►
From YouTube: Orchestrating Master Data with Prospecta
Description
We live in a complex and chaotic world now. One of the things that we need to do well in this environment is the ability to quickly respond to the changes we see in our markets and make quick decisions.
In order to make these quick decisions, we need high quality data, when we need them, wherever we are and in a manner that we find useful and relevant.
Having clean and high quality data is fundamental. In this session, Sid Bose and Ashish Modi from Prospecta Software share how their SAP Endorsed Business solution MDO FUSE can help SAP Customers clean their data and keep it clean.
#MDO #MDM #MasterDataManagement #MasterDataGovernance #MasterData #DataDrivenDecisionMaking
A
So
yes,
today
we
today
we
will
talk
about
our
speakers
who
we
are
like
myself,
siddharth
and
my
colleague,
ashish
modi
is
joined
from
sydney,
australia
about
overview
who
we
are
what
we
do
ours.
You
know
mdm
as
a
subject
overview
of
the
product
and,
of
course,
how
we
are
entangled
with
sap
right
and
how
is
the
ecospace
all
about
the
data
quality
journey
all
together
like
how
you
start
from
quality,
to
transformation,
to
your
governance,
structure,
different
dedication
models
and,
of
course,
a
quick
flavor
of
the
product
that
how
the
product
looks
like.
A
A
B
A
A
As
I
said,
we
are
close
to
300
people
across
the
globe
and
working
from
india
offices
like
gurgaon,
noida
and,
of
course,
in
bangalore.
We
have
head
of
headquarters
in
sydney
of
course
in
melbourne,
as
well
as
moving
to
uk
and
and
the
u.s
offices
as
well
focus
again
data
automation,
data
quality
data
governance,
the
best
thing
about,
and
thanks
to
sap
we
are
indro's
partner
with
sap.
A
A
As
you
see
like
we
are,
we
are
tightly
coupled
with
sap
being
an
endorsed
app
all
the
data
integration
model,
pre-configured
business,
business
rules
and
the
masters.
What
we
talk
about,
like
you
know,
master
data
happens
with
like
more
of
materials.
Customer
vendors,
coupled
with
sap
s4
as
well
during
the
rest
of
our
journey,
we'll
talk
about
more
on
this,
but
yeah
talking
about
the
magic
quadrant
and
and
more
often
too,
we
will.
We
were
being
recognized
very
well
suited,
apart
from
all
the
mdm
partners,
right
to
more
talk
about
the
industry
trends
altogether.
C
A
Back
if
you
go,
the
master
data
was
more
of
a
you
know.
Overall
layer
it
used
to
be,
but
now
it
is
getting
top
of
the
town
to
every
manufacturing
industry,
because
the
awareness
is
getting
more
into
it
that
how
we
encourage
more
of
mass
data,
how
we
get
into
the
data
quality,
the
data
governance
right,
how
we
automate
our
data,
hey,
look
you!
You
have
a
bad
data,
how
we
can
help
you
out.
A
So
that's
that's
the
that's
the
trend
which
is
going
on
right
now,
of
course,
mdm
mdm
as
a
process
is
not
a
cost
effective
tool.
But
yes,
it
can
very
well
give
you
that
you
know.
That
means
that
kind
of
highlight
that
hey
look.
There
is
some
leakage
which
is
happening
across
your
organization
and
how
we
should
adapt
a
business
development
or-
or
you
can
say,
a
completely
transformation
out
of
it
right
and
that's
that's
how
we
have
that
right.
I
actually.
B
All
right
now,
I
think
so
so
that
I'm
I'm
fine
with
you
know:
we've
covered
we
covered
this
slide.
Well,
yeah
yeah.
I
can
yeah
yeah,
so
I
mean
from
an
industry
trend
and
benefits.
You
know
that's
what
siddharth
was
mentioning.
I
mean
it's
just
an
open
question
at
this
stage.
You
know
I
mean
how
often
are
we
finding
that
there
is
free
text
spending
happening
within
our
company?
B
You
know:
how
often
do
we
hear
about
cataloging
being
too
hard
or
having
duplicate
materials
all
across
you
know
the
the
eco
space
manual,
you
know
requisitioning
or
time
you
know
physical.
You
know
time
spent
on
looking
for
spare
parts
warehousing
materials.
So
all
of
these
are
the
cost
generators
that
we
see
from
you
know.
Industry
trend
perspective,
the
the
benefits
definitely
with
high
with
you
know,
higher
quality
data
and
then
in
this
sense
we
are
talking
specifically
about
the
mro
data.
B
B
The
cataloging
process
say,
for
example,
is
simpler.
Visibility
of
material
inventory
is
current,
actual
you
find
materials
when
you
need
them.
You
know
the
status
of
your
materials
in
your
inventory
is
true.
It's
not
that
I
can
see
something,
but
when
I
go
to
find
it
there
is
not
that
material
available.
So
I
think
so
the
value
created
is
really
really
high
and
you
know
that's
one
of
the
leading
factors
to
to
go
in
for
an
mdm
solution.
A
Yeah
so
talk
about
more
of
data
quality
perspective
that,
as
I
said,
you
know,
cost
effectiveness
like
cost
implications.
Are
there.
So
these
are
some
of
the
you
know,
a
quick
statement
which
we
have
gathered
from
dnb
report.
It's
like
more
of
how
many
100
000
data
or
500
000
data
you
have
and
how
much
you
you
spend
you
you
you
map
the
complete
costing
out
of
it
and
how
much
is
the
percentage
this
is
all
about.
A
So
this
is
more
of
you
know,
data
quality
because
that's
the
stepping
stone
of
getting
into
a
master
data
management
perspective
because
data,
if
you
have
a
bad
data
of
course,
you
will
never
have
a
governance
structure
knee
structure
in
place,
so
that's
something
which,
which
talks
about
more
of
the
cost
effectiveness
perspective
mdo
overview.
So
that's
how
come
like,
like
you
come
to
know
about
the
solution
all
together,
so
it's
like
majorly
five
aspects
of
the
product,
so
one
is
the
baseline,
which
is
mdu
fused
platform.
A
So
so
first
thing
is
that
platform
is
a
low
code
and
no
code.
You
don't
have
to
do
the
con.
You
don't
have
to
do
the
coding,
it's
a
drag
and
drop
a
purely
configured
platform,
okay
being
into
sas
based
approach.
So
first
first
aspect
is
the
master
data
management
which
you
talk
about
for
enterprise
data
solution,
like
more
of
you,
know,
improvising
the
quality
of
your
data
and
do
the
governance
in
place.
A
The
second
aspect
is
diw,
which
is
data
intelligence
workbench,
which
talks
about
your
data
quality
right.
So
drw
is
a
it's
a
kind
of
a
framework
with
the
with
your
mdm
solution.
All
together
we
do
the
profiling.
We
help
different
industry
to
understand
what
is
the
current
status
of
the
data
enrichment?
A
A
We
can
do
it
as
a
one-time
activity,
because
we
have
a
separate
team
of
mdas
master
data
service
where
they
help
and
build
the
complete
library.
Out
of
it
gives
you
the
fuzzy
logic
of
the
exact
match.
Out
of
your
you
know,
all
your
part
numbers
material
descriptions.
Nouns
and
modifiers
and
all
those
stuff
so
diw
enables
you
to
understand
your
current
quality
of
your
data,
eradicate
all
the
duplicates
manage
the
complete
data
set
out
of
it
before
we
get
into
the
governance
structure.
Right
analytics,
of
course,
most
important
aspect:
hey
look.
A
What
is
the
current
health
of
your
data?
How
do
we
see
that
right
and
the
holistic
view
perspective?
So
that's
a
md
as
a
strong
reporting
framework,
a
dashboard
framework.
You
can
say
that,
where
you
know
do
the
analytics
meets
perspective,
you
you
you
take
out
the
trends
of
your
quality
or
of
your
data
quality
or
or
your
governance
perspective
yeah.
Actually
you
want
to
talk
about
more
on
this.
B
And
yeah
I
mean
so,
you
know
beyond
analytics.
I
would
say
you
know
the
metadata
thing,
which
is
the
cataloging
and
the
linear
solution.
You
know
that's
coming
on.
I
mean
that's
as
part
of
our
road
map
so
that
you
jump
on
slide.
Okay,
no
worries!
So
that's,
that's!
You
know
part
of
our
road
map.
You
know
the
lineage
piece
is
becoming
more
and
more
important
in
in
today's
world.
You
know
having
having
data
traced
from
the
source
till
the
way
we
use
it
from
a
data
integration
perspective.
B
A
The
deduplication
part
make
make
the
complete
data
set
consistent,
how
you
manage
the
quality,
the
compliance
and
mediation
of
all
those
all
those
data
sets,
and,
of
course,
mdo
has
got
our
standard
business
rules
schemas
and
validations,
which
has
been,
which
is
already
pre-configured,
designed
as
per
the
best
industry
practice,
as
per
our
experience
into
different
customers
right.
So
so
these
rules,
these
rule
engine,
are
very,
very
important
to
run
your
complete
business.
A
You
know
your
your
complete
process
out
of
it
right
for
for
for
end-to-end
mass
data,
and
these
workflows,
of
course,
workflows
is
a-
is
a
very,
very
strong
aspect
of
mass
data,
because
that
enables
you
a
maker
and
shaker
kind
of
a
process.
Why?
Because,
whenever,
when
you
want
to
create
a
material
of
course,
as
a
lead
perspective,
you
want
to
know
what
kind
of
material
has
been
created.
What
is
the
description
generation
all
about?
A
What
are
the
means
means
what
are
the
other
logics
you
have
put
while
creating
the
material
or
while
changing
the
material
or
extending
it,
or
to
extend
it
out
right
and
the
workflow
plays
a
very,
very
important
role
in
doing
a
maker
and
checker
kind
of
a
role,
because
it's
both
from
the
initiator.
It
goes
to
a
reviewer
and
an
approver,
and
accordingly
it
moves
to
your.
A
It
moves
your
end
system,
which
is
s4hana
or
ecc
right,
so
that's
something
which
is
purely
guided,
purely
plug
and
play
drag
and
drop,
and
accordingly,
you
can
configure
the
workflow
in
it.
That's
that
that's
the
power
of
the
means
power
of
the
product
right
apart
from
that
source
systems
perspective
takes
from
irrespective
whatever
system
it
is
like.
You
know
if
it's
a
legacy
system
like
oracle
or
or
or
any
kind
of
sap
ec,
or
so,
for
example,
if
you're,
if
you're
getting
into
s4hana
journey
you're
going
to
start
it.
A
So
that's
the
right
time
to
get
on
to
your
data
quality
aspect,
understand
your
complete
set
of
systems
and
accordingly,
move
to
your
s4hana,
because
s4hana
accepts
the
consistent
data
right
with
the
governance
structure
in
place.
So
that's
that's
how
we
play
an
important
role
in
in
in
highlighting
all
these
aspects
very,
very,
very
manually,
yeah
ashish
you
want
to
you,
want
to
speak
more
on
this.
B
A
Right
right
right,
absolutely
as
we
talk
about
mdu
for
sap,
we
are
in
rose
app.
It
means
we
are
federated,
like
enterprise
master
data
strategy
altogether,
of
course,
pre-defined
data
models.
We
have
an
inbuilt
integration
like
hci,
hana
cloud
integration
platform
through
bdp,
right
and,
of
course,
there's
like
purely
industry,
agnostic,
all
the
materials
customer
vendors.
All
all
these,
all
these
standard
materials
are
part
of
the
system
of
the
of
the
product,
which
is
inbuilt,
which
is
pre-configured,
of
course,
the
maintaining
audio
golden
records.
A
You
know
and
and
of
course,
from
a
product
perspective,
it
is
purely
ai
and
mlm
based
because
it
it
validates
the
data
automatically.
Okay,
it
gives
you
the
prompt
it
gives
you
it
gives
you
all
those
highlighters
that
hey
look.
You
are
entering
the
wrong
data
right
and
that's
how
the
process
has
to
be.
So
it
is
all
embedded
with
your
with
your
sap
system,
of
course,
that's
something
which
is
again
a
very,
very
important
aspect
to
know
for
all
the
sap
customers,
all
together,
yeah
hashish.
B
I
was
just
saying
you
know
from
a
sap
landscape
perspective.
You
know
we
are
also
working
with
the
ain
network
and
you
know
the
c4s
contents
is
coming
in
so
again
this
this
will
be
an
extra
layer
of
you,
know,
integration
or
how
we
work
with
the
sap
landscape
and-
and
I
think
it's
very
interesting
times
you
know
the
ai
and
peace
is
very,
very
interesting.
A
Yeah.
Absolutely
so,
as
I
said
journey,
it's
it's
a
it's
a
it's
a
long
journey,
of
course,
when
you
do
the
complete
health
assessment
of
your
data,
how
the
data
looks
like,
irrespective
whatever
data
it
is
and
where
the
condition
is
and
how
you
profile
it
do
the
enrichment.
You
understand
the
quality
of
the
data,
the
current
states
of
the
data,
there's
some
missing
information
or
missing
part
number
description.
A
Anything
anything
can
happen
in
your
data
right
and
and
how
we
cleanse
and
enrich
and
how
we
govern-
and
this
is
something
which
is
which
is
embedded.
Why?
Because
this
is
something
which
is
really
like
it
has
to
be.
This
is
the
actual
process
to
ensure
your
data
quality
as
it
is
at
par,
and
you
have
a
structured
governance
in
process
right
anyway.
A
B
B
The
second
piece
could
be
you
yes,
I
know
my
you
know.
The
quality
of
my
data
is
not
that
great
or
you
know
that
you
know
what
we've
done.
This
exercise
we've
cleaned
up
and
we
don't
want
to
be
in
that
state
all
over
again
at
any
given
point
in
time.
So,
based
on
you
know
what
phase
of
the
journey
you
are
in,
if
you
don't
know
what
you
don't
know,
you
know
I
would,
you
know,
recommend
that
go
in
to
start
with
the
data
health
assessment.
B
B
You
know
compliance
piece,
so
you
know
I
mean
all
of
that
can
be
done
and
and
and
assessment
report
comes
through
for
you
to
understand
your
data
quality
beyond
that.
Knowing
what
the
problem
is.
You
of
course
then
need
to
start
working
on
the
problem,
which
is
more
about
cleansing
and
enriching
the
data.
So
cleansing
is
you
know
what
you
know
a
tool
like
diw
that
mdo
has,
you
know,
gives
you
the
opportunity
to
say:
remove,
duplicates,
look
out
for
missing
aspects
of
of
data
and
beyond
that
you
know
there
is
the
enrichment
piece.
B
You
know
you
got
the
manufacturer
part
number
available
to
you,
you
know,
can
can
a
tool
or
can
someone
else
go
in
and
enrich
that
data?
So
now,
all
in
all
you
have
data
which
is
good.
You
have
removed
the
duplicates.
You
have
a
golden
record
and
now
at
this
point
in
time
you
want
to
make
sure
that
your
data
is
not
being
corrupted
or
you
know,
no
bad
data
enters
your
system
and
that's
where
we
would
say
you
know
start
with
the
governance
piece.
B
A
B
A
little
bit
of
that
in
our
you
know
in
our
previous
slide
or
the
talks,
and
it's
it's
all
about
the
two
governance
models
of
data.
As
we
said
you
know,
looking
at
the
data
quality
profiling,
cleansing
enrichment
is
more
of
the
passive
governance
which
allows
you
to
correct
and
enrich
data,
and
then
you
know
you
have
something
else,
which
is
the
active
data
governance
which
we
spoke
about,
which
is
and
again
the
two
concepts
out
here
are
are,
like
you
know.
A
B
B
B
B
B
B
A
B
B
You
know
we
go
with
the
sap
business
or
the
btp
system,
and
this
comes
standard
along
with
mdu.
So
once
you
subscribe
to
mdo,
the
btp
infrastructure
is,
is
already
you
know
paid
for,
you
know
comes
along
with
mdo
and
we
do
the
integration
through
cloud
integration
on
the
customer
side.
You
know
with
your
sap
instance.
We
expect
you
to
have
the
cloud
connector
and
you
know
your
your
ecp.
B
B
You
know
you
know
it's
standard
documents
that
comes
along
with
mdo,
and
our
experience
is
that
you
know
this
can
be
set
up
within
a
week,
so
the
connectivity
and
integration
can
happen
in
in,
in
fact,
a
few
days
time
and
all
these
standard,
you
know,
data
models
that
we
spoke
about.
We
have
the
transports
which
come
into
the
customer
environment.
Prospector
namespace
programs
are
also
shared
with
the
customers,
which
you
just
need
to
import
into
your
system
and
off
you
go.
A
B
B
You
know
which,
which
we
need
reduction
in,
not
able
to
find
the
right
parts.
You
know
poor
description
again,
free
text
purchasing.
I
can't
find
a
part,
I'm
just
gonna,
you
know
raise
a
new
po
excuse
me
and,
and
I'm
gonna
you
know,
go
ahead
and
purchase
something
now.
Not
only
are
you,
you
know
creating
extra
inventory,
but
you
know
the
spend
analysis
goes
for
a
toss
again,
taxonomy
standards,
you
know
how
do
we
follow
all
of
that.
B
So
that's
that's
an
you
know,
important
thing
that
you
know
to
think
about.
I
I
already
touched
base
a
little
bit
on
the
ain,
which
is
the
sap
business
networks
piece.
I
think
so.
Those
those
are
the
areas,
and
you
know
typically
what
we've
seen
is.
You
know
the
benefits
realized
by
customers
is
has
been
in
the
range
of
five
to
ten
percent.
You
know
either
from
inventory,
holding
cost,
or
you
know
just
your
space
procurement.
You
know
bringing
in
more
efficiency
in
that
area.
B
B
The
challenges
are,
you
know
more,
so
from
data
rules
for
assets
not
being
documented
and
and
again
there
is
a
lot
of
unstructured
data
in
there
low
data
collaboration
with
partners.
You
know
there
are
no
defined
industry
standards
or
taxonomies,
followed
manual
processes
to
request
for
master
data
creation.
So
that's
where
the
governance
piece
comes
in
and
again,
you
know,
retreating.
B
Our
solution
with
with
data
quality
piece
coming
in
and
with
active
governance
coming
in,
you
are
basically
being
able
to
bring
in
structure,
and
you
know
some
process
to
the
madness.
I
would
say:
benefits
typically
realized
have
also
been
that
you
know
automation
of
data,
so
60
of
master
data
requests
will
not
need
you
know
approvals
anymore
and,
and
they
are
automated
based
on
governance
rules,
and
you
know
those
things
and
so
basically,
that's
the
two
areas
that
I
wanted
to
highlight.
B
Correct,
I
can
just
go
with
that,
so
that
yeah
so
differentiators
for
mdo,
as
we
initially
are
highlighted,
it
is
one
system
for
both
data
quality
governance
and
data
stewardship.
It's
it's
a
sas
based
product
available
on
the
cloud.
So
it's
a
quicker.
You
know
roi
on
your
investment,
easy
to
deploy
and
configure,
as
dart
mentioned
before
it
is
low
code,
no
code,
type
of
an
environment.
B
Very
rarely
do
you
go
into
that
part
of
coding
and
customization,
but
apart
from
that,
it
is
all
very
user
friendly
ui
is
is
what
drives
you
know
the
creation
of
business
rules,
the
configuration-
and
we
basically
say
you
know
a
customer-
can
configure
and
start
using
the
system
on
their
own.
You
know
that.
That's
how
easy
it
is.
B
We
already
highlighted
predefined
data
models
for
both
domain
and
the
system.
So
from
the
perspective
of
the
masters
that
we
spoke
about,
all
of
those
masters
are
pre-configured.
With
the
existing
business
rules
that
you
know
you,
you
would
have
habituated
within
sap.
That's
all
in
there
aiml
based
data
quality,
so
that
touch
base
a
little
bit
on
that
industry,
specific
content
for
standardization
and
compliance.
So
we
spoke
about
the
various
you
know,
iso
codes
and
the
unspsc
compliance
that
we
make
it
very
important.
B
One
out
here
is
less
reliance
on
id
and
more
self-managed,
and
I
spoke
to
you.
You
know
a
little
bit
about
that
that
it's
all
configuration
based
once
the
connectivity
is
set
up.
I
think
so
that's
pretty
much
what
you
will
rely
on
the
id
beyond
that.
It
is
all
configuration,
and
you
know,
data
stewards
and
admins
can
manage
the
whole
system
on
their
own
and,
of
course
it
provides
you
the
foundation
for
a
digital
transformation
project.
You
know
you
could
be
in
the
journey
for
any
of
your
digital
processes
or
your
s4
transformations.
A
Yeah,
just
one
more
point
I
believe,
being
mdo
is
on
to
cloud
which
is
amazon
web
services.
We
are
partnered
with
which,
which
actually
gives
you
a
warranty
of
99.9,
which
means
it
states
that
your
your
complete
data
set
is
like
fully
secured.
You
know
it,
it
is
like
means
like
auto,
archival
and
and
and
it's
like
confidentiality
we
maintain
means
from
that
perspective,
that's
something
which
is
again
a
very,
very
important
aspect
and,
of
course,
as
you
said,
less
reliance
on
I.t
self-managed,
so
you
don't
have
to
depend
on
us.
A
Rather,
you
can
be
the
own
admin
of
your
system
right.
It's
just
a
quick,
drag
and
drop
configuration,
and
you
can
define
your
roles,
your
users
and
you
know
all
your
fields
or
workflows
and
accordingly
you
can
manage
the
complete
system
out
of
it.
So
that's
these
are
some
of
the
points
which
makes
mdo
different
from
the
other
ones,
of
course
yeah
yeah.
So
I
believe
it's
time
to
have
a
quick
view
of
the
product
hashish.
B
B
B
Then
you
go
to
your
data
sets
and
what
you
see
on
your
left
are
the
various
masters
which
have
been
configured
into
the
into
the
system.
So
you
know
you
see
a
bill
of
materials,
equipment,
maintenance,
plan,
material
master.
So
for
this
one
we'll
just
go
through
the
material
masterpiece
and
once
you're
in
the
material
masters
piece.
You
know
you
you're,
creating
a
new
record
or
this,
which
is
now
a
request
actually
for
a
new
material.
B
B
So
basically,
you
know
a
short
description
is
a
40
character
thing,
which
is
an
sap
limitation,
but
you
still
have
the
long
description
and
you
know
this
is
one
of
the
aspects
how
you
bring
in
standardization,
because
now
I'm
not
the
person
who
will
say
single,
sgl
or
sg.
You
know
I
don't
make
my
own
abbreviations.
B
The
system
has
the
capability
of
generating
the
description
for
you
based
on
you
know
the
noun
modifiers
that
that
we
choose
for
so
we'll
just
have
a
look
at
that,
so
you
know
someone's
going
in
and
creating
or
you
know,
choosing
a
class
of
ball
bearing
and
you
know
they're
putting
in
the
attributes
of
of
the
of
the
spare
up
there
and
what
you
see
up
there.
You
know
the
system
is
already
generating
this
short
description
and
the
long
description
based
on
the
attributes
that
the
user
chooses
for.
B
C
B
So
you
see
the
descriptions
all
being
populated
and
then
you're
going
into
your
basic
data,
adding
just
and
reference
old
material.
You
know
net
weight
gross
weight,
and
this
is
this
is
pretty
much
what
the
user
is
going
to
do
at
this
stage
and
safety
record
now.
You
know
I
spoke
a
little
bit
about
inbuilt
validations
or
business
rules.
B
So
basically
there
is
a
validation
which
even
sap
does,
which
you
know
mdo
does
is
around
net
weight
cannot
be
greater
than
the
crossfit
right,
just
as
an
example,
so
the
system
will
stop
you
going
forward
from
there
on,
and
you
know
the
data
needs
to
be
corrected
before
you
know
this.
The
user
moves
ahead
so
now,
on
saving
the
record
a
record
with
you
know,
a
number
ending
in
six
was
what
was
generated.
We
can
see
some
process
logs.
B
C
B
B
You
know,
extend
the
material
to
different
plants
within
your
organization
and
also
you
know
within
the
plants
you
can
go
for
different
evaluations,
storage
data
type.
So
we'll
just
have
a
look
at
that.
So
you
know
the
user
is
now
extending
this
material
to
multiple
plants
and
within
plants.
Then
they
can.
You
know
so
now
more
mandatory
information
becomes
available
based
on
the
plant
level
data
that
we
need
to
fill
up,
so
the
mrp
type.
The
reorder
point
has
been
entered.
B
A
B
So
at
this
stage,
the
user-
you
know
the
reviewer
or
the
extender
has
done
that
you
know
and
it's
gone
for
the
final
approval
and
the
person
who's
doing.
The
final
approval
will
also
look
into
and
try
to
approve,
also
interesting
out
here
is
that
there
can
be
certain
checks
and
on
on
on
data
fields,
which
you
can
say
that
it's
not
mandatory
for
a
reviewer
or
an
initiator,
because
at
times
you
know
not
that
that
information
is
not
the
responsibility
of
that
particular
role.
B
But
when
it
comes
to
the
final
approval,
the
person
needs
to
be
able
to.
You
know
make
sure
that
those
fields
are,
you
know,
entered
correctly.
So
that's
where
you
see
more
validation
and
checks
being
put
in
place,
and
basically
you
know
the
approver
is
scanning
through
making
sure
all
the
data
is
correctly
filled
up
and
beyond
that
they
will
go
and
approve,
and
at
this
point
in
time
the
material
completes
its
you
know,
process
in
mdo
and
with
the
integration
back
to
sap.
B
A
A
C
C
B
B
As
I
said
out
of
the
box,
you
know
you're
going
for
a
you
know,
material
master
out
of
the
box
use
the
validations.
Yes,
your
business
will
need
a
few
more
business
rules
which
can
be
very
quickly
configured
and
stick
to
the
standards.
I
think
you
know,
with
with
all
of
that
about
12
weeks
is
something
which
is
achievable
per
master.
I
would
say.
C
Okay
and
in
terms
of
governance
right,
so
one
of
the
one
of
the
biggest
problems
when
it
comes
to
data
quality
is
the
difficulty
of
maintaining
quality
over
a
period
of
time,
which
means
the
governance
become
that
much
more
critical.
Otherwise,
every
two
years
you
have
to
clean
up
your
data
again
and
again
right.
C
B
So
so
to
keep
the
data
quality
high
mukesh,
I
would
say
you'll
absolutely
need
the
active
governance
or
which
is
the
you
know,
the
normal
governance
that
we
saw
about,
but
to
start
with,
you
know
if
you're,
in
a
bad
shape
right
now.
If
your
data
quality
is
not
that
great,
then
then
I
would
highly
suggest
that
you
start
with
the
passive
governance,
which
is
the
data
quality.
B
Look
at
your
data
quality
improve
the
data
quality
because
you
don't
want
to
be
in
a
situation
where
you
have
bad
data
but
you're
starting
to
start
governing
the
new
data
coming
in.
But
at
the
same
time
you
know
not.
Everyone
has
the
luxury
of
of
really
stopping
their.
You
know
doing
anything
doing
the
data
quality
improvements
first
and
then
going
for
active.
So
we've
also,
you
know,
support
scenarios
where
you
know
you
could
start
with
active
governance
now
and
then
you
are
on
a
on
a
periodic
basis.
B
You
know
taking
say
100
000
materials
at
a
time
and
then
you're
cleaning
up
or
doing
the
passage
of
governance
piece.
You
know
your
your
cleansing
and
enriching
that
data
in
parallel,
but
but
at
least
you're
stopping
garbage
coming
in
into
your
system.
So
it's
it's.
It's
a
good
combination.
You
know
it
all
depends
on
what
the
customer
needs.
Are.
You
know
how
much
time
can
you
afford
and
you
know
which
model
the
customer
makes.
You
know
the
best
sense
for
the
customer.
C
A
So,
for
if
you,
if
you
ask
this
question
so
from
that
perspective,
our
the
customer
base
should
be
a
large
enterprises,
because
the
data
set
or
the
volume
of
the
data
is
huge
and,
of
course,
the
quality
means
data
will
get
improvised
if,
if
the
quantum
of
of
the
data
set
is
huge,
so
large
customer,
ranging
from
a
manufacturing
industry
or
a
farmer,
can
be
a
good
one
and
they
can
find
us
we
are.
We
are
always
available
in
our
website.
A
A
C
Right
super.
Thank
you
so
much
for
taking
time
and
sharing
your
insights
with
us.
I
know
that
no
digital
transformation
program
is
complete
unless
your
data
is
clean.
Data
driven
decision
making
is
super
critical,
going
forward
in
the
very
uncertain
environment
that
we
operated,
and
in
order
for
for
that
to
work,
you
need
to
have
your
data
clean.
So
please
look
up
the
solution
of
a
web
prospector
and
reach
out
to
them
or
to
me
if
you
want
to
get
more
information.
So
thank
you
so
much
for
your
insights
and
sharing
today.