduces the
versatility (Demirkan & Delen, 2013; George et al., 2015).
2.7.3 Classification Mode
Classification mode is a classification function (classifier)
that can map data items in a data set to a given class (Wixom et
al., 2014). The classification mode is often expressed as a
classification tree. Searching from the root of the tree according
to the value of the data, going up along the branch where the
data is satisfied, and going to the leaves to determine the
category (Moro et al., 2015).
2.7.4 Regression Mode
The function definition of the regression mode is similar to the
classification mode. The difference is that the prediction values
of the classification mode are discrete, and the prediction values
of the regression mode are continuous (George et al., 2015). If
the characteristics of an animal are given, the classification
model can be used to determine whether the animal is a mammal
or a bird; given the education and work experience of a person,
the regression model can be used to determine the extent to
which the person’s annual salary is (George et al., 2015).
2.7.5 Time Series Mode
The time series mode predicts future values based on trends in
data over time. Here we have to take into account the special
nature of time, like some periodic time definitions such as the
week, month, season, year, etc., special days such as the impact
of holidays, the calculation of the date itself, and some places
that require special consideration (Thamir & Poulis, 2015).
Such as the correlation before and after the time (how much
influence the past has on the future). Only when fully
considered there are also some areas that require special
considerations such as the correlation before and after time

(how much influence the past has on the future)
(Wongthongtham & Abu-Salih, 2015).
2.7.6 Clustering Mode
The clustering mode divides the data into different groups. The
difference between the groups is as large as possible, and the
difference within the group is as small as possible
(Wongthongtham & Abu-Salih, 2015). Unlike the classification
mode, clustering is not known until it is divided into groups and
groups, and it is not known which group/number of data items
to define. In general, people with business knowledge should be
able to understand the meaning of these groups
(Wongthongtham & Abu-Salih, 2015). If the resulting model is
incomprehensible or unavailable, the model may be meaningless
and needs to go back to the previous stage to reorganise the data
(Sun et al., 2015).
2.8 Discrepancies in Data Warehousing
The huge amount of data and the shortcomings of
traditional data management methods have caused the
phenomenon of “data congestion” (data jail) in most enterprises,
which is not conducive to the management of enterprises and to
the effective use of information. Therefore, how to solve the
data congestion, and at the same time enable these data to fully
play a role has become a hot issue in the business development
of enterprises (Thamir & Poulis, 2015). The establishment of
the concept of a new enterprise of “data two assets”. At present,
most large and medium-sized enterprises are information-rich
organisations, and the performance of an information-rich
organisation depends not only on factors such as products,
services or locations, but more importantly on knowledge. And
knowledge from data-information is a not-simple process
(Wixom et al., 2014). The essence of business intelligence is to
transform data into knowledge, and to focus on knowledge
discovery and mining, so that the data assets of enterprises can
bring obvious economic benefits, reduce the influence of

uncertain factors, and enable enterprises to obtain new
competitive advantages (Sharda et al., 2016).
E-commerce based on the business model of internet: The
development and application of technologies such as e-mail,
electronic data exchange, electronic payment system, and e-
marketing provide a market and living environment for business
intelligence systems (George et al., 2015; Moro et al.,
2015). Furthermore, the development of business intelligence
also benefits from the development of related technologies, such
as high-capacity storage technology on hardware, parallel
processor technology, software mining tools, data w arehouse
environment management tools, etc., enabling enterprises to
invest at lower cost. Business intelligence, which in turn leads
to a higher return on investment (Demirkan & Delen,
2013). Coupled with Internet technology, data access and
sharing between distributors, suppliers, business partners,
retailers and manufacturing companies is possible, so the
research and application of business intelligence has been paid
more and more attention (George et al., 2015).
There are five main reasons why the transaction processing
environment is not suitable for decision support system (DSS)
applications (Dinter et al., 2015; Alpar & Schulz, 2016):
(1) The performance characteristics of transaction processing
and analysis processing are different: In the transaction
processing environment, the user's behaviour is characterised by
high frequency of data access operations. The time required for
each operation is short, so the system can allow multiple users
to use system resources in a time-sharing manner while
maintaining a short response time; in an analytics processing
environment, the user's behaviour pattern is completely
different (Dinter et al., 2015). A DSS application may need to
run for several hours, which consumes a lot of system
resources. It is obviously not appropriate to put two
applications with such different processing performance in the
same environment (Dinter et al., 2015).
(2) Data integration issues DSS needs to use integrated

data: Comprehensive and correct data is the premise of effective
data warehousing technology for effective analysis and decision
making in business intelligence systems (Alpar & Schulz,
2016). The more complete the relevant data is collected, the
more reliable the results will be. The true state of data in most
of today's enterprises is decentralised rather than
integrated. There are many reasons for this distraction, mainly
transaction processing application dispersion, "spider web"
problems, data inconsistencies, external data, and unstructured
data (Alpar & Schulz, 2016).
(3) Data dynamic integration problem: The biggest disadvantage
of static integration is that if the data changes in the data source
after data integration, these changes will not be reflected to the
decision maker, resulting in the use of outdated data by the
decision maker (Sharda et al., 2016). The integration data must
be refreshed in a certain cycle, which we call dynamic
integration. Obviously, transaction processing systems do not
have the ability to dynamically integrate.
(4) Historical data problem: Transaction processing generally
only needs current data. In the database, short-term data is
generally stored, and the retention period of different data is
also different. Even if some historical data is preserved, it is not
fully utilised. But for decision analysis, historical data is quite
important, and many analytical methods must rely on a large
amount of historical data (Dinter et al., 2015; Alpar & Schulz,
2016). Without detailed analysis of historical data, it is difficult
to grasp the development trend of the enterprise. DSS has
higher requirements on the breadth of data and time, and the
transaction processing environment is difficult to meet these
requirements (Alpar & Schulz, 2016).
(5) Comprehensive problem of data: A large amount of detailed
data is accumulated in the transaction processing system. In
general, DSS does not analyse these detailed data. Before the
analysis, it is often necessary to synthesise the detailed data to
different degrees (Alpar & Schulz, 2016). Transaction
processing systems do not have this comprehensive capability.

According to the standardisation theory, this integration is often
limited by a data redundancy. To improve the efficiency and
effectiveness of analysis and decision making, analytical
processing and its data must be separated from operational
processing and its data (Dinter et al., 2015). Analytical data
must be extracted from the transaction processing environment,
reorganised according to the needs of DSS processing, and a
separate analysis processing environment is established. The
data warehouse is a kind of data storage that appears to build
this new analytical processing environment (George et al.,
2015).
2.9 Current Paradigms of Business Intelligence
With the establishment of the online information platform
of securities companies and the development of online
transactions, business intelligence has also had corresponding
solutions (Moro et al., 2015). Using Web log analysis and
mining technology, the company can not only better track
existing customers, but also discover interesting
behaviour/transaction patterns that cannot be found in many
offline trading system research and analysis processes, and even
affect current market potential factors. There is a certain degree
of independence between Web-based business intelligence and
business intelligence based on internal business systems, but
they are inextricably linked (Sharda et al., 2016). If you can
combine the two, it will make the enterprise's intelligent
platform a new level and more powerful. In the following
sections, the author will focus on this content. In short, with the
deepening and intelligent transformation of the competition,
business intelligence can effectively help securities companies
to integrate various resources and conduct creative analysis and
mining according to user needs, thus precipitating the
company's many years of operation (Alpar & Schulz, 2016). A
large amount of data is transformed into knowledge and further
becomes a driving force for business growth. Business
intelligence has broad application prospects in the securities

industry. It can provide securities companies with more
comprehensive, accurate and secure data; more convenient data
query application methods; more forward-looking data analysis
and mining methods (Alpar & Schulz, 2016). This will bring a
full range of information control capabilities, efficient resource
utilisation, rapid response and decision making.
From the perspective of system architecture, business
intelligence systems generally consist of data warehouse (or
data field), data analysis, data mining, online analysis, data
backup and recovery (Thamir & Poulis, 2015). The business
intelligence system extracts useful data from the data collected
by different data sources, cleans the data to ensure the
correctness of the data, converts the data, reconstructs it and
stores it in the data warehouse or data field (the data becomes
information at this time), then find the right query and analysis
tools, data mining tools and OLAP tools to process the
information (when the information becomes the knowledge of
the decision-making), and finally present the knowledge to the
user and turn into decision-making (Thamir & Poulis, 2015;
Alpar & Schulz, 2016).
Decision support tools includes everything from basic
query and reporting tools to advanced online analytical
processing to information mining tools. All tools support GUI
client interfaces, most of which can be used on the Web
interface. Most of these tools can handle database information,
and some can handle complex and unstructured information on
file systems, multimedia, mail, or web servers (Sharda et al.,
2016). The access tools include application interfaces and
middleware that enable customer tools to access and process
business information in databases and file systems. Database
middleware allows customers to transparently access various
heterogeneous database servers in the background. Web server
middleware allows Web clients to connect to the database
(Thamir & Poulis, 2015). The data management part generally
uses three layers of information storage. The highest level is the
data warehouse. The data warehouse integrates the business

information of the whole enterprise. The middle level is the
department data warehouse, which stores the commercial data
warehouse of a commercial unit, user group or department
(Alpar & Schulz, 2016). Application information of technology
in a business intelligence system, these data fields can be
directly established on the basis of the enterprise business
operating system; the lowest level of the structure stores the
tailored information based on user and application requirements
(Wongthongtham & Abu-Salih, 2015).
Furthermore, data warehouse model and construction tools are
used to capture data from the operating system and external data
source systems, process and convert, and finally load the data
into a global or departmental data warehouse (Sharda et al.,
2016). In addition, the metadata management manages metadata
related to the entire business intelligenc e system, including
technical metadata used by developers and administrators, and
business metadata that supports business users (Alpar & Schulz,
2016).
Data mining technology is mainly used to find the hidden rules
or relationships between data from a large amount of data. It is
usually automatically recognised by the machine and does not
require more manual intervention (Sharda et al., 2016). Using
data mining technology, it can provide intelligent and
automated assistance for users' decision analysis. It can be
applied in many fields such as retail, finance and insurance, and
medical industry (Moro et al., 2015). Moreover, cluster model,
when the data to be analysed lacks description information, or
cannot be organised into any classification mode, the cluster
model can be used. The clustering model divides user data into
groups that are different from each other according to a measure
of similarity. The data in the group is similar, and the data
between the groups is quite different (George et al., 2015;
Sharda et al., 2016). The core of the clustering model is to
convert some obvious methods of measuring proximity to
quantitative testing methods (Wixom et al., 2014; George et al.,
2015).

2.10 Conclusion and Critical Analysis of Literature Review
With the help of business intelligence and the use of
massive amounts of data accumulated in the medium and long
term, four applications can be realised:
(1) Analysis of customer classification and characteristics:
According to the customer's large number of consumption
records and customer's archives data, the customers are
classified, and the consumption power, consumption habits,
consumption cycle, demand tendency and credibility of each
type of customers are analysed (Kimball et al., 2015). Class
customers bring the most profit to the company, which kind of
customers only bring the least profit to the company and
demand the most return, and then give different services and
offers to different types of customers (Ramakrishnan et al.,
2012; Kimball et al., 2015).
(2) Analysis of marketing strategy: The use of business
intelligence to realise the simulation of the marketing strategy
on the model, the simulation results will indicate whether the
marketing strategy is appropriate, and the company can adjust
and optimise its marketing strategy to maximise its Success
(Kimball et al., 2015).
(3) Analysis of operating costs and income: Cost accounting for
various types of economic activities, comparing the balance of
income between business income and various expenses,
analysing the curve of economic activities, and obtaining
corresponding improvement measures and measures to reduce
(Chen et al., 2012; Kimball et al., 2015)
(4) Analysis and prevention of fraudulent behaviours: Using on-
line analysis and data mining techniques, summarising the rules
of fraudulent fees and arrears behaviours, establishing a set of
rules for fraudulent behaviour and arrears behaviour on the
basis of data warehouse, warning various frauds Fees, arrears,
and reduce corporate losses (Kimball et al., 2015).
Data mining is the process of mining knowledge from a large
amount of data stored in a database, data warehouse or other

information base. Data mining technology stems from the direct
demand of business, so it has a wide range of use values in
various commercial fields, mainly in the following aspects (Sun
et al., 2015; Sharda et al., 2016):
(l) Customer analysis with the "customer-centric" business
philosophy: In-depth knowledge, analysis of customers,
understanding of customers and guiding customers' needs has
become an important issue in business operations. By analysing
the accumulated transaction data, customers can be classified
according to various customer indicators (such as natural
attributes, income contribution, transaction amount, value, etc.),
and then the behaviour patterns of different types of customers
can be determined, so as to adopt corresponding marketing
measures to promote the maximisation of corporate profits
(Sharda et al., 2016).
(2) Optimisation of operational planning and enterprise
resources: Cost savings are the key to corporate
profitability. By analysing historical financial data, inventory
data and transaction data, you can find the key points of
enterprise resource consumption and the input-output ratio of
main activities, so as to provide decision-making basis for
optimal allocation of enterprise resources, such as reducing
inventory, improving inventory turnover, and improving Capital
usage rate, etc. (Sun et al., 2015)
(3) Anomaly detection: In many commercial fields, anomalous
events have significant commercial value, such as customer
churn, bank letter chapter business intelligence system core
technology card fraud, mobile phone arrears in
telecommunications, and signs of special diseases in medicine,
etc. These anomalous events can be quickly and accurately
identified through singularity analysis in data mining (Sun et
al., 2015).
(4) Management of enterprise analysis models Enterprise
members use data mining to tune and customise models. This
will gradually accumulate model libraries that meet the needs of
the enterprise and become an important part of the enterprise

knowledge base. At present, data mining technology is very
practical in commercial applications (Thamir & Poulis, 2015).
Specific examples of common business applications
are: Customer segmentation, customer retention, target
marketing, customer development, fraud detection, shopping
basket analysis, credit scoring, credit risk assessment, portfolio
management, market analysis, security management, customer
profitability analysis, resource management, profit analysis,
cross-selling , incremental sales, customer service automation,
activity analysis, sales forecasting, revenue forecasting, demand
forecasting, claims analysis, performance analysis, store
location, inventory control, process quality control, failure
analysis, prescription analysis, and more (Wixom et al., 2014).
Furthermore, the most significant value of the risk management
of the business intelligence project is the forecasting trend and
the decision-making. Therefore, the basic data preparation work
in the early stage of the BI project must be done relatively well
(Debortoli et al., 2014). Informative data is the cornerstone of
success for BI projects: The more basic, more detailed, and
more accurate the underlying data, the more accurate BI's
predictions for the enterprise. Correspondingly, the survey of
users of business analysis companies found that the risks posed
by the quality of the underlying data and data integration are
also the biggest problems that plague the B1 project (Alhyasat
& Al-Dalahmeh, 2013; Moro et al., 2015). Therefore, data
quality is a very important factor in the BI field. The quality of
the data reflects the value of the data. The higher the quality of
the data, the greater the value of the data. Enterprises must use
data to analyse and make decisions, and must build high-quality
data (Debortoli et al., 2014). Without accurate data as the basis,
data analysis and data mining are not reliable, and corpor ate
decisions will be significantly affected and biased. After some
BI projects were launched, the person in charge found that the
results of the system calculations were quite different from the
actual calculation results. After reviewing it, it was found that
the original data was inaccurate (Alhyasat & Al-Dalahmeh,

2013). Only reorganise the underlying data. The main function
of the BI system is to perform statistics and analysis on the
existing basic data in order to provide data support for
management decision-making. If the underlying data is
inaccurate, it can make BI projects difficult. Therefore, the
project administrator needs to take appropriate measures to
ensure the accuracy of the underlying data. Many users already
have multiple applications before building a BI system (Chiang
et al., 2012; Debortoli et al., 2014).
The biggest difference between data warehouse and traditional
database storage is that the database is used for enterprise daily
transaction processing, while the data warehouse is used for
business analysis processing (Alpar & Schulz, 2016). The
purpose of the data warehouse is to analyse the business
information to assist the enterprise decision-making without
affecting the daily operation processing, and to provide an
application basis for the decision support system. Therefore,
data warehouses and databases are two types of data
management systems that are used for different purposes of
business operations. The data storage technology is the core of
the data warehouse technology (Thamir & Poulis, 2015). The
data integrated in the data warehouse usually organises the data
in a star schema, that is, a one-dimensional table structure of
the fact table. The fact table is also called the primary table,
including quantitative or actual data for business activities. This
data can be measured numerically and consists of multiple rows
and columns; the dimension table, also known as the auxiliary
table, is generally small and is descriptive data reflecting a
certain dimension of the business. The fact table and the
dimension table are connected by relationship (Thamir &
Poulis, 2015; Alpar & Schulz, 2016).
CHAPTER THREE: RESEARCH METHODOLOGY3.1
Introduction
Research methodology tends to form an integral part of
academic research studies. This includes the selection of the

most appropriate approach for rendering the various research
activities in order to achieve the overall objectives of the
respective research study under consideration (Alvesson &
Sköldberg, 2017; Creswell & Creswell, 2017). In this respect,
this chapter will include the discussion of the various research
methodologies generally used in academic research studies. In
addition, the exclusive study design which is used for the
fulfilment of the research objectives will also be entailed in this
chapter. Furthermore, the associated ethical considerations
related to the academic research studies will also be highlighted
in this chapter.
3.2 Research Methodologies
The standardised approaches of research are generally
associated with the differences in the collection and the analysis
of the data for the completion of the research studies. The
researcher's methodological orientation determines the research
methods he/she may take when studying a problem (Cohen et
al., 2013). If the method involves the activity of collecting data,
the methodology explains the rationality of conducting these
activities. Methodological orientation should be the connection
point between epistemology and method, or the basis of design
research programs (Cohen et al., 2013).
At present, the research on educational research methods and
methodology itself has not attracted the attention of education
researchers. In recent years, research the author gave a certain
level of research on a new term in the field of educational
research methods. At the same time, people have also discussed
some important research methods in the study of education,
quantitative research (Creswell & Creswell, 2017). However,
after reviewing these research results, most of the research i s
concentrated on the theoretical level, and some opinions are
expressed. Based on the author's pure speculative thinking,
there are often different opinions and different situations.
Therefore, so far, quantitative research has not yet formed a
comprehensive, objective and correct understanding (Cohen et
al., 2013).

The investigation and comparative analysis of the application
status reveals objectively the existing status and role of
quantitative research in educational research. The research uses
literature analysis, content analysis, historical law, comparative
research method, graphic method and table (Creswell & Clark,
2007). They are sufficient to summarise the basic characteristics
and mode of operation of qualitative research. The various
variants of qualitative research will be attributed to possible
changes in the six elements. In the following, these elements
will be discussed in the form of the six major issues involved in
the research process (Alvesson & Sköldberg, 2017).
Although the elements are given in order, it does not mean that
the researcher must follow this order rigidly. Since qualitative
research is an evolving dynamic process, the elements often
permeate each other and cycle (Creswell et al., 2003). It is
generally believed that the theoretical basis of quantitative
research is positivism and realism; the theoretical basis of
qualitative research is naturalism, constructivism or other post-
positivist philosophical schools. This kind of argument can be
established in some cases, but it is not a certain rule. In fact,
the philosophical and methodological practices that were
thought to be opposite each other in the past can actually be
merged (Cohen et al., 2013). For example, it is not difficult to
find a researcher who holds both positivist beliefs and
qualitative research methods.
However, there are also qualitative researchers who are willing
to use deduction to prove a predetermined theory. Induction and
deduction can also illustrate the differences between other
methodologies. When using inductive methods, the research
object is the centre of research (Alvesson & Sköldberg,
2017). The research object not only plays an important role in
the construction of the theory, but also the object of the
research object is protected, and the authority of the
interpretation of the problem is also highly respected. In
contrast, the deductive rule is easy to focus on the researcher,
and the researcher is the most authoritative from beginning to

end. The above differences show that the attitude of the
researcher's voice and authority is an important indicator to
distinguish different types of research (Cohen et al., 2013).
Therefore, exploring qualitative research methods outside the
circle will help to improve the traditional qualitative research
methods and make them eventually develop in the direction of
localisation. In the discussion of qualitative research methods in
the West. How to define qualitative research is a hot issue. This
article explores a possible definition framework to outline the
basic image of a qualitative research approach (Creswell et al.,
2003; Creswell & Creswell, 2017). The discussion will focus on
the six basic elements of qualitative research and introduce the
Western genres and practices in these areas for reference by
domestic educational researchers. The discussion of qualitative
research methods often involves the definition of qualitative
research. Since qualitative research involves multiple methods
and involves the collection of multiple empirical data, it is
difficult to give a straightforward definition (Creswell & Clark,
2007).
The method usually represents a set of methods for collecting
data; while the inquiry reflects the position that should be
recognised in terms of cognition and methodology. Sherman
went on to point out that qualitative researchers who emphasise
inquiry are likely to form a consensus. Similarly, qualitative
researchers who focus on methods are also likely to reach
consensus (Cohen et al., 2013). And if qualitative research is
used as the method and the end of inquiry, these two aspects are
mutually exclusive. It is difficult to form a harmonious and
unified image. In order to visually summarise qualitative
research methods, this paper will present a graphical definition
framework. The framework can describe the whole process of
qualitative research. It also illustrates the difference between
inquiry and method (Alvesson & Sköldberg, 2017). 3.3
Research Design
3.3.1 Data Collection

This study will be based upon the conduction of primary as
well as secondary research. In this respect, the primary research
is associated with the development of research surveys through
the intrinsically designed questionnaire reflecting towards the
various aim and objectives of this particular study. In this
regard, the selected population for the development of the
primary research study, the population of the study consisted of
a total of 75 participants selected for the survey. However, due
to the limitations of time as well as other constrictions, only 24
of the respondents completely reverted with the questionnaire
surveys. The findings of the results of the survey will be
mentioned in the following chapter.
For the development of the primary research findings, the
participants were distributed an online survey questionnaire
through the use of Google docs form. This form enables the
acquisition of the answers given by the participants which is
easily accessible through the online form. The participants were
intimated regarding the surveys both personally as well as
through their respective online contacts. The participants were
also given the consent form in order to respect their personal
interests and consents regarding the participation in this study
survey.
Moreover, in order to augment the various findings of the
study and the authentication of the respective findings, the use
of secondary research data has also been utilised. This will be
mainly included in the discussion of the research findings of
this study. The integration of the secondary data with the
primary research findings will enable enhance comprehension of
the various study results. In addition, this will also enable
clarity in terms of the discussion of the various findings of the
study under the relate contexts of the associated and
authenticated findings of other peer-reviewed journal articles
related to the subject under consideration. The selection of the
various research study associated with this study was
maintained through the search on various databases including
“Google, Bing, Yahoo, Arxiv, TandFonline, Wiley,

Researchgate and academia.” The search of the relevant
materials for the study was maintained through the use of
exclusive this included the use of keywords related to the
objectives of this study.
3.3.2 Data Analysis
For the analysis of the various findings of the study for the
primary study of this thesis, the use of graphical tabulation was
maintained. This was carried out on the excel sheets through
applying appropriate formulae and analytical inputs related to
the survey. This resulted in the development of graphical
representations of the data analysed through the questionnaire
survey. As the questions included in the survey were generally
close-ended, the statistical tabulation of the results was
rendered quite easily. Moreover, for the augmentation of the
research findings against the related secondary research data,
the use of interpretative research philosophy has been
employed. This is associated with the development of
inferential analysis reflecting towards the evaluation of the
research surveys. In addition, the use of this approach enables
the optimum integration of the associated conceptual
frameworks with the findings of the study.
3.4 Ethical Considerations
The foremost ethical consideration associated with this
study is related to the consent of the participants of the primary
study surveys. In this respect, the participants of the survey
were provided with the ethics consent form (Bryman, 2015).
This contained the relevant consent of the participants for
providing their respective inputs for the study survey.
Furthermore, regarding the use of secondary data from the past
literature, the various sources being used in this study have
been accurately cited and referred to in order to conform with
the associated legal and ethical considerations.
CHAPTER FOUR: FINDINGS AND DISCUSSION4.1
Introduction

This chapter will enumerate the various findings of the
study acquired through the primary research surveys. In
addition, the respective findings of the study will be assessed
and appraised through the integration and support from the
related secondary research studies in order to attain
authentication of the related research surveys. The various
findings of the study have been graphically represented against
the respective literature to support the relevant findings of the
study.
4.2 Findings and Discussions
The following section will include the various survey
findings developed through the statistical tabulation of the
various answers gleaned through the questionnaire surveys. This
will be discussed through the integration of the relevant
literature associated with the study findings.
The foremost query asked of the participants was regarding the
perception related to the role of business intelligence in the
management of the projects. In this respect, majority of the
participants answered that they agree to the notion. Hence, it is
established that business intelligence tends to play an integral
role in the management of the projects. For a single project, it is
generally relatively independent of other projects, with only a
few minor interfaces to other projects. For business intelligence
projects, each sub-project is closely linked, and the failure of
any one project will affect other sub-projects, which may lead
to the failure of the entire project. The more sub-projects, the
more complex the management interface, so handling the
management interface between sub-projects is the key to the
success of business intelligence projects (Elbashir et al., 2008;
Williams & Williams, 2010).
The next question asked of the participants was regarding the
use of business intelligence for the development of competitive
edge for the respective projects under consideration. In this

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