Welcome
to
our
JMP
presentation
on
Harnessing
the
Power
to
Look
Within:
How W
e
Used
JMP Live
to
Understand
the H
ealth
of
JMP
Use
at
our
company.
Before
we
start
getting
into
the
real
content
of
our
presentation,
we're
going
to
start
by
giving
you
guys
a
little
background
on
ourselves.
I
am
Isabel
and
this
is
Katie.
We
both
work
as
statisticians
at
Regeneron Pharmaceuticals
at
their
industrial
operations
and
product
supply
site,
or
IOPS.
The
primary
responsibility
of
our
site
is
to
manufacture
the
medicine
for
patients.
As
statisticians,
our
main
job
here
is
to
enable
the
site
to
use
statistics.
We
work
with
a
variety
of
groups,
from
groups
like
process
scientists,
manufacturing,
quality
control,
and
quality
assurance.
A
s
you
guys
know,
JMP
is
a
powerful
statistical
tool.
Over
the
years,
we
have
developed
our
JMP
program
as
part
of
our
strategy
for
expanding
statistical
thinking
at
Regeneron.
A
few
years
into
our
JMP
program,
our
management
came
to
us
and
asked
if
our
program
was
having
a
meaningful
impact
on
the
business
and
are
we
being
successful?
While
we
felt
that
we
were,
we
realized
we
didn't
have
any
objective
evidence
to
show
that
this
was
true.
We
didn't
really
have
any
meaningful
metrics
to
gage
the
health
of
our
program.
So
rather
than
just
take
a
couple
metrics
and
call
it
a
day,
we
decided
to
do
this
really
intentionally.
We
took
a
step
back
and
realized
that
we
needed
a
mission
and
vision
statement
for
our
JMP
program.
We
asked
ourselves,
what
are
we
trying
to
accomplish?
How
are
we
actually
aiding
in
our
team
and
company
goals?
First,
we
actually
reviewed
Regeneron's
mission
statement,
which
is
to
repeatedly
bring
important
new
medicines
to
patients
with
serious
diseases.
Regeneron
does
this
through
the
Regeneron
way.
There's
some
more
information
about
that
in
the
presentation
right
here
and
a
link
below
if
you
want
to
learn
more.
But
after
reviewing
all
of
this
information,
we
came
up
with
our
JMP
Program
Mission
and
Vision
to
help
to
accomplish
Regeneron's
mission
by
providing
the
site
with
the
tools
and
software
knowledge
to
effectively
and
efficiently
organize,
analyze,
and
report
data
that
will
guide
decision
making
and
process
understanding.
After
we
developed
our
mission
and
vision,
it
was
time
to
start
determining
our
metrics.
We
started
by
asking
ourselves
some
questions.
The
first
one
being,
what
benefits
are
we
actually
providing
to
our
business
for
Regeneron?
We
realized
that
the
biggest
benefit
to
the
business
is
really
coming
from
how
the
JMP
users
at
our
company
are
using
JMP.
So
then
we
started
to
ask
ourselves,
"Well,
how
are
we
supporting
those
JMP
users?"
Then,
what
must
we,
on
this
JMP
program
team,
excel
at
in
order
to
support
these
users,
and
how
should
we
develop
and
grow
to
continue
to
support
the
company?
This
resulted
in
us
finding
these
four
different
areas:
company
benefit,
JMP
user
benefit,
internal
processes
learning
and
growth.
We
realized
that
they
all
fit
into
each
other.
A
bit
of
like
a
leading
and
lagging
indicator
stuff
here.
We
have
our
learning
and
growth
of
our
JMP
team,
which
is
going
to
help
our
internal
processes.
If
our
internal
processes
are
running
well,
we
should
have
a
lot
of
benefits
to
our
JMP
users.
Finally,
if
our
JMP
users
are
using
JMP,
well,
there
should
be
a big
benefit
to
the
company.
So
with
these
four
different
areas,
we
identified
different
objectives
that
we
want
to
work
on
or
monitor.
We
mapped
out
similar
to
before,
the
relationship
between
these
objectives.
The
ones
at
the
bottom
are
for
the
learning,
the
pink
ones
in
the
middle
are
for
our
internal
processes,
the
green
are
for
our
JMP
users
and
the
top
there
on
the
blue
are
for
the
company
benefits.
As
an
example
here,
one
of
the
objectives
for
our
JMP
user
benefits
is
providing
custom
JMP
tools.
A portion
of
our
program
is
looking
at
the
development
and
implementation
of
JMP
custom
tools
through
JMP
scripts.
Some
examples
of
some
of
the
scripts
we
do
might
be
doing
new
and
new
outlier
tests
to
increase
the
functionality
of
JMP
to
our
Regeneron
JMP
users
or
automating
some
of
their
routine
analyzes.
We
can
map
the
relationship
how
this
directly
benefits
those
company
area
of
ensuring
high
quality
analysis
and
increased
efficiency.
Once
we
have
our
objectives
in
place,
we
sat
down
and
tried
to
figure
out
the
different
measurements
or
metrics
for
each
objective.
An
example
here,
again,
going
back
to
that
provide
custom
JMP
tools
objective,
we
started
to
look
at
the
number
of
custom
scripts
that
were
actually
available,
the
number
of
script
uses
and
the
utilization
rate
of
our
script
users,
what
percent
are
actively
using
the
scripts.
Once
all
that
was
done,
we
had
a
bunch
of
metrics
and
we
started
to
review
those
metrics
and
realized
we
didn't
have
the
data
for
a
lot
of
these
metrics.
This
actually
became
one
of
the
most
time-
consuming
parts
of
this
process,
was
identifying
the
different
data
sources
and
starting
to
collect
the
data
to
make
sure
we
had
these
robust
metrics
for
our
program.
Then
finally
we
were
able
to,
as
we
come
up
with
projects
and
initiatives,
map
those
to
the
objectives
so
we're
able
to
make
sure
that
these
projects
and
initiatives
are
actually
aiding
at
our mission
vision.
Once
we
had
all
of
that
outlined,
it
was
time
to
start
mapping
out
how
we
want
our
metrics
to
look.
Before
going
into
JMP,
we
thought
it
would
be
best
to
come
up
with
a
game
plan
of
how
we
want
things
to
look
at
the
end.
We
actually
drew
out
the
way
that
some
metrics
should
look.
This
is
a
great
way
to
make
sure
everything
is
going
to
look
good
in
the
end,
fit
together.
When
doing
this
part,
it's
going
to
be
really
important
to
maybe
look
online
and
review
some
good
practices
when
it
comes
to
things
like
accessibility
or
good
visuals
for
metrics,
keeping
in
mind
things
like
color
and
font
size
and
the
directionality
of
text
are
important.
Just
make
sure
that
all
of
your
users
can
view
your
metrics
and
understand
them.
At
that
point,
we
had
our
game
plan
for
how
we
want
our
metrics
to
look
for
our
JMP
program.
It
was
time
to
jump
over
to
JMP
and
start
working
these
out.
Katie's
going
to
demonstrate
how
our
flow
through
JMP
getting
these
metrics
together
and
then
publishing
to
JMP Live,
and
she's
going
to
be
using
that
example
we
talked
about
earlier,
which
is
looking
at
the
number
of
script
uses.
All
right.
Thanks,
Isabel.
Now
that
Isabel's
given
an
overview
of
the
hard
part,
the
time
where
we
have
to
do
a
lot
of
thinking
to
make
sure
that
we're
coming
up
with
meaningful
metrics,
I
get
to
do
the
fun
part,
which
is
to
demo
how
easy
this
can
be
from
start
to
finish
to
build
out
our
dashboard
in
JMP.
A
couple
of
things
we're
going
to
touch
on,
we're
going
to
touch
on
a
bunch
of
different
tools
at
a
pretty
high
level.
Hopefully,
it
brings
some
awareness
to
some
things
that
are
available
for
you
and
how
you
can
piece
it
all
together
to
get
from
start
to
finish,
from
your
data
to
your
JMP Live
report.
If
you
have
questions,
I
guess
this
is
recording,
so
we'll
have
to
take
them
at
another
time.
Some
of
the
things
we're
going
to
cover
in
the
overview
is
the
connecting
to
the
data
sources.
Like
Isabel
mentioned,
this
is
a
challenge,
is
identifying
which
sources
we're
going
to
connect
to.
We're
going
to
connect
to
a
couple
different
items
here,
one
being
a
text
file
and
one
being
an
Excel
file.
Data
cleanup.
Once
we
pull
that
data
in,
usually,
there's
some
kind
of
modification
and
cleanup
that's
involved.
We
can
do
that
all
within
the
JMP
data
table.
Then
we'll
summarize
and
visualize
the
data,
create
the
graphics
that
we
want
to
display
for
our
metrics,
and
then
easily
publish
them
to
JMP Live
so
that
we
can
share
them
with
other
users.
Once
we
have
completed
that
process
and
workflow,
there's
some
cool
tools
that
we
can
use
so
that
we
can
recreate
that
process
with
a
click
of
a
button
so
that
we
can
generate
these
metrics
on
a
routine
basis.
We
can
also
take
that
automated
script
and
workflow
and
package
it
in
an
add
in
so
that
we
can
share
it
with
other
users.
I'm
going
to
start
off
with
the
import
of
our
text
file.
If
I
go
to
open
and
I
navigate
to
where
the
file
is
located,
I
have
to
tell
JMP
that
I
also
want
it
to
display
text
files.
I
can
select
the
file
that
I'm
interested
in
importing.
The
first
one
that
we're
going
to
pull
from
is
called
our
usage
log.
We
have,
as
Isabel
mentioned,
our
validated
JMP
scripts.
These
are
scripts
that
people
can
access
through
an
add-
in.
But
every
time
they
use
that
tool
in
the
add-
in,
it
will
log
the
use
of
that
script
in
a
controlled
file.
So
this
is
called
our
usage
log.
If
we
open
up
that
file,
we
can
see
that
it's
imported
all
of
our
data,
as
we
would
expect,
into
a
JMP
data
table.
To
start,
we're
just
going
to
talk
through
some
of
the
columns
that
are
included
in
our
data
set.
We
have
the
script
name,
we
have
the
username,
the
JMP
version,
a
date,
timestamp,
and
the
data
tables.
This
is
all
of
the
things
that
get
logged
every
time
someone
executes a script
in
our
validated
script
environment.
Some
things
we
might
want
to
clean
up
here.
To
start,
I'm
going
to
modify
the
script
name.
I
know
that
there's
some
things
that
have
changed
with
the
naming
of
the
scripts
throughout
their
use,
so
I
just
want
to
clean
them
up
so
they
get
grouped
together
correctly
and
we
can
look
at
for
any
other
little
errors
too,
that
we
can
clean
up
along
the
way.
Here
we
have
our
list
of
script
names
and
then
if
we
need
to
recode
them,
we
can
select
them
over
here
to
change
the
name.
The
first
one
I'm
going
to
change
is
this
Old
Outlier
Test
Name.
We
had
originally
had
a
name
for
a
script.
We
changed
it
to
just
Outlier
Test.
I
want
to
make
sure
that
all
those
get
counted
together
because
those
were
technically
the
same
script.
I
can
just
rename
it
here,
rename
it
Outlier
Test.
Now
it
will
indicate
that
those
two
are
now
going
to
be
grouped
together.
Also,
just
looking
through,
I
can
see
that
there's
a
little
bit
of
a
typo
here.
That
simulation
has
a
capital
I.
I
don't
want
to
have
to
keep
cleaning
this
up
all
throughout
the
data
table
and
things
later
on.
I'm
just
going
to
tell
it
in
the
recode
that
I
want
it
to
correct
that
to
be
a
lowercase
I.
When
we're
happy
with
that,
we
can
hit Okay
,
you
can
see
it's
created
a
new
column
here
for
the
script
names
of
their
cleaned-
up
data
file.
I
don't
love
the
name
script
name
too,
so
I'm
just
going
to
grab
that
and
change
it.
Instead,
I
just
want
it
to
display
script
descriptive
enough
and
I
don't
have
to
explain
why
there's
a
two
in
front
of
it
when
we're
looking
at
our
metrics.
So
we'll
clean
that
up
now.
Now
we're
still
looking
at
the
rows
representing
each
use
of
our
script.
We
could
just
summarize
this
in
different
ways,
but
it
might
be
helpful
when
we
go
to
do
some
digging
into
this
data
later
on
to
also
have
some
information
related
to
the
different
departments
that
we're
running
our
script.
I
know
we
want
to
pull
in
that
information.
We're
going
to
go
to
our
next
data
table.
This
one
we're
going
to
import
is
an
Excel
file.
I
will
say
that
when
we're
routinely
running
these
metrics,
we
do
actually
connect
directly
to
the
data
mart
that
houses
this
data
so we
can
pull
the
most
update
version
of
this
file
anytime.
For
this
example,
we
had
to
pull
a
static
file
so
that
we
could
recode
for
privacy
and
security.
To
import
that
file,
I'm
going
to
click
on
the
file,
and
I
don't
think
you
always
have
to
do
this,
but
just
to
be
safe,
I'm
going
to
open
using
the
Excel
Wizard.
One
of
the
nice
things
about
the
Excel
Wizard
is it
gives
you
the
ability
to
preview
your
file
before
you
import.
Now,
I
know
that
this
file
I
created
this
file,
so
I
know
that
it's
a
nice
clean
file
with
the
right
headings
and
the
data
all
ready
to
go
just
as
I
would
want
it
to
import.
But
just
in
case,
a
lot
of
times
we
receive
files
from
different
people
in
all
different
formats
with
merged
headings.
One
of
the
nice
things
about
this
tool
is
you
can
make
some
of
those
modifications
to
clean
up
the
data
and
import
it
correctly
on
the
first
try.
There's
a
couple
more
options
if
we
click
Next.
If
you
have
any
hidden
rows,
you
can
tell
JMP
how
you
want
to
handle
it.
Again,
all
this
is
a
pretty
clean,
easy
file.
When
I
know
that
it's
how
I
want,
I'll
make
sure
my
sheet
is
selected
and
select
Import.
Just
to
take
a
look
at
this
data
set,
I
now
have
the
first
and
last
name
of
the
users,
the
username,
and
then
I
also
have
the
information
about
their
location
and
their
department.
It
would
be
helpful
if
I
could
get
this
information
merged
into
my
usage
log
file
so
that
I
can
break
down
my
analysis
on
the
usage
by
the
location
and
department.
I'm
going
to
take
advantage
of
the
update
tool.
There's
a
bunch
of
different
tools
available
in
the
tables,
the
Tables
menu
here
that
allow
you
to
make
modifications
and
manipulations
to
your
data
table.
I
suggest
browsing
through
it
when
you
have
a
lot
of
data
cleanup
to
do
or
trying
to
get
creative
combining
different
files.
But
this
one's
pretty
simple.
We're
just
going
to
go
to
the
update
because
we
would
like
to
update
this
file,
the
usage
log
with
information
from
the
HR
roster.
So
I
selected
my
HR
roster,
and
now
I'm
just
going
to
tell
JMP
that
I
want
to
update.
With
selected
columns,
I
don't
need
all
that
information.
The
first
and
last
name
and
username
is
pretty
redundant.
I'm
just
going
to
grab
the
location
and
department.
Then
from
the
main
table,
I
don't
really
want
anything
to
update.
I
want
that
to
be
left
alone.
I
do
have
to
tell
JMP
how
I
want
to
link
up
the
rows.
I'm
just
going
to
indicate
that
I
want
the
usernames
to
match
by
row
and
we
can
hit
okay.
You
can
see
now
that
the
usage
log
table
that
we
imported
originally
has
been
updated
to
include
the
location
and
department.
Now
that
I
have
the
information
that
I
want
all
in
one
place,
I
can
start
to
play
around
with
the
different
graphics
for
the
metrics.
So
here
I'm
going
to
go
to
Graph,
Graph
Builder,
which
is
a
great
tool
to
drag
and
drop
whether
you
know
what
you're
looking
for
or
if
you
like
to
explore
different
ideas.
It's
really
quick
and
easy
to
make
adjustments
and
changes
to
get
a
feel
for
how
you
want
your
metrics
to
be
displayed.
For
this
first
example,
we're
just
going
to
summarize
by
department.
I'm
going
to
grab
the
department
group
and
put
it
down
here
at
the
X.
Pretty
straightforward,
just
a
simple
bar
chart.
It'll
pull
up
by
default,
but
I
can
see
overall
the
amount
of
use
between
the
different
departments.
Maybe
I
want
a
little
more
granularity
to
this
or
more
information
on
this.
Maybe
I
want
to
see
what
this
looked
like
year
over
year.
Has
this
been
consistent
year
over
year?
A
quick
change
that
I'll
make
is
that
I'm
going
to
add
an
overlay
by
year.
Notice
we
don't
have
a
year
column
available.
One
thing
we
could
do
is
go
back
to
our
data
table
and
create
it.
But
we're
already
too
far
along.
So
I
think
I'm
just
going
to
create
a
temporary
column,
which
I
can
do
by
just
right
clicking
on
the
date
time
column.
Going
to
date
time
and
selecting
year.
Now
it
creates
a
temporary
column.
Notice
it
didn't
add
it
to
the
full
data
table,
but
I
can
use
it
here
as
an
overlay
in
my
graph.
So
quick
and
easy
click
modification.
Now
I
just
have
a
little
bit
more
information
of
how
the
uses
compare
from
one
year
to
the
next.
You
can
see
that
the
overall
drop
for
Department
2,
there's
been
a
drop
for
Department
2
with
the
use
of
the
script
in
general.
Maybe
we
would
dig
into
that
a
little
bit
more.
But
I
think
for
now,
I'm
happy
with
how
that
graph
came
out
so
I'm
just
going
to
hit
Okay
to
get
rid
of
that
control
panel
and
the
first
one's
done.
Another
thing
we
might
want
to
look
at
is
the
use
of
the
scripts
over
time.
I'll
go
back
to
my
Graph
Builder.
Then
I'm
just
going
to
putâ¦
Before
I
just
throw
the
date
time
variable
on
the
x-axis,
I'm
going
to
create
another
category.
I'd
like
to
see
it
summarized
by
week
over
year, year
and
then
the
week.
So
I'm
going
to
go
to
the
date
time
again,
create
a
temporary
column,
and
select
your
week.
If
I
take
that
and
put
it
on
the
x-axis,
you
can
see
that
it's
in
sorted
order.
We
have
the
amount
of
use
of
each
of
the
scripts
for
each
week
of
the
year.
So
a
total
count
of what
the
use
is.
It's
getting
a
little
messy
with
the
bars.
It
might
be
more
appropriate
to
use
a
line
in
this
case
than
when
it's
like
the
line
icon.
You
can
create
combinations
of
these
if
you
want
some
overlaying.
Sometimes
it
makes
your
charts
too
busy.
We
have
a
lot
of
information
on
here
already,
so
I
think
I'm
going
to
leave
it
as
is,
but
there's
a
lot
of
cool
options
up
there.
Now
what
we
have
is
just
a
line
that
shows
the
overall
use
of
the
scripts.
But
what
I'd
really
like
to
know
is
if
there's
any
trends
or
changes
that
we're
seeing
from
one
script
to
the
next.
I
want
to
use
my
newly
created
column
and
put
that
in
the
overlay.
Now
I
have
a
separate
line
for
each
of
the
scripts
over
time,
I'm
going
to
click
Done,
because
I
think
I
have
everything
laid
out
the
way
that
I
want.
But
it's
also
four
years
worth
of
data.
Maybe
that's
not
what
I
want
to
be
looking
at
just
as
a
snapshot.
To
give
myself
the
option
to
filter
on
the
date,
I
am
going
to
go
and
add
in
a
local
data
filter.
If
you
click
on
the
lyric,
L
ocal
Data F
ilter,
we
can
choose
from
the
list
of
columns
that
we
have.
But
again,
I'm
going
to
create
that
temporary
column
to
include
the
year.
If
I
click
the
year,
plus
sign,
now
I
have
the
ability
to
filter.
One
of
the
things
I
don't
like
is
that
it's
on
this
continuous
scale
where
it'd
be
much
easier
if
I
could
just
click
on
the
bars
associated
with
each
year.
I
can
actually
go
and
tell
JMP
that
I
want
this
temporary
column
to
have
a
modeling
type
that's
nominal
ordinal
and
now
I
can
apply
the
filter
so
I
can
just
look
at
2023
if
I
want
to
look
over
the
past
year.
If
I
want
to
look
over
the
past
two
years,
I
can
select
both.
But
it's
a
nice
feature
to
drill
down
into
the
different
sets
of
data.
So
now
I
have
my
two
graphs
for
my
metrics,
but
it's
not
super
helpful
if
I'm
the
only
one
who
can
see
them.
This
is
where
we're
going
to
publish
them
to
JMP Live
so
that
we
can
share
with
other
users.
To
publish,
you
just
go
to
the
file
menu,
select
Publish
and
Publish
Reports
to
JMP Live
just
a
second
while
it
gets
things
ready,
and
then
we
can
select,
in
this
case,
I'm
going
to
publish
both
of
these
reports
that
we
just
created.
Next,
I
can
select
the
location
where
I
want
them
to
go.
I've
created
a
file
for
JMP
Discovery,
so
that's
where
we're
going
to
place
them.
If
I
need
to
rename
anything,
we
can
do
it
here.
I'll
leave
them
default
for
now.
Also,
if
you
wanted
to
add
any
descriptions,
any
clarifying
information,
a
lot
of
times
with
metrics,
there
might
be
little
nuances
to
how
you
summarized
or
calculated
the
data.
You
can
bring
clarity
to
any
of
that
using
the
description
field.
When
we're
ready,
we'll
hit
Publish.
Now
it's
successfully
published
the
reports.
If
we
want
to
go
see
it,
we
can
click
the
Open
buttons.
We'll
do
that
just
to
go
take
a
look.
Here
is
our
first
post
that
came
up.
You'll
notice
that
it
matches
the
graph
that
we
just
created
using
the
Graph
Builder.
Now
it's
published
there
for
everyone
to
see.
If
anyone
wants
to
go
and
check
on
the
metrics
related
to
department
that's
available
as
well.
Copy
this.
That's
the
workflow
to
how
to
get
from
our
data
set
all
the
way
to
our
JMP Live
report
from
start
to
finish.
But
typically
we
don't
just
do
a
one-
and-
done
metric
analysis.
It's
something
that
we'd
like
to
repeat
on
a
routine
cadence
and
make
sure
that
we
have
up-
to-
date
data
and
are
discussing
with
our
team
routinely.
So
ideally,
we'd
like
to
make
this
super
easy
to
rerun
that's
where
the
JMP
workflow
can
really
save
us
some
time.
I'm
going
to
close
out
of
all
this
just
to
clean
up
my
files
a
little
bit.
Don't
need
to
save
it.
One
of
the
things
that
I
did
before
we
started
the
presentation
was
open
up
a
new
workflow.
If
you
open
up
a
workflow
and
turn
on
the
Workflow
Builder
by
just
clicking
the
red
button,
it
will
actually
record
all
the
tasks
that
you
perform
within
JMP
in
the
sequence
that
you
performed
them.
Pretty
neat
tool
because
I
really
didn't
have
to
do
anything
to
build
out
this
automated
workflow
other
than
turn
on
the
button.
To
create
a
new
one,
you
would
just
go
File, N
ew
and
Workflow.
One
of
the
things
you
notice
that's
also
a
nice
feature
is
this
JMP
Log
History.
You
see,
I
have
a
blank
workflow
here
because
I
haven't
actually
recorded
anything,
but
maybe
I
had
performed
steps
that
I
intended
to
record
but
just
forgot
to
hit
the
button.
It
actually
has
a
log
of
all
of
your
activity
stored
down
below.
You
can
always
go
in
and
then
drag
these
up
to
the
top
if
you
actually
wanted
to
include
them
in
your
recording.
I'm
going
to
close
out
of
this
one
and
just
to
walk
through
quickly,
this
is
a
summary
again
of
the
steps
that
we
had
just
performed,
including
the
recode.
We
did
our
import,
we
did
our
recode,
even
just
changing
the
name
of
the
column
to
Script
to
make
sure
that
that's
consistent.
The
importing
of
the
HR
roster
from
Excel,
updating
the
data
table,
building
out
our
reports
and
then
publishing
to
JMP Live.
We
even
have
closing
up
those
data
tables
that
was
performed
at
the
end.
One
of
the
nice
things
too,
if
you
need
to
make
any
modifications
to
these
or
you're
trying
your
hand
at
some
JMP Scripting,
you
can
double-
click,
it
will
actually
open
up
to
link
you
to
the
JMP
script
that's
included
in
order
to
perform
that
task.
So
if
you
need
to
make
any
modifications
or
adjustments
or
you
just
want
to
copy
out
this
script
to
put
into
another
location,
it's
all
available
right
here
and
super
easy
to
pull
or
modify.
The
last
thing
I
want
to
add
in
here
is
that
if
you
do
have
any
custom
code
that
you'd
like
to
include,
you
can
click
on
the
drop-
down
up
here
and
say
Add
a
Custom
Action.
Here,
you
can
type
out
any
code
that
you
would
like.
Maybe
at
the
end,
after
closing
all
these
tables,
I
wanted
to
actually
open
up
the
JMP Live
web
page.
I
could
just
paste
in
the
web
function,
paste
in
the
URL
and
then
when
I
execute
this
step,
it
will
actually
go
and
open
up
the
web
page
with
our
analysis.
That's
building
it
out
start
to
finish.
Then
one
thing
we
can
do
from
here,
I
had
mentioned
it
would
be
really
nice
if
we
could
package
this
so
that
we
can
share
it
with
other
users.
I
may
not
be
the
only
person
that
needs
to
run
and
update
these
metrics.
If
you
click
on
the
layer
up
here,
you
can
save
all
of
this
entire
workflow
and
all
of
the
steps
associated
with
it
into
a
single
script
file.
You
can
either
save
this
file
and
share
it,
or
you
can
take
advantage
of
the
JMP A
dd-I
ns
and
package
the
script
right
within
the
Add-I
n
Menu
so
that
people
can
run
it
right
from
their
JMP
desktop.
All
they
have
to
do
is
double-
click
to
install
the
Add-I
n
and
they
would
be
able
to
find
it
along
with
all
the
other
menus.
I
have
one
for
JMP
Discovery
and
they
can
re-
execute
the
metrics
by
just
clicking
a
button.
Those
go
through
all
the
steps.
If
you
are
savvy
with
JMP Scripting,
you
could
make
some
of
these
flashing
tables
and
things
invisible.
You
can
incorporate
a
lot
of
your
own
custom
items
and
there
they
are,
brand
new
reports,
updated
just
a
few
seconds
ago.
There's
our
department
bar
chart
and
then
our
use
over
time.
I know
that
was
a
quick
overview,
but
we
touched
on
a
lot
of
different
things
at
a
high
level
and
tried
to
illustrate
how
we
could
connect
them
all
together.
Starting
with
our
data
source,
cleaning
up
the
data
files,
building
out
our
visualizations
and
summaries,
and
then
publishing
to
JMP Live.
Optional,
if
you
wanted
to
turn
on
that
workflow
builder,
you
can
record
all
those
steps
so
you
can
repeat
them
easily
later
on,
and
you
can
package
it
up
in
an
add-
in
so
that
you
could
share
with
other
users
to
run
the
metrics
as
well.
Anything
to
add
before
we
wrap
it
up,
Isabel?
Nope.
Thanks,
Katie.
We
hope
you
guys
learned
a
lot
about
identifying
metrics
and
actually
implementing
your
metrics
and
visuals,
using
JMP
and
then
publishing
to
JMP Live.
Thanks,
everybody.
