Hello.
My
name
is
Peter
Polito.
I'm
a
Senior
Systems
Engineer
at
JMP.
Today
I'm
going
to
be
talking
to
you
about
quantifying
your
organization's
analytical
maturity
in
order
to
make
data-
driven
decision
making
so
you
can
do
things
better.
I've
been
using
JMP
for
some
time.
I
actually
learned
JMP
on
a
boot
leg
copy
of
version
six
when
I
was
in
graduate
school.
It's
been
a
part
of
my
life
for
a
long
time,
and
being
able
to
use
it
to
help
others
find
success
is
one
of
my
great
joys.
Today,
I
have
a
lot
of
people
that
are
helping
support
this,
primarily
is
Brady
Brady.
He
is
a
Principal
Systems
Engineer
also
at
JMP.
He
helped
craft
the
background
tool
to
perform
all
the
analytics
that
I'll
be
presenting
and
that
you
may
be
able
to
take
advantage
of.
Then,
of
course,
I
want
to
thank
my
team.
We
work
with
high
tech
companies
here
in
the
United
States,
and
that
is
Ben
Ross,
who
is
a
Strategic
Account
Manager,
and
Kyle
Bickford,
a
Senior
Account
Executive.
The
goal
for
today
is
to
demonstrate
how
you
can
collaborate
with
your
JMP
support
team
to
quantify
your
organization's
analytical
maturity.
By
quantifying
that,
you'll
be
able
to
understand
where
people
are
spending
their
time
and
how
competent
they
are,
and
then
use
that
as
a
benchmark
to
track
progress.
By
working
with
your
JMP
support
team
in
this
effort,
they
can
help
craft
the
support
necessary
to
bring
your
team
from
where
they
are
to
where
you
would
like
them
to
be.
How
do
we
do
this?
We
use
the
JMP Analytical Workflow Survey.
I'll
refer
to
it
as
JAWS
throughout
this
talk.
I
feel
that's
a
little
catchier.
The
tool,
it's
a
quantitative
tool
to
just
measure
the
analytical
maturity
for
your
entire
organization.
Despite
JMP being
in
the
name,
it
is
not
just
for
JMP
users
and
so
the
idea
is
that
you
assess
an
entire
organization.
You
can
break
it
down
by
department,
by
job
title,
etc.,
and
then
use
this
by
annual
rerunning
of
the
survey
to
understand
if
you're
moving
in
the
right
direction
and
what
areas
you
need
help
in.
It
also
helps
you
identify
white
space
where
maybe
analytics
is
heavily
used,
so
that
you
can
bring
that
up
to
your
management
or
whoever
it
might
be
in
order
to
help
get
your
entire
organization
moving
in
mass
in
the
direction
that's
going
to
drive
your
company
towards
discovery,
efficiency
and
growth.
How
does
it
work?
It's
simple.
It's
a
five
minute
anonymous
survey.
It
measures
the
amount
of
time
a
person
spends
performing
a
particular
analytical
task.
It's
not
just
one,
but
all
of
their
analytical
tasks.
It
understands
their
self-professed
competency.
By
doing
this
anonymously,
we
find
that
people
tend
to
be
more
honest.
Not
only
do
they
get
to
say,
I
spend
two
hours
a
week
doing
data
visualization,
but
they
can
also
say,
I
don't
really
know
what
I'm
doing,
or
I
got
this.
I'm
definitely
an
advanced
user.
So
we
can
understand
how
much
time
they're
spending
and
what
level
of
competency
they
have.
Maybe
most
importantly,
it'll
give
the
person
opportunity
to
say,
I
need
to
know
how
to
do
this,
and
I
need
help
because
I
don't
know
what
I'm
doing,
or
I
need
to
do
this
to
complete
my
job,
and
I'm
totally
competent
in
this.
It's
time
spent,
how
well
they
think
that
they're
able
to
do
it,
and
where
do
they
need
support
to
do
their
job
better.
If
you
think
about
it,
those
three
things
for
management
to
know
that,
I
mean,
that's
incredibly
valuable.
This
is
a
very
easy
way
to
identify
this
information,
and
we
present
it
in
a
way
that
has
great
visuals,
easy
to
comprehend
and
digest,
easy
to
share
with
upper
management
to
help
build
that
roadmap
for
getting
your
organization
from
where
they
are
to
where
you
want
them
to
be.
What
exactly
are
we
looking
at?
Well,
if
you're
unfamiliar,
this
is
called
the
JMP
analytical
workflow.
It
works
left
to
right.
On
the
left
you
have
where
is
data
coming
from?
For
example,
in
the
survey,
it's
going
to
ask
how
much
of
your
week
is
spent
interacting
with
files
or
documents
or
databases
or
web
APIs.
Your
data
comes
into
some
analytical
tool,
preferably
JMP,
but
it
might
be
something
else.
Then
people
spend
time
doing
tasks.
From
accessing
data
to
performing
basic
data
analysis
and
modeling.
Maybe
they're
doing
reliability,
consumer
research.
Maybe
their
job
is
more
focused
on
building
automations
for
the
organization.
They're
doing
something
or
a
series
of
some
things
that
take
some
time
and
requires
competency.
Then,
of
course,
they
need
to
share
that.
It'd
be
a
shame
if
all
of
our
hard
work
just
lived
in
our
hard
drive,
we
presented
it
in
a
PowerPoint
in
a
meeting,
and
then
it
just
goes
away.
We
want
data
to
come
in,
we
want
something
to
be
done
to
that
data,
and
then
we
want
data
to
be
shared
with
the
entire
organization
so
that
people
can
learn.
The
longer
I
am
in
this
position,
the
longer
I
recognize
there's
no
such
thing
as
a
one-off
problem.
Everything
comes
back
in
some
shade
of
gray
relative
to
where
it
started.
If
we
have
an
area
or
a
way
to
query
all
of
the
problems
an
organization
has
solved,
it's
going
to
save
a
lot
of
time
in
the
future
because
people
aren't
going
to
be
starting
at
ground
zero.
W
e
want
to
measure
all
of
these
things.
Where
are
they
spending
their
time?
How
capable
do
they
feel
they're
at
doing
that?
And
where
do
they
need
support
to
do
that
better?
The
first
step
is
collecting
the
data.
This
is,
as
I
mentioned,
about
a
five
minute
survey.
The
first
three
questions
are c
ompletely
customizable
to
fit
your
organization.
This
is
anonymous,
but
we
want
to
know
some
things.
We
want
to
know
where
are
these
people
located?
For
some
of
you,
it
might
be
we're
all
in
one
place.
Maybe
they
work
in
the
office,
they
work
from
home.
Who
knows?
Maybe
they
work
in
the
United
States.
Maybe
they
work
in
Europe.
Maybe
they
work
in
Asia.
Maybe
they're
just
spread
out
in
different
sites
across
the
US.
But
we
can
customize
that
to
fit
your
o
rganizational
design.
Then
we
want
to
know
what
department
they're
in
and
what
are
their
job
roles.
This
allows
us
to
slice
and
dice
that
data
once
that
survey
data
is
collected
to
better
understand
where
are
things
working
well
and
where
do
we
need
support?
You
may
have
an
R&D
department
that's
in
one
location
that's
just
crushing
it.
They
are
very
competent.
They're
very
capable.
They're
not
spending
too
much
time
because
they
built
automation.
Then
their
peers
at
maybe
a
newer
location
are
way
behind.
They're
the
ones
that
need
support
by
designing
it
in
such
a
way
that
we
can
slice
and
dice
it
by
department,
by
job
title,
by
region.
We
can
really
get
to
the
heart
of
where
support
is
needed
or
really
pat
ourselves
in
the
back
because
we're
doing
things
well.
We
are
where
we
thought
we
would
be.
But
I've
administered
the
survey
many
times,
and
every
single
time
I
hear,
I
thought
we
were
better,
so this
is
a
great
way
to
measure
that.
From
there,
we're
going
to
look
at
how
much
time
do
people
spend
doing
particular
things.
You'll
see,
none
of
this
is
JMP
specific.
It's
really
designed
for
anyone.
Anyone
working
with
data
needs
to
get
that
data.
Anyone
working
with
data
needs
to
clean
that
data,
put
it
into
a
position
where
they
can
analyze
it,
look
for
outliers,
visualize
it,
whatever
the
case
may
be.
Additionally,
we'll
have
something
very
similar
to
this
to
collect
data
on
what
their
competency
is.
Then,
of
course,
as
I
mentioned,
we
have
the
opportunity
for
them
to
say,
I
need
to
access
data.
It
is
critical
to
my
task,
and
I
am
not
good
at
it.
I'm
inefficient,
I
don't
know
how
to
query
our
database,
I
don't
know
how
to
bring
in
55
CSV
files
efficiently,
I
need
advanced
training.
Or
I'm
really
good
at
this,
or
I
don't
even
need
this,
someone
just
emails
me
a
file
and
I
do
my
work.
It
allows
them
to
tell
you
what
do
I
need
to
be
better
at,
so
that
I
can
be
better
at
my
job.
At
the
end
of
the
day,
most
of
our
people
want
to
do
their
job
well.
They
want
to
be
successful.
They
want
to
advance
in
the
company.
They
want
to
show
that
they
have
value
and
worth.
This
is
an
opportunity
for
them
to
tell
you
where
they
think
they
need
help.
There's
been
a
few
instances
where
I've
talked
to
a
management
team,
and
they're
like,
this
department
doesn't
know
how
to
do
that.
Then
the
survey
results
come
back
and
the
manager
is
like,
my
goodness,
they
all
feel
like
they
need
to
know
how
to
do
this
better.
I
have
no
idea.
This
is
really
an
eye-
opening
opportunity
for
a
lot
of
people
when
they
see
these
results
to
really
fully
understand
exactly
where
their
people
are
versus
where
they
think
they
should
be.
We
collect
this
data,
your
JMP
support
team
will
analyze
this
data,
and
then
they'll
be
able
to
present
it
to
you.
Now
we're
going
to
walk
through
what
this
data
looks
like,
so
you
can
get
a
sense
of
what
will
I
learn.
We'll
go
at
a
couple
of
different
views.
At
the
10,000
foot
view,
we
get
these
heat
maps
that
show
where
are
people
spending
their
time.
I've
broken
this
up
by
organizational
wide
on
the
upper
left,
by
job
title
on
the
upper
right,
and
by
department
at
the
bottom.
If
we
just
look
at
the
upper
left,
we
can
see
that
people
are
predominantly
interacting
with
files,
probably
Excel
files
or
databases,
and
they're
doing
a
lot
of
data
exploration,
a
lot
of
basic
data
analysis
modeling,
very
little
reliability
analysis,
a
little
bit
of
quality
process
engineering,
and
they're
primarily
sharing
images.
If
we
look
at
job
title,
it's
a
similar
story,
but
this
particular
job
title
is
maybe
doing
a
little
bit
more
time
running
design
experiments.
Then
by
department,
maybe
this
is
an
analytics
department
or
a
chemistry
department,
but
they're
doing
a
lot
of
DOE,
a
lot
of
basic
data
analysis,
a
lot
of
data
base,
and
then
sharing
images.
As
a
leader
in
your
organization,
you
might
ask
yourself,
are
images
the
best
way
to
share
this
data?
If
not,
this
shines
a
light
on
the
fact
that
your
company
is
spending
a
lot
of
time
sharing
images.
Maybe
A,
this
could
be
automated,
or
B,
maybe
we
want
to
push
people
in
a
different
direction
sharing
some
other
data
format,
writing
particular
reports
or
etc.
But
it
just
shines
a
light
on
the
things
that
are
going
on.
Then
you
and
the
support
team
will
work
together
to
better
understand,
they'll
probably
have
lots
of
questions,
is
this
what
you
want?
Is
this
what
you
expected?
Should
you
have
people
doing
more
quality
if
you're,
say,
a
manufacturing
firm?
Do
you
want
people
to
be
quality
minded
or
do
you
have
a
quality
department?
Those
sorts
of
questions
will
start
to
flesh
themselves
out
and
they'll
help
you
craft
how
they
might
be
able
to
provide
that
support
to
you.
Or
you
might
just
take
these
results
and
say,
thank
you
so
much.
Now
we're
going
to
go
do
what
we
think
we
need
to
do.
Going
a
little
bit
deeper.
We'll
call
this
the
maybe
8,000
foot
view.
This
example
is
broken
up
by
department
and
location,
and
it's
showing
how
much
time
people
are
spending
doing
data
exploration
and
visualization.
We
can
see
in
the
manufacturing
department
and
in
the
fermentation
PD
department,
a
few
people
are
spending
over
eight
hours
a
week.
Whereas
over
at
R&D,
they're
spending
considerably
less
time.
Maybe
that's
fine,
maybe
that
isn't
fine.
But
again,
it
just
helps
you
see
exactly
how
your
people
are
spending
their
time.
If
you
have
someone
located
in
the
east
in
the
firm
department
and
they're
spending
zero
time
doing
data
exploration and
visualization,
that
might
be
a
problem.
I
would
think
they
would
need
to
share
their
data
and
look
at
that
data.
So
you
might
have
some
questions
and
you
go
ask
that
team
to
better
understand
exactly
how
they're
doing
things.
Then
we
get
to
go
a
little
bit
deeper,
and
these
are
my
favorite
images.
What
we're
looking
at
is
proficiency
on
the
left
and
usage
on
the
bottom.
Y
versus
X.
Then
these
cells
are
colored
by
how
many
people
are
spending
time
or
grafted
with
their
competency.
If
we
look
on
the
left,
we
see
quality
process
and
engineering.
For
this
particular
organization,
by
and
large,
people
are
not
doing
much
quality
work.
This
is
where
I
always
ask,
do
you
want
your
company
to
be
quality
minded,
or
is
quality
focused
on
a
single
department?
If
you
want
your
people
to
be
quality
minded,
this
might
be
a
red
flag.
No
one
is
an
advanced
user.
People
are
spending
predominantly
less
than
an
hour
a
week,
and
the
majority
of
the
people
aren't
doing
it
at
all.
This
would
be
a
situation
where
I
might
come
in
and
say,
can
I
teach
your
people
about
the
quality
tools
in
JMP
so they
can
better
understand
how
to
build
and
interpret
a
control
chart?
How
they
might
look
at
metrics
like
CPK
and
PPK.
Are
we
hitting
spec
limits?
Are
we
not
hitting
spec
limits?
How
do
we
understand
that
more
deeply
so
we
can
make
more
intelligent
decisions
to
solve
problems
and
understand
things.
Contrasting
that
on
the
right
with
this
basic
data
analysis
and
modeling
image,
we
see,
I
would
call,
more
maturity.
There
are
very
few
people
that
aren't
performing
this
task
at
all.
The
majority
of
the
people
are
intermediate
too
with
some
advance,
and
there's
also
a
lot
of
beginners.
But
what
I
see
here
is
critical
mass.
I
see
that
this
organization
has
enough
competence
and
enough
people
that
understand
it
well
enough
that
they
can
help
draw
those
novice
users
down
to
the
intermediate
level
and we
have
some
advanced
users
that
can
bring
the
intermediate
down
to
their
level.
We
also
have
people
spending,
predominantly
1-4
hours.
Not
that
many
people
are
spending
20
%
of
their
week
performing
this
task.
When
I
see
that,
I
ask,
could
this
be
automated?
We'll
get
to
more
of
that
in
just
a
moment.
This
really
helps
people
understand
where
they
are.
Do
they
have
maturity
in
this
analytical
capability
or
do
they
need
support?
Are
they
where
they
thought
they
would
be
or
do
they
need
to
move
their
people
through
support
to
a
more
advanced
understanding
and
competency?
This
is
probably
one
of
my
favorite
images.
What
we're
looking
at
here
is
on
the
left
on
the
Y
axis,
is
the
amount
of
time
people
are
using
a
particular
capability
per
week.
On
the
X
are
those
different
capabilities
that
we
saw
in
the
JMP
analytical
workflow.
For
now,
you
can
just
ignore
the
color.
Those
are
color
coded
by
the
amount
of
time
they've
been
using
JMP.
That's
one
of
the
only
JMP
specific
questions
in
the
entire
survey.
But
what
we
see
here,
and
I
want
to
draw
your
attention
right
here,
is
there
are
six
people
spending
eight
plus
hours
a
week
performing
data
access.
If
you
have
1
person
spending 8
hours
a
week,
that's
20
%
of
the
week.
That
means
five
dots
equate
to
one
annual
salary.
Is
one
annual
salary
how
you
want
to
be
spendingâ¦
Do
you
want
to
be
spending
that
amount
of
time
on
data
access?
Probably
not.
I
mean,
it's
not
cheap
to
hire
someone.
It's
not
cheap
to
support
them,
provide
benefits
and
training,
and
keep
them
motivated
and
keep
them
growing
within
the
organization.
This
is
a
very
impactful
image
because
it
shows
us
where
can
we
automate.
Clearly,
you
can
see
up
in
the
very
upper
left,
that
green
dot,
someone
has
already
automated
data
access.
Whereas
we're
spending
1.2
annual
salaries
on
data
access,
and
many
of
them
are
new
users.
Half
of
them
are
only
1-3
years.
So
could
we
come
in
and
teach
people
about
automation?
Could
the
person
that
has
already
automated
this
sit
down
with
these
other
six
people
and
teach
them
how
they
have
automated
their
process?
Because
if
you
can
free
up
an
entire
annual
salary,
think
of
what
you
can
do.
I've
worked
with
people
that
are
in
hiring.
I've
worked
with
people
that
manage
teams.
The
common
thread
I
hear
is
we
need
more
people.
Either
A,
we
don't
have
the
budget,
or
B,
and
right
now
in
this
environment,
it's
just
sometimes
hard
to
hire
people.
If
you
can
liberate
an
entire
person
from
a
particular
task,
just
think
of
what
more
you
could
do.
They
could
solve
more
problems,
they
could
help
bring
automation
elsewhere.
They
can
automate
data
access
for
everybody
potentially.
This
is
a
really
useful
thing.
On
the
flip
side,
if
we
look
at,
say,
predictive
modeling
and
machine
learning,
it's
right
here
in
the
center,
we
can
see
there
are
only
two
people
spending
any
time
at
all:
one,
one to
four
hours
a
week
and
one
less
than
an
hour
a
week.
We're
spending
a
fraction
of
the
time,
particularly
compared
to
data
access,
on
predictive
modeling
and
machine
learning.
Perhaps
this
is
not
necessary
in
your
organization.
Perhaps
it's
very
necessary
if
you
are
trying
to
understand
why
aren't
we
hitting
our
manufacturing
KPIs?
Why
are
we
having
these
issues
in
our
process?
We're
not
able
to
understand
exactly
why,
despite
having
everything
set
up
the
way
we
think
should
work,
we're
not
hitting
our
metrics.
Well,
again,
this
is
a
ripe
opportunity
for
support.
Yet
on
the
other
flip
side,
so
I
think
we're
looking
at
a
triangle
here,
a
prism
here,
basic
data
analysis
and
modeling,
I
see
they're
doing
fantastic.
They
have
a
lot
of
people
that
are
performing
basic
data
analysis
and
modeling.
We
have,
as
we
saw
earlier,
some
good
there.
We're
not
spending
a
lot
of
time.
These
are
one
of
the
tasks
where
automation
may
not
be
possible.
It
might
be,
but
it
might
be
people
are
dealing
with
problems
that
are
unique
every
single
time.
Again,
this
is
an
opportunity
where
I,
as
someone
trying
to
support
a
customer,
might
be
starting
to
ask
some
questions
and
understand
if
automation
is
even
possible.
But
by
and
large,
this
is
a
good
vertical
in
this
particular
graph,
as
well
as
over
on
the
far
right,
sharing
and
communicating
results.
That's
another
one
that
is
very
easy
to
automate,
but
the
majority
of
people
are
sharing
results.
I
might
have
some
questions
about
why
there's
maybe
about
40
%
that
aren't
and
is
that
important
to
you?
But
this
just
really
puts
the
entire
story
in
one
image
that
really
helps
you
understand
where
opportunity
is
to
A,
automate,
B,
train,
and
C,
say,
great,
we're
doing
well,
we
don't
need
to
spend
time
on
that.
Then
finally,
where
do
your
people
think
they
need
support?
On
the
left,
we
have
those
capabilities.
On
the
bottom,
we
have
four
questions.
Is
this
critical
to
my
task
and
training
is
needed?
This
is
critical
to
my
task
and
basic
training
is
needed.
I
don't
need
this,
or
maybe
I'm
just
interested.
I've
organized
these
based
on
the
majority
of
people
or
the
number
of
people
that
feel
that
advanced
training
is
needed.
This
is
from
a
different
organization.
But
I
imagine
if
you
are
a
tech-driven
company,
an
analytically-
driven
company,
you
would
probably
think
that
basic
data
analysis
and
modeling
is
not
something
that
you
need
to
worry
about.
But
here,
the
majority
of
the
people
are
saying
this
is
critical
to
my
task
and
I
need
advanced
training.
Again,
it's
just
shining
a
spotlight
on
the
areas
where
you
might
need
to
support
your
organization,
where
you
might
need
to
support
your
people
because
they're
saying
very
clearly,
I
need
help.
Whereas
mass
customization,
automation
and
scripting,
reliability
analysis,
these
aren't
things
that
people
need
as
much
support
on.
You
can
know
that
I
don't
need
to
spend
time
in
this
area
or
that
area.
I
need
to
focus
up
here.
It
turns
out
a
lot
of
these
are
fairly
basic
tasks
that
I
think
a
lot
of
people
think
that
people
are
fully
capable
and
competent
of,
but
they're
clearly
saying,
no,
no,
I
need
some
help.
The
benefits
of
the
JAWS
is,
it
identifies
strengths
and
areas
for
improvement
within
your
organization.
You're
able
to
work
with
your
JMP
support
team
and
provide
support
in
those
areas.
Your
support
team
has
the
training,
has
the
tools,
has
the
backing
support
of
a
large
organization
that
is
focused
solely
on
expanding
the
use
of
JMP
to
come
in
and
guide
your
people
in
whatever
support
you
need.
We
are
here
to
help
you
out.
Then
the
beauty
of
this
is
if
you
administer
this
survey
annually,
you're
going
to
start
to
be
able
to
track
progress.
You'll
be
able
to
see
we
needed
a
lot
of
help
in
design
of
experiment.
A
year
later,
we
see
improvement.
The
support
has
worked,
and
we
just
need
to
go
a
little
bit
farther.
We
can
say
DOE
is
now
doing
great.
Let's
focus
our
attention
elsewhere.
Some
best
practices.
This
is
very
practical,
but
what
we
have
learned
is,
don't
allow
a
long
time
for
the
survey
to
be
filled
out.
We
say
send
it
out
on
Monday,
send
a
reminder
email
on
Wednesday,
and
close
the
survey
out
on
Friday.
How
this
would
work
is
you
would
work
with
your
JMP
support
team
to
craft
those
questions
about
region,
about
department,
about
job
title,
and
then
they'll
just
provide
you
the
survey.,
You
send
that
survey
out
and
people
fill
it
out,
we
collect
the
data,
it's
all
anonymous,
and
then
we
analyze
those
results
and
then
come
back
and
share
those
results
with
you.
But
keep
it
short.
Don't
allow
people
a
long
time
because
people
get
busy
and
they
just
forget.
It's
really
important,
I
think,
to
get
those
three
questions
right.
We
don't
want
to
be
too
much
of
a
grouper
because
then
you
don't
have
the
level
of
understanding
that
you
might
want.
You
want
to
be
a
splitter.
Really
dig
down,
get
those
departments
right,
get
those
job
titles
right,
get
those
regions
right,
because
you
can
always
group
things
together
later
to
understand
the
survey
results,
but
you
can't
split
them
once
you've
collected
that
data.
This
one
is
probably
the
most
important,
is
it's
incredibly
valuable
to
get
management
buy-in
and
then
develop
a
team
to
help
administer
that
survey.
You
need
people
that
people
are
going
to
listen
to.
If
you
have
someone
in
your
company
who
is,
for
lack
of
a
better
term,
not
well
liked
and
they
send
out
a
survey,
people
probably
aren't
going
to
be
as
likely
to
participate
as
if
you
have
a
team
of
3-5
people
that
are
leaders
within
the
departments
or
leaders
within
their
organizations,
and
people
are
going
to
hear
that
and
listen
to
that.
It
helps
even
more
when
you
have
management
saying,
you
need
to
do
this.
The
flip
side
of
this
is
when
you
get
the
data
back,
you
want
management
to
be
involved.
You
want
someone
that
has
some
decision-
making
capabilities
to
see
the
results
and
understand
what's
going
on
so
that
they
can
help
craft
the
big
picture.
It's
great
when
JMP
usage
expands
from
the
bottom
up,
but
when
you're
trying
to
drive
something
at
an
organizational
level,
you
really
need
people
that
are
higher
up
to
help
drive
the
usage
from
the
top
down.
We
strongly
encourage
that
you
administer
this
organization
wide.
Ignore
JMP
usage.
A
lot
of
our
companies
have
email
list
group
of
strictly
their
JMP
users.
But
really,
that's
often
just
a
snippet
of
the
company.
We
have
found
people
and
human
resources
that
when
they
see
what
JMP
can
do,
like
I've
got
to
have
that.
We
don't
always
think
of
an
analytical
software
tool
as
being
something
that
maybe
HR
would
want
or
would
gain
benefit
from,
but
they
do.
By
understanding
where
your
entire
organization
is,
you're
going
to
be
able
to
make
better
decisions,
you're
going
to
be
able
to
make
better
support
calls,
and
you're
going
to
be
able
to
move
your
entire
organization
versus
just
moving
a
single
department
or
a
single
job
title.
Lastly,
I'm
sorry,
second
to
last,
lean
on
your
JMP
support
team.
They
administer
these
surveys
frequently.
They
know
how
to
interpret
the
results.
They
know
how
to
help
you.
Even
if
you
don't
want
to
use
them
to
provide
that
support,
if
you
have
an
internal
education,
maybe
you
want
to
build
internal
education
up
as
a
result
of
the
survey,
but
lean
on
them
to
help
guide
you
because
this
is
what
we
do.
We
support
companies
like
yours
to
help
them
build
analytical
excellence.
Lastly,
and
I've
said
this
a
few
times,
administer
the
survey
annually,
so
that
you
can
actually
track
your
progress.
It's
one
thing
to
have
an
analytical
snapshot.
It's
a
lot
better
to
have
an
analytical
time
series.
Collecting
that
data
annually
is
going
to
really
help
you
gage
is
this
successful?
Do
we
need
to
change
things?
Is
what
we're
doing
working.
Maybe
you
go
to
your
support
team
and
they
lead
the
training
and
you
don't
see
growth.
So
you
turn
to
your
internal
education
team,
or
maybe
the
flip
side
is
true.
We
just
want
you
to
be
better,
we
want
to
be
collaborators
with
you.
We
want
to
support
you
in
whatever
you
think
is
the
best
way
to
execute
that
plan.
I
will
close
with
a
call
to
action
[inaudible 00:24:26]
survey.
It
can
really
help
an
organization.
I've
seen
it
help
an
organization.
I've
been
able
to
administer
close
to
a
dozen
of
these,
and
all
of
them
have
resulted
in...
I'm
going
to
close
with
a
call
to
action.
Connect
with
your
JMP
support
team
and
complete
the
JMP Analytical Workflow Survey.
Being
able
to
understand
where
you
are
as
an
organization
versus
where
you
want
to
be
is
incredibly
valuable.
At
the
end
of
the
day,
our
goal
is
to
help
you
democratize
analytics,
help
you
have
one
version
of
the
truth,
help
you
make
analytically-
driven
decisions,
and
from
that,
gain
efficiency,
quicker
discovery,
and
save
money,
save
time.
This
survey
is
an
incredibly
powerful
tool
to
help
you
achieve
those
ends.
We
have
the
expertise
to
help
you
not
only
administer
the
survey,
but
interpret
the
survey
and
create
a
plan
to
then
make
decisions
for
training
support,
and
help
drive
you
from
where
you
are
to
where
you
want
to
be.
Thank
you
very
much.
