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Analytics Y1 2.1 Data Lifecycle Management (DLM) EssentialsVersion en ligne
Explore DLM in enterprise contexts.
)
is
a
that
governs
the
from
its
to
its
,
ensuring
throughout
its
.
In
modern
enterprises
,
the
sheer
volume
and
variety
of
data
demand
governance
with
clear
,
,
and
deletion
rules
.
A
robust
DLM
strategy
combines
governance
policies
,
roles
,
and
technologies
to
manage
metadata
,
access
controls
,
and
data
movement
across
on
-
premises
and
cloud
environments
,
supporting
and
informed
decision
-
making
.
The
Foundational
Pillars
of
DLM
rest
on
the
CIA
triad
of
information
security
.
dictates
that
data
is
protected
from
unauthorized
access
,
disclosure
,
and
theft
,
achieved
through
encryption
at
rest
and
in
transit
,
ACLs
,
and
.
?
categorizing
data
by
its
sensitivity
?
guides
the
appropriate
safeguards
.
ensures
data
remains
accurate
and
consistent
,
using
data
validation
rules
,
checksums
and
hash
functions
,
and
comprehensive
audit
trails
.
An
supports
forensic
analysis
and
compliance
.
guarantees
access
through
redundant
storage
such
as
RAID
arrays
,
geo
-
replicated
cloud
storage
,
and
robust
backup
and
disaster
recovery
plans
.
The
Five
Phases
of
the
DLM
Model
describe
a
sequential
process
.
The
phase
handles
the
generation
and
ingestion
of
data
,
with
data
quality
and
validation
policies
.
In
the
phase
,
data
is
tiered
so
hot
data
sits
on
high
?
performance
storage
while
cold
data
moves
to
options
like
tape
or
S3
Glacier
.
The
phase
governs
processing
,
analysis
,
and
access
control
,
including
RBAC
to
limit
who
can
view
transactions
or
de
?
identified
datasets
,
mindful
of
GDPR
and
HIPAA
.
In
Archival
,
data
is
moved
offline
with
retention
schedules
,
and
finally
ensures
verifiable
deletion
with
an
audit
log
.
The
Role
of
_____1_____
,
_____2_____
,
and
_____3_____
A
successful
DLM
strategy
is
not
a
one
-
time
implementation
but
an
ongoing
,
automated
process
.
?
_____1_____
:
The
entire
DLM
framework
is
underpinned
by
meticulously
defined
policies
.
These
policies
are
not
abstract
guidelines
;
they
are
technical
rules
embedded
in
the
systems
that
manage
data
.
They
dictate
everything
from
how
data
is
classified
upon
creation
to
the
specific
retention
periods
for
different
data
types
.
?
_____2_____
:
Manual
DLM
is
unfeasible
in
an
age
of
exabytes
of
data
.
Automated
DLM
tools
and
platforms
are
essential
.
These
solutions
can
automatically
classify
data
,
apply
retention
policies
,
and
manage
the
migration
of
data
between
storage
tiers
without
human
intervention
.
?
_____4_____
:
DLM
must
be
integrated
with
other
enterprise
systems
,
including
_____3_____
,
security
information
and
event
management
(
SIEM
)
systems
,
and
.
This
ensures
that
DLM
policies
are
enforced
consistently
across
the
entire
IT
infrastructure
and
that
any
deviations
are
immediately
flagged
and
addressed
.
is
a
strategic
discipline
that
governs
data
from
creation
to
deletion
.
It
emphasizes
processes
,
,
integrity
,
and
availability
across
stages
such
as
collection
,
storage
,
processing
,
archiving
,
and
disposal
.
By
defining
clear
governance
rules
and
,
organizations
reduce
risk
,
ensure
,
and
optimize
costs
.
Effective
lifecycle
management
transforms
data
from
a
potential
liability
into
a
valuable
asset
,
enabling
accurate
analytics
,
auditable
records
,
and
timely
access
for
trusted
users
while
maintaining
privacy
and
resilience
against
threats
.
Data
Lifecycle
Management
(
DLM
)
is
a
policy
-
driven
framework
that
governs
the
flow
of
data
from
inception
to
final
disposition
,
ensuring
security
,
_____
,
and
_____
throughout
its
lifespan
.
The
CIA
triad
?
_____
,
_____
,
and
_____
?
forms
the
foundation
:
encryption
at
rest
and
in
transit
protect
data
,
while
robust
ACLs
and
RBAC
control
access
.
ranks
sensitivity
,
and
_____
provide
forensic
evidence
.
DLM
comprises
five
phases
:
Creation
,
Storage
,
Usage
,
Archival
,
and
Destruction
,
each
guided
by
defined
policies
and
automation
.
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