Straumann Group is transforming dentistry with data, AI
Straumann
Group’s
Sridhar
Iyengar
has
a
bold
mission:
To
transform
the
nearly
70-year-old
company’s
data
and
technology
organization
into
a
data-as-a-service
provider
for
the
global
manufacturer
and
supplier
of
dental
implants,
prosthetics,
orthodontics,
Straumann
Group’s
Sridhar
Iyengar
has
a
bold
mission:
To
transform
the
nearly
70-year-old
company’s
data
and
technology
organization
into
a
data-as-a-service
provider
for
the
global
manufacturer
and
supplier
of
dental
implants,
prosthetics,
orthodontics,
and
digital
dentistry
—
and
to
provide
business
stakeholders
machine
learning
(ML)
as
a
service
as
well.
“My
vision
is
that
I
can
give
the
keys
to
my
businesses
to
manage
their
data
and
run
their
data
on
their
own,
as
opposed
to
the
Data
&
Tech
team
being
at
the
center
and
helping
them
out,”
says
Iyengar,
director
of
Data
&
Tech
at
Straumann
Group
North
America.
Doing
so
will
be
no
small
feat.
The
Basel,
Switzerland-based
company,
which
operates
in
more
than
100
countries,
has
petabytes
of
data,
including
highly
structured
customer
data,
data
about
treatments
and
lab
requests,
operational
data,
and
a
massive,
growing
volume
of
unstructured
data,
particularly
imaging
data.
The
company’s
orthodontics
business,
for
instance,
makes
heavy
use
of
image
processing
to
the
point
that
unstructured
data
is
growing
at
a
pace
of
roughly
20%
to
25%
per
month.
Advances
in
imaging
technology
present
Straumann
Group
with
the
opportunity
to
provide
its
customers
with
new
capabilities
to
offer
their
clients.
For
example,
imaging
data
can
be
used
to
show
patients
how
an
aligner
will
change
their
appearance
over
time.
“It
gives
a
lot
of
power
to
our
providers
in
selling
their
services
and
at
the
same
time
gets
more
NPS
[net
promoter
score]
for
us
from
the
patient,”
says
Iyengar,
who
believes
AI
will
play
a
critical
role
in
Straumann’s
image
processing
and
lab
treatments
businesses.
Hence
the
drive
to
provide
ML
as
a
service
to
the
Data
&
Tech
team’s
internal
customers.
“All
they
would
have
to
do
is
just
build
their
model
and
run
with
it,”
he
says.
But
to
augment
its
various
businesses
with
ML
and
AI,
Iyengar’s
team
first
had
to
break
down
data
silos
within
the
organization
and
transform
the
company’s
data
operations.
“Digitizing
was
our
first
stake
at
the
table
in
our
data
journey,”
he
says.
Selling
the
value
of
data
transformation
Iyengar
and
his
team
are
18
months
into
a
three-
to
five-year
journey
that
started
by
building
out
the
data
layer
—
corralling
data
sources
such
as
ERP,
CRM,
and
legacy
databases
into
data
warehouses
for
structured
data
and
data
lakes
for
unstructured
data.
That
step,
primarily
undertaken
by
developers
and
data
architects,
established
data
governance
and
data
integration.
Now,
the
team’s
information
architects,
in
conjunction
with
business
analysts,
are
working
on
the
semantic
layer,
which
feeds
data
from
data
warehouses
and
data
lakes
into
data
marts,
including
a
finance
mart,
sales
mart,
supply
chain
mart,
and
market
mart.
The
next
goal,
with
the
aid
of
partner
Findability
Sciences,
will
be
to
build
out
ML
and
AI
pipelines
into
an
information
delivery
layer
that
can
support
predictive
and
prescriptive
analytics.
“As
the
information
layer
gets
mature,
that’s
where
the
ML
and
the
AI
will
start
seeing
some
green
shoots,”
he
says,
adding
that
although
data
transformation
was
a
pressing
need
when
he
signed
on
in
2021,
he
wanted
a
more
compelling
vision
to
sell
the
board
and
business
leaders
on
tackling
it.
For
that,
he
relied
on
a
defensive
and
offensive
metaphor
for
his
data
strategy.
The
defensive
side
includes
traditional
elements
of
data
management,
such
as
data
governance
and
data
quality.
The
offensive
side?
That
is
the
domain
of
AI
and
advanced
analytics
that
serve
a
role
beyond
just
insight
and
business
optimization.
“The
offensive
side
is
how
to
generate
revenue,
all
of
the
insights
from
the
historical
data
that
we
have
collected
and,
in
fact,
forecast
the
trends
that
are
coming,”
Iyengar
says.
“Most
of
the
data
that
we
get
on
the
offensive
side
are
unstructured,
and
we
want
to
make
sure
that
it
makes
sense
to
the
business
leaders
and
help
them
harmonize
and
enrich
it
in
such
a
manner
that
they
can
serve
their
customers
more
efficiently
and
that
the
customers
get
served
and
leverage
Straumann’s
services
in
a
much
more
robust,
frictionless
manner.”
Not
surprisingly,
it
was
this
offensive
side
that
got
Straumann’s
board
invested
in
Iyengar’s
plan
for
transformation.
“When
the
customer-centricity
and
the
digital
transformation
piece
was
proposed
—
along
with
data
transformation
—
I
think
that
resonated
with
them,”
Iyengar
says.
Skilling
up
for
the
future
Iyengar’s
team
found
success
by
adopting
a
use-case
approach,
not
unlike
that
of
one
of
Strauman’s
core
businesses.
“We
pretty
much
took
the
same
principle
of
the
pre-treatment
and
the
post-treatment
images
that
we
show
to
our
patients,”
Iyengar
says.
The
team
asked
company
leaders
to
pick
a
number
of
customer-centric
vectors
to
illustrate
how
data
innovations
could
be
used
to
drive
business
outcomes.
One
of
the
targets
was
driving
down
customer
churn.
The
team
started
by
splitting
churn
propensity
into
two
values:
one
for
retention
of
existing
customers
and
one
for
new
customer
acquisition.
It
used
typical
customer
lifetime
values
and
analyzed
buying
patterns
to
provide
the
marketing
team
and
sales
team
with
insights
they
could
use
to
drive
their
strategies.
Iyengar
says
adopting
this
approach
to
selling
digital
transformation
internally
has
made
the
job
much
easier.
“We
are
seeing
a
lot
of
investments
being
approved
from
all
the
businesses
in
order
to
support
that
initiative,”
he
says.
In
the
meantime,
as
the
team
begins
to
build
out
ML
and
AI
capabilities,
it
is
also
imperative
to
transform
the
Data
&
Tech
team
itself.
“The
skill
set
that
we
have
inherently
from
our
traditional
school
point
of
view
doesn’t
suit
the
ML
and
AI
part
of
it,”
Iyengar
says.
“What
you
need
there
is
statisticians
and
mathematicians,
not
programmers
and
coders,
right?
So,
we
have
been
transforming
ourselves
as
well,
culturally
and
from
a
skill
point
of
view.
That
takes
its
own
time.
We
have
a
learning
curve
at
our
end
to
build
the
right
skill
set
within
us.”
Iyengar
is
supplementing
his
team’s
skill
set
with
help
from
enterprise
AI
specialist
Findability
Sciences.
The
company’s
Findability.ai
platform
combines
machine
learning,
computer
vision,
and
natural
language
processing
(NLP)
to
aid
customers
in
their
AI
journey.
“I
have
a
lot
of
traditional
ETL
skills
in
my
team,”
he
says.
“What
I
don’t
have
is
the
ML/AI
skill
set
right
now.
Partners
are
helping
us
in
that
space.”
Ultimately,
Iyengar
says,
these
changes
will
transform
how
the
Data
&
Tech
team
interfaces
with
the
business.
For
now,
it
operates
under
a
centralized
“hub
and
spokes”
model.
But
he
says
hiring
statisticians
and
mathematicians
in
his
team
won’t
be
scalable.
Instead,
what
he
really
wants
within
three
to
five
years
is
to
embed
them
in
teams
closer
to
the
lines
of
business,
so
the
businesses
can
run
models
by
themselves.
“Right
now,
we’re
driving
the
bus
at
100
miles
and
hour
and
changing
the
tires
at
the
same
time,
which
is
not
going
to
be
scalable
by
any
means,
though
I’m
proud
of
my
team
that
we
are
doing
it,”
he
says.
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