BIG DATA-DRIVEN DECISION MAKING IN PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS
Md Atiqur Rahaman,
No information about this author
Farhana Zaman Rozony,
No information about this author
Md Samiul Alam Mazumder
No information about this author
et al.
Academic journal on science, technology, engineering & mathematics education.,
Journal Year:
2024,
Volume and Issue:
4(3), P. 44 - 62
Published: July 24, 2024
This
study
investigates
the
impact
of
big
data-driven
decision-making
in
construction
project
management
through
a
qualitative
comparative
analysis.
By
conducting
semi-structured
interviews
with
managers,
data
analysts,
and
workers
across
various
types
projects,
research
identifies
key
themes
related
to
benefits
challenges
integrating
analytics.
The
findings
highlight
significant
advantages
such
as
enhanced
operational
efficiency,
improved
processes,
cost
reduction,
budget
management,
timely
delivery,
quality
control
assurance.
However,
including
integration
complexities,
privacy
concerns,
need
for
specialized
skills,
organizational
resistance
change
are
also
revealed.
underscores
importance
fostering
culture
strong
leadership
support
maximize
while
emphasizing
context-specific
strategies
tailored
different
types.
Language: Английский
A REVIEW OF IMPLEMENTING AI-POWERED DATA WAREHOUSE SOLUTIONS TO OPTIMIZE BIG DATA MANAGEMENT AND UTILIZATION
Md Kazi Shahab Uddin,
No information about this author
Kazi Md Riaz Hossan
No information about this author
Academic journal on business administration, innovation & sustainability.,
Journal Year:
2024,
Volume and Issue:
4(3), P. 66 - 78
Published: July 28, 2024
This
review
examines
the
implementation
of
AI-powered
data
warehouse
solutions
to
optimize
big
management
and
utilization,
analyzing
25
peer-reviewed
articles
published
over
last
decade.
As
organizations
increasingly
rely
on
vast
amounts
for
strategic
decision-making,
traditional
warehousing
techniques
have
struggled
keep
pace
with
volume,
variety,
velocity
modern
data.
The
integration
artificial
intelligence
(AI)
into
processes
has
emerged
as
a
critical
advancement,
enhancing
processing
efficiency,
accuracy,
scalability.
study
synthesizes
findings
from
literature
highlight
key
benefits
such
automated
extraction,
transformation,
loading
(ETL)
processes,
real-time
analytics,
improved
quality
through
advanced
cleansing
anomaly
detection.
Additionally,
it
identifies
significant
challenges
including
security
risks,
complexities,
need
specialized
skills
substantial
investments.
concludes
recommendations
future
research
practical
applications,
emphasizing
importance
planning
robust
measures
fully
leverage
AI's
potential
in
revolutionizing
warehousing.
Language: Английский
A REVIEW OF UTILIZING NATURAL LANGUAGE PROCESSING AND AI FOR ADVANCED DATA VISUALIZATION IN REAL-TIME ANALYTICS
Md Kazi Shahab Uddin
No information about this author
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
1(4), P. 34 - 49
Published: July 28, 2024
This
review
explores
the
integration
of
Natural
Language
Processing
(NLP)
and
Artificial
Intelligence
(AI)
in
enhancing
data
visualization
for
real-time
analytics.
In
an
era
characterized
by
exponential
growth
data,
traditional
static
visualizations
are
increasingly
inadequate
meeting
demands
decision-making.
NLP
AI
offer
sophisticated
tools
to
dynamically
interpret
visualize
turning
vast
amounts
raw
information
into
actionable
insights
across
various
domains.
paper
synthesizes
current
research,
methodologies,
applications
visualization,
highlighting
key
advancements
such
as
enhanced
interpretability,
processing
capabilities,
improved
user
interaction
through
natural
language
queries
interactive
elements.
It
also
addresses
challenges
limitations
associated
with
implementing
these
technologies,
including
computational
complexity,
quality
issues,
ethical
considerations.
The
identifies
significant
trends
future
directions,
augmented
virtual
reality
(AR/VR)
use
generative
models,
which
promise
further
advance
field.
By
providing
a
comprehensive
overview
state
this
aims
inform
guide
research
development
efforts
leveraging
technologies
more
effective
efficient
data-driven
Language: Английский
CYBERSECURITY SOLUTIONS AND PRACTICES: FIREWALLS, INTRUSION DETECTION/PREVENTION, ENCRYPTION, MULTI-FACTOR AUTHENTICATION
Ms Roopesh
No information about this author
Academic journal on business administration, innovation & sustainability.,
Journal Year:
2024,
Volume and Issue:
4(3), P. 37 - 52
Published: July 25, 2024
In
today's
digitally
interconnected
world,
cybersecurity
is
paramount
for
protecting
sensitive
information
from
sophisticated
threats.
This
literature
review
examines
four
key
solutions—firewalls,
intrusion
detection
and
prevention
systems
(IDPS),
encryption,
multi-factor
authentication
(MFA)—highlighting
their
roles,
advancements,
challenges
based
on
105
articles.
Firewalls
(n=35),
including
packet-filtering,
stateful
inspection,
proxy,
next-generation
firewalls
(NGFWs),
act
as
barriers
controlling
network
traffic.
NGFWs
integrate
deep
packet
inspection
application
awareness,
enhancing
security
despite
complex
maintenance
issues.
IDPS
technologies
(n=30)
have
evolved
anomaly
to
AI-integrated
systems,
improving
threat
while
facing
false-positive
rates
zero-day
exploit
challenges.
Encryption
(n=25)
ensures
data
confidentiality,
progressing
basic
ciphers
algorithms
like
AES
post-quantum
cryptography,
though
it
grapples
with
computational
management
complexities.
MFA
(n=15)
enhances
through
multiple
verification
factors,
evolving
passwords
biometrics
behavioral
analytics,
yet
faces
user
inconvenience
potential
bypass
methods.
A
comparative
analysis
reveals
that
effectively
prevent
detect
threats
but
require
meticulous
management;
encryption
demands
efficient
strengthens
may
encounter
resistance.
Integrating
these
solutions
within
a
layered
framework
provides
comprehensive
protection,
leveraging
strengths
resilient
posture.
Case
studies
affirm
multi-layered
approaches
reduce
breaches,
underscoring
the
effectiveness
of
integrated
practices.
Continuous
innovation,
education,
adaptive
are
vital
addressing
dynamic
cyber
threats,
reinforcing
need
robust,
multi-faceted
strategy.
Language: Английский
https://allacademicresearch.com/index.php/AJSTEME/article/view/89
Janifer Nahar,
No information about this author
Nusrat Jahan,
No information about this author
Sadia Afrin Shorna
No information about this author
et al.
Academic journal on science, technology, engineering & mathematics education.,
Journal Year:
2024,
Volume and Issue:
4(3), P. 63 - 74
Published: July 24, 2024
The
integration
of
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML)
in
the
financial
sector
has
brought
about
a
profound
transformation
decision-making
processes,
risk
management,
predictive
analytics.
This
comprehensive
study
aims
to
systematically
identify
analyze
foundational
theories,
emerging
themes,
research
clusters
within
extensive
body
AI
ML
finance
literature
through
an
in-depth
bibliometric
analysis.
By
meticulously
examining
vast
array
publications
spanning
over
two
decades,
uncovers
intricate
evolution
applications
finance,
mapping
out
key
areas
providing
valuable
insights
into
future
directions.
findings
reveal
significant
accelerating
growth
application
across
various
domains,
notably
fraud
detection,
portfolio
algorithmic
trading,
demonstrating
substantial
impact
transformative
potential
these
technologies.
not
only
charts
current
landscape
but
also
identifies
critical
gaps
opportunities
for
exploration,
underscoring
ongoing
maturation
this
dynamic
field.
Language: Английский
BIG DATA ANALYTICS FOR ENHANCED BUSINESS INTELLIGENCE IN FORTUNE 1000 COMPANIES: STRATEGIES, CHALLENGES, AND OUTCOMES
Md Rasel Ul Alam,
No information about this author
Sk Abdur Rahim Shabbir
No information about this author
Academic journal on business administration, innovation & sustainability.,
Journal Year:
2024,
Volume and Issue:
4(3), P. 53 - 65
Published: July 25, 2024
This
study
investigates
the
transformative
impact
of
big
data
analytics
and
business
intelligence
on
operations
strategic
decision-making
Fortune
1000
companies,
with
a
focus
Walmart.
Walmart's
integration
advanced
tools
has
enabled
significant
optimization
across
various
areas,
including
inventory
management,
customer
engagement,
supply
chain
operations.
Leveraging
data,
Walmart
gained
deep
insights
into
behavior,
allowing
for
accurate
demand
forecasting
streamlined
operations,
which
enhance
operational
efficiency
competitive
advantage.
The
highlights
use
predictive
to
improve
management
efficiency,
demonstrating
how
analyzing
purchasing
patterns
preferences
reduces
stockouts
excess
inventory,
thus
boosting
satisfaction
minimizing
costs.
Despite
its
infrastructure,
faces
challenges
in
real-time
due
silos
created
by
vast
Enhancing
governance
practices
is
crucial
ensure
quality,
security,
compliance.
Additionally,
examines
dynamic
pricing
algorithms
adjust
prices
based
market
conditions,
effectively
maximizing
sales
profitability,
aligning
previous
research
benefits
retail.
Furthermore,
broader
economic
implications
data-driven
strategies
are
discussed,
noting
that
while
efficient
lower
benefit
consumers,
they
also
pose
small
local
businesses.
provides
detailed
analysis
leverage
sustain
advantage
drive
success,
offering
valuable
other
companies
importance
technology,
organizational
culture,
achieving
sustained
success.
Language: Английский