Data virtualization unifies data for seamless AI and analytics

Data
integration
stands
as
a
critical
first
step
in
constructing
any
artificial
intelligence
(AI)
application.
While
various
methods
exist
for
starting
this
process,
organizations
accelerate
the
application
development
and
deployment
process
through
data
virtualization.

Data
virtualization
empowers
businesses
to
unlock
the
hidden
potential
of
their
data,
delivering
real-time
AI
insights
for
cutting-edge
applications
like
predictive
maintenance,
fraud
detection
and
demand
forecasting.

Despite
heavy
investments
in
databases
and
technology,
many
companies
struggle
to
extract
further
value
from
their
data.
Data
virtualization
bridges
this
gap,
allowing
organizations
to
use
their
existing
data
sources
with
flexibility
and
efficiency
for
AI
and
analytics
initiatives.

Virtualizing
data
acts
as
a
bridge,
enabling
the
platform
to
access
and
display
data
from
external
source
systems
on
demand.
This
innovative
approach
centralizes
and
streamlines
data
management
without
requiring
physical
storage
on
the
platform
itself.
A
virtual
layer
establishes
itself
between
data
sources
and
users,
enabling
organizations
to
access
and
manage
their
data
without
replication
or
movement
from
its
original
location.

Why
choose
data
virtualization?

• Data
  virtualization
  streamlines
  the
  merging
  of
  data
  from
  diverse
  sources
  by
  eliminating
  the
  need
  for
  physical
  movement
  or
  duplication.
  This
  significantly
  reduces
  data
  integration
  time
  and
  expense,
  while
  also
  minimizing
  the
  potential
  for
  inaccuracies
  or
  data
  loss.

• Organizations
  can
  achieve
  a
  centralized
  perspective
  of
  their
  data,
  regardless
  of
  its
  storage
  source.
  This
  serves
  as
  a
  single
  point
  of
  reference
  for
  analytics,
  reporting
  and
  data-based
  decisions,
  resulting
  in
  increased
  accuracy
  and
  quicker
  generation
  of
  valuable
  insights.

• Organizations
  gain
  the
  ability
  to
  effortlessly
  modify
  and
  scale
  their
  data
  in
  response
  to
  shifting
  business
  demands,
  leading
  to
  greater
  agility
  and
  adaptability.

Breaking
down
data
silos:
Fueling
machine
learning
success
with
data
virtualization

AI
has
significantly
transformed
large
companies,
reshaping
business
operations
and
decision-making
processes
through advanced
analytics
solutions.
This
transformation
heavily
relies
on
data
virtualization,
which
serves
as
a
central
hub,
connecting
real-time
data
streams
from
various
sources,
such
as
sensor
data
and
equipment
logs,
and
eliminating
data
silos
and
fragmentation. 

Data
virtualization
not
only
integrates
real-time
data
but
also
historical
data
from
comprehensive
software
suites
used
for
various
functions,
such
as enterprise
resource
planning
or
customer
relationship
management.
This
historical
data
provides
valuable
insights
into
areas
like
maintenance
schedules,
asset
performance
or
customer
behavior,
depending
on
the
suite.

By
combining
real-time
and
historical
data
from
diverse
sources,
data
virtualization
creates
a
comprehensive
and
unified
view
of
an
organization’s
entire
operational
data
ecosystem. This
holistic
view
empowers
businesses
to
make
data-driven
decisions,
optimize
processes
and
gain
a
competitive
edge.

With
the
rise
of
generative
AI
chatbots,
foundation
models
now
use
this
rich
data
set.
These
algorithms
actively
sift
through
the
data
to
uncover
hidden
patterns,
trends
and
correlations,
providing
valuable
insights
that
enable
advanced
analytics
to
predict
a
range
of
outcomes.
These
predictions
can
identify
potential
business
opportunities
like
market
shifts
and
customer
needs,
proactively
detect
and
prevent
system
issues
and
failures,
and
optimize
maintenance
schedules
for
maximum
uptime
and
efficiency.

Design
considerations
for
virtualized
data
platforms

 1.
Latency
and
real-time
analysis

Challenge: 

Accessing
stored
data
directly
typically
incurs
less
latency
compared
to
virtualized
data
retrieval,
which
can
impede
real-time
predictive
maintenance
analyses,
where
timely
insights
are
crucial.

Design
considerations:

We
need
a
two-pronged
approach
to
ensure
real-time
insights
and
minimize
delays
in
accessing
virtualized
data.
First,
we’ll
analyze
the
network
infrastructure
and
optimize
data
transfer
protocols.
This
can
involve
techniques
like
network
segmentation
to
reduce
congestion
or
using
faster
protocols
like
UDP
for
certain
data
types.
By
optimizing
data
transfer,
we
decrease
the
time
it
takes
to
retrieve
the
information
you
need.
Second,
we’ll
implement
data
refresh
strategies
to
maintain
a
reasonably
up-to-date
dataset
for
analysis.
This
might
involve
using
batch
jobs
to
perform
incremental
data
updates
at
regular
intervals,
balancing
the
update
frequency
with
the
resources
required.
Striking
this
balance
is
crucial:
too
frequent
updates
can
strain
resources,
while
infrequent
updates
can
lead
to
outdated
data
and
inaccurate
predictions.
By
combining
these
strategies,
we
can
achieve
both
minimal
latency
and
a
fresh
data
set
for
optimal
analysis.

2.
Balancing
update
frequency
and
source
system
strain

Challenge: 

Continuously
querying
virtualized
data
for
real-time
insights
can
overload
the
source
systems,
impacting
their
performance.
This
poses
a
critical
concern
for
predictive
analysis
or
AI,
which
depends
on
frequent
data
updates.

Design
considerations:

To
optimize
query
frequency
for
your
predictive
analysis
and
reporting,
need
to
carefully
design
how
it
accesses
data.
This
includes
focusing
on
retrieving
only
critical
data
points
and
potentially
using
data
replication
tools
for
real-time
access
from
multiple
sources.
Additionally,
consider
scheduling
or
batching
data
retrievals
for
specific
crucial
points
instead
of
constant
querying,
reducing
strain
on
data
systems
and
improving
overall
model
performance.

3.
Virtualization
layer
abstraction
and
developer
benefits

Advantage: 

The
virtualization
layer
in
the
data
platform
acts
as
an
abstraction
layer.
This
means
developers
building
AI/ML
or
data
mining
applications
for
business
once
the
abstraction
layer
is
ready
without
worrying
about
where
the
data
is
physically
stored
or
its
specific
storage
details.
They
can
focus
on
designing
the
core
logic
of
their
models
without
getting
bogged
down
in
data
management
complexities.
This
leads
to
faster
development
cycles
and
quicker
deployment
of
these
applications.

Benefits
for
developers:

By
utilizing
an
abstraction
layer,
developers
working
on
data
analytics
can
focus
on
the
core
logic
of
their
models.
This
layer
acts
as
a
shield,
hiding
the
complexities
of
data
storage
management. 
This
translates
to
faster
development
times
as
developers
don’t
need
to
get
bogged
down
in
data
intricacies,
ultimately
leading
to
quicker
deployment
of
the
predictive
maintenance
models.

4.
Storage
optimization
considerations

Storage
optimization
techniques
like
normalization
or
denormalization
might
not
directly
apply
to
all
functions
of
a
specific
data
analysis
application,
but
they
play
a
significant
role
when
adopting
a
hybrid
approach.
This
approach
involves
integrating
both
ingested
data
and
data
accessed
through
virtualization
within
the
chosen
platform.

Assessing
the
tradeoffs
between
these
techniques
helps
ensure
optimal
storage
usage
for
both
ingested
and
virtualized
data
sets.
These
design
considerations
are
crucial
for
building
effective
ML
solutions
using
virtualized
data
on
the
data
platform.

Data
virtualization:
A
strategic
powerhouse
for
modern
applications

Data
virtualization
has
evolved
beyond
mere
innovation.
It
serves
as
a
strategic
tool
for
enhancing
the
capabilities
of
various
applications.
A
prime
example
is
a
data
virtualization
platform.
This
platform
facilitates
the
development
of
a
wide
range
of
applications
by
using
data
virtualization,
thereby
significantly
improving
their
efficiency,
adaptability
and
capacity
to
deliver
near
real-time
insights.

Let’s
explore
some
compelling
use
cases
that
showcase
the
transformative
power
of
data
virtualization.

1.
Optimizing
supply
chains
for
a
globalized
world

In
today’s
interconnected
global
economy,
vast
networks
with
complex
dependencies
characterize
supply
chains.
Data
virtualization
streamlines
these
intricate
systems
crucially.
A
data
virtualization
platform
unifies
data
from
numerous
sources,
including
production
metrics,
logistics
tracking
details
and
market
trend
data.
This
comprehensive
view
empowers
businesses,
offering
a
complete
picture
of
their
entire
supply
chain
operations.

Imagine
having
unimpeded
visibility
across
all
aspects.
You
can
proactively
identify
potential
bottlenecks,
optimize
logistics
processes
and
adapt
to
shifting
market
dynamics
in
real
time.
The
result
is
an
optimized
and
agile
value
chain
delivering
significant
competitive
advantages.

2.
Deep
dive
into
customer
behavior:
Customer
analytics

The
digital
revolution
has
rendered
understanding
your
customers
critical
for
business
success.
A
data
virtualization
platform
breaks
down
data
silos
by
using
data
virtualization.
It
seamlessly
integrates
customer
data
from
various
touchpoints,
such
as
sales
records,
customer
service
interactions
and
marketing
campaign
performance
metrics.
This
unified
data
landscape
fosters
a
comprehensive
understanding
of
customer
behavior
patterns
and
preferences.

Armed
with
these
profound
customer
insights,
businesses
can
create
highly
personalized
experiences,
target
promotions
and
innovate
products
that
resonate
more
effectively
with
their
target
audience.
This
data-driven
approach
promotes
customer
satisfaction
and
cultivates
enduring
loyalty,
a
key
element
for
thriving
in
today’s
competitive
environment.

3.
Proactive
fraud
detection
in
the
digital
age

Financial
fraud
constantly
evolves,
presenting
a
challenging
detection
task
addressed
proactively
by
data
virtualization
platforms.
The
platform
identifies
potential
fraud
attempts
in
real
time
by
virtualizing
and
analyzing
data
from
various
sources,
such
as
transaction
logs,
user
behavior
patterns
and
demographic
details.
This
approach
not
only
protects
businesses
from
financial
losses
but
also
fosters
trust
with
their
customer
base,
a
crucial
asset
in
today’s
digital
age.

The
transformative
potential
of
data
virtualization
is
exemplified
by
these
impactful
applications.

IBM
Cloud
Pak®
for
Data
platform
and

IBM
watsonx
empowers
businesses
to
unlock
the
full
power
of
their
data,
driving
innovation
and
gaining
a
significant
competitive
edge
across
diverse
industries.
IBM
also
offers

IBM
Data
Virtualization
as
a
common
query
engine
and

IBM
Knowledge
Catalog
for
data
governance.

We
are
here
to
help
you
at
every
step
of
your
data
virtualization
journey.

Predict
outcomes
faster
by
using
a
platform
built
with
a
data
fabric
architecture