Product lifecycle management for data-driven organizations 

In
a
world
where
every
company
is
now
a
technology
company,
all
enterprises
must
become
well-versed
in
managing
their
digital
products
to
remain
competitive.
In
other
words,
they
need
a
robust
digital
product
lifecycle
management
(PLM)
strategy.
PLM
delivers
value
by
standardizing
product-related
processes,
from
ideation
to
product
development
to
go-to-market
to
enhancements
and
maintenance.
This
ensures
a
modern
customer
experience.
The
key
foundation
of
a
strong
PLM
strategy
is
healthy
and
orderly
product
data,
but
data
management
is
where
enterprises
struggle
the
most.
To
take
advantage
of
new
technologies
such
as
AI
for
product
innovation,
it
is
crucial
that
enterprises
have
well-organized
and
managed
data
assets.
 

Gartner
has
estimated
that

80%
of
organizations
fail
to
scale
digital
businesses
because
of
outdated
governance
processes.
Data
is
an
asset,
but
to
provide
value,
it
must
be
organized,
standardized
and
governed.
Enterprises
must
invest
in
data
governance
upfront,
as
it
is
challenging,
time-consuming
and
computationally
expensive
to
remedy
vast
amounts
of
unorganized
and
disparate
data
assets.
In
addition
to
providing
data
security,
governance
programs
must
focus
on
organizing
data,
identifying
non-compliance
and
preventing
data
leaks
or
losses.  

In
product-centric
organizations,
a
lack
of
governance
can
lead
to
exacerbated
downstream
effects
in
two
key
scenarios:  

1.
Acquisitions
and
mergers

Consider
this
fictional
example:
A
company
that
sells
three-wheeled
cars
has
created
a
robust
data
model
where
it
is
easy
to
get
to
any
piece
of
data
and
the
format
is
understood
across
the
business.
This
company
is
so
successful
that
it
acquired
another
company
that
also
makes
three-wheeled
cars.
The
new
company’s
data
model
is
completely
different
from
the
original
company.
Companies
commonly
ignore
this
issue
and
allow
the
two
models
to
operate
separately.
Eventually,
the
enterprise
will
have
weaved
a
web
of
misaligned
data
requiring
manual
remediation. 

2.
Siloed
business
units

Now,
imagine
a
company
where
the
order
management
team
owns
order
data
and
the
sales
team
owns
sales
data.
In
addition,
there
is
a
downstream
team
that
owns
product
transactional
data.
When
each
business
unit
or
product
team
manages
their
own
data,
product
data
can
overlap
with
the
other
unit’s
data
causing
several
issues,
such
as
duplication,
manual
remediation,
inconsistent
pricing,
unnecessary
data
storage
and
an
inability
to
use
data
insights.
It
becomes
increasingly
difficult
to
get
information
in
a
timely
fashion
and
inaccuracies
are
bound
to
occur.
Siloed
business
units
hamper
the
leadership’s
ability
to
make
data-driven
decisions.
In
a
well-run
enterprise,
each
team
would
connect
their
data
across
systems
to
enable
unified
product
management
and
data-informed
business
strategy.  

How
to
thrive
in
today’s
digital
landscape

In
order
to
thrive
in
today’s
data-driven
landscape,
organizations
must
proactively
implement
PLM
processes,
embrace
a
unified
data
approach
and
fortify
their
data
governance
structures.
These
strategic
initiatives
not
only
mitigate
risks
but
also
serve
as
catalysts
for
unleashing
the
full
potential
of
AI
technologies.
By
prioritizing
these
solutions,
organizations
can
equip
themselves
to
harness
data
as
the
fuel
for
innovation
and
competitive
advantage.
In
essence,
PLM
processes,
a
unified
data
approach
and
robust
data
governance
emerge
as
the
cornerstone
of
a
forward-thinking
strategy,
empowering
organizations
to
navigate
the
complexities
of
the
AI-driven
world
with
confidence
and
success.

See
how
IBM
can
help
you
set
up
effective
data
management
solutions