Your Data Architecture Holds the Key to Unlocking AI’s Full Potential
In
the
words
of
J.R.R.
Tolkien,
“shortcuts
make
long
delays.”
I
get
it,
we
live
in
an
age
of
instant
gratification,
with
Doordash
and
Grubhub
meals
on-demand,
fast-paced
social
media
and
same-day
Amazon
Prime
deliveries.
In
the
words
of
J.R.R.
Tolkien,
“shortcuts
make
long
delays.”
I
get
it,
we
live
in
an
age
of
instant
gratification,
with
Doordash
and
Grubhub
meals
on-demand,
fast-paced
social
media
and
same-day
Amazon
Prime
deliveries.
But
I’ve
learned
that
in
some
cases,
shortcuts
are
just
not
possible.
Such
is
the
case
with
comprehensive
AI
implementations;
you
cannot
shortcut
success.
Operationalizing
AI
at
scale
mandates
that
your
full
suite
of
data–structured,
unstructured
and
semi-structured
get
organized
and
architected
in
a
way
that
makes
it
useable,
readily
accessible
and
secure.
Fortunately,
the
journey
to
AI
is
one
that
is
more
than
worth
the
time
and
effort.
AI
Potential:
Powering
Our
World
and
Your
Business
That’s
because
AI
promises
to
be
one
of
the
most
transformational
technologies
of
our
time.
Already,
we
see
its
impact
across
industries
and
applications.
If
you’ve
experienced
any
of
these,
then
you’re
seeing
AI
in
action:
-
Automated
assistants
such
as
Amazon
Alexa,
Microsoft
Cortana
and
Google
Assistant. -
COVID
vaccines
and/or
personalized
medicine
used
to
treat
an
illness
or
disease. -
Smart
cars
that
alert
drivers
like
you,
help
you
park
and
ping
you
when
it’s
time
for
maintenance. -
Shopping preferences
that
are
tailored
to
your
specific
tastes
and
proactively
sent
to
you.
Despite
these
AI-powered
examples,
businesses
have
only
just
begun
to
embrace
AI,
with
an
estimated
12%
fully
using
AI
technology.1
But
this
is
changing
rapidly.
And
that’s
because
AI
holds
massive
potential.
In
one
Forrester
study
and
financial
analysis,
it
was
found
that
AI-enabled
organizations
can
gain
an
ROI
of
183%
over
three
years. 2
That’s
why
AI
is
a
key
determinant
of
your
future
success.
Businesses
that
lead
in
fully
deploying
AI
will
be
able
to
optimize
customer
experiences
and
efficiencies
that
help
maximize
customer
retention
and
customer
acquisition
and
gain
a
distinct
advantage
over
the
competition.
The
growing
divide
between
AI
haves
and
have-nots
is
underway
and
at
a
certain
point,
that
chasm
will
not
be
crossable.
For
example,
today
airports
can
use
AI
to
keep
passengers
and
employees
safer.
AI
working
on
top
of
a data
lakehouse,
can
help
to
quickly
correlate
passenger
and
security
data,
enabling
real-time
threat
analysis
and
advanced
threat
detection.
In
order
to
move
AI
forward,
we
need
to
first
build
and
fortify
the
foundational
layer:
data
architecture.
This
architecture
is
important
because,
to
reap
the
full
benefits
of
AI,
it
must
be
built
to
scale
across
an
enterprise
versus
individual
AI
applications.
Constructing
the
right
data
architecture
cannot
be
bypassed.
That’s
because
several
impeding
factors
are
currently
in
play
that
must
be
resolved.
All
organizations
need
an
optimized,
future-proofed
data
architecture
to
move
AI
forward.
Complexity
slows
innovation
Data
growth
is
skyrocketing.
One
estimate3
states
that
by
2024,
149
zettabytes
will
be
created
every
day:
that’s
1.7
MB
every
second.
A
zettabyte
has
21
zeroes.
What
does
that
mean? According
to
the World
Economic
Forum4,
“At
the
beginning
of
2020,
the
number
of
bytes
in
the
digital
universe
was
40
times
bigger
than
the
number
of
stars
in
the
observable
universe.”
Dell
Data’s
size
alone
creates
inherent
complexity.
Layered
on
top
of
that
are
the
different
types
of
data
stored
in
various
siloes
and
locations
throughout
an
organization.
It
all
adds
up
to
a
“perfect
storm”
of
complexity.
A
complex
data
landscape
prevents
data
scientists
and
data
engineers
from
easily
linking
the
right
data
together
at
the
right
time.
Additionally,
multiple
systems
of
record
create
a
confusing
environment
when
those
sources
do
not
report
the
same
answers.
Extracting
value
from
data
Highly
skilled
data
scientists,
analysts
and
other
users
grapple
with
gaining
ready
access
to
data.
This
has
become
a
bottleneck,
hindering
richer
and
real-time
insights.
For
AI
success,
data
scientists,
analysts
and
other
users
need
fast,
concurrent
access
to
data
from
all
areas
of
the
business.
Securing
data
as
it
grows
Securing
mission-critical
infrastructure,
across
all
data
in
an
enterprise,
is
a
default
task
for
every
organization. However,
as
data
grows
within
an
enterprise,
more
desire
for
access
and
use
of
that
data
produces
an
increasing
amount
of
vulnerable
security
end
points.
Catalyzing
AI
at
Scale
with
Data
Lakehouse
The
good
news
is
that
data
architectures
are
evolving
to
solve
these
challenges
and
fully
enable
AI
deployments
at
scale.
Let’s
look
at
the
data
architecture
journey
to
understand
why
and
how
data
lakehouses
help
to
solve
complexity,
value
and
security.
Traditionally,
data
warehouses
have
stored
curated,
structured
data
to
support
analytics
and
business
intelligence,
with
fast,
easy
access
to
data.
Data
warehouses,
however,
were
not
designed
to
support
the
demands
of
AI
or
semi-structured
and
unstructured
data
sources.
Data
lakes
emerged
to
help
solve
complex
data
organizational
challenges
and
store
data
in
its
natural
format.
Used
in
tandem
with
data
warehouses,
data
lakes,
while
helpful,
simultaneously
create
more
data
silos
and
increase
cost.5
Today,
the
ideal
solution
is
a
data
lakehouse,
which
combines
the
benefits
of
data
warehouses
and
data
lakes.
A
data
lakehouse
handles
all
types
of
data
via
a
single
repository,
eliminating
the
need
for
separate
systems.
This
unification
of
access
through
the
lakehouse
removes
multiple
areas
of
ingress/egress
and
simplifies
security
and
management
achieving
both
value
extraction
and
security.
Data
lakehouses
support
AI
and
real-time
data
applications
with
streamlined,
fast
and
effective
access
to
data.
The
benefits
of
a
data
lakehouse
address
complexity,
value
and
security:
-
Create
more
value
quickly
and
efficiently
from
all
data
sources -
Simplify
the
data
landscape
via
carefully
engineered
design
features -
Secure
data
and
ensure
data
availability
at
the
right
time
for
the
right
requirements
For
example,
pharmacies
can
use
a
data
lakehouse
to
help
patients.
By
quickly
matching
drug
availability with
patient
demand,
pharmacies
can
ensure
the
right
medication
is
at
the
right
pharmacy
for
the
correct
patient.
Moving
AI
Forward
AI
deployments
at
scale
will
change
the
trajectory
of
success
around
the
world
and
across
industries,
company
types
and
sizes.
But
first
things
first
mandate
that
the
right
data
architecture
be
put
in
place
to
fully
enable
AI.
While
data
lake
solutions
help
accelerate
this
process,
the
right
architecture
cannot
be
bypassed.
As
J.R.R.
Tolkien
intimated,
anything
worth
achieving
takes
time.
Want
to
learn
more? Read
this ESG
paper.
*************
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