Today
we're
going
to
cover
the
JMP
Analytic
Workflow,
so that you can see firsthand
how all these capabilities
come together.
Anybody
who's
doing
data analysis
has
a
shared
objective.
They're
trying
to
take
raw
information,
data,
and we're trying to turn
that into
a
shareable
insight
or
an
actionable
insight.
The
only
thing
that's
different
is
the
steps
that
we'll
take
as
we
move
from
one
end
of
this
process
to
the
other.
Whether
you're
new
to
the field of
analytics
and
statistics
and have more simpler needs,
or
whether
you're
a
more
advanced
practitioner
with more sophisticated needs,
JMP
Software
offers
the
flexibility to meet those needs
wherever
you
are
in
your
analytics
journey.
The
JMP
Analytic
Workflow
is a quick and easy
set of analytical capabilities
to
bring
you
from
data
to
insights.
What
we're
going
to
cover
is
a
few workflows
so
that
you
can
see
how
this
can
be
implemented
in
practice.
I
want
everybody
to
just
picture
right
now
that
we're
responsible
for
this
machine,
and
this
machine
produces
product
for a
business.
Recently,
the performance of our product
has been outside of our expectations,
and
we
believe
the
answer
for
what
is
going
on
can be determined by analyzing
some of the data
that's
available
on
this
machine.
So
what
we're
going
to
do is
we're going to build an analytic workflow
to see if we can figure out
what's
happening
with
this
issue.
Building
the
workflow
involves
these
three
steps.
The
first
step
is
having
an
understanding
of
the
data.
The
data
that
we'll
be
using
in
this
case
are
machine
logs
that
are
saved
on
the
machine
as
Excel
files.
The
second
step
relates to
the
analytical
capability.
In
order
for
us
to
know
which
analytical
capability
we
need
to
use
in
the
workflow,
we
have
to
understand
the
question
that
we're
trying
to
answer,
and
the
question
that
we're
trying
to
answer
here
is
a
simple
one.
What
is
happening
with
this
machine?
The
third
element
is
a
shareable
insight.
So
after
we
get
an
answer
to
this
question,
we are going to have to
share
that
insight
with
others.
In
this
case,
we're
going
to
be
sharing
our
insights
with
management,
and
we're
going
to
be
sharing
those
findings
as
a
Word
document.
So
more
specifically,
when we take a look at the workflow,
we're
going
to
be
working
with
Excel
files.
We're
going
to
leverage
JMP's
data
access
platform
to bring the data into JMP.
Once
the
data
is
in
JMP,
we're
going
to
perform
some
data
exploration
and
visualization,
and
then
lastly,
we'll share and communicate those results
as a business document,
which
in
this
case
will
be
a
Word
document.
So
to
begin
the
process,
we
open
our
Excel
file.
When
you
open
Excel
files
in JMP,
it
opens
a
special
tool
called
the
Excel
Import
Wizard.
And
the
Excel
Import
Wizard
allows
us
to
do
many
things.
We
can
access
different
worksheets
in
the
Excel
file,
and we can perform some very simple
data cleaning steps
before
we
import
the
data.
We
can
also
preview
the
data.
So
as
I
look
at
the
preview,
I can see that I have information from
June of last year,
and
I
can
see
that
I'm correctly capturing
the
measurements
from
our
machine.
I
can
import
this data
now
into
JMP,
where
we
have
a
JMP
data
table.
Now
that
we
have
our
data,
we
can
perform
a
visual
exploration.
I
will
use
the
Graph
Builder
tool,
which
is
available
under
the
Graph
menu.
And
I'll
plot
our
measurements
over time
for
our
piece
of
equipment.
And
as
I
plot
our
measurements
over
time,
I
can
begin
to
see
something
that's
quite
surprising.
The
performance
of
our
machine
was meeting expectations initially,
but over time you
can
see
that
the
performance
has
slowly
drifted,
and
now
we're
into
a
region
where
we're
performing
and
producing
bad
material.
This
is
the
first
time
that
we've
now
used
data
and
analytics
to
understand
what's
happening
in
our
process.
And
what
we're
seeing
here
is
that
the
machine
has
actually
been
performing
for
quite
some
time
without
a
calibration.
So
if
we
can
calibrate
the
machine,
we can get it back to
the original performance
for
what
we
need
in
order
to
produce
stable
material.
So
this
is
quite
a
significant
finding.
And
now
we
want
to
share
this
finding
with
our
management
so that we can take that
additional action,
which
is
to
perform
the
calibration.
When
it
comes
time
to
share
this
insight,
we can just simply export this,
and
in
this
case, we're going to
export
it
as
a
Word
document.
So
we're
going
to
capture
that
Word
document,
and
we're
going
to
share
that
now
with
our
management.
So
here
we
have
the
Word
document.
We've
captured
that
visual,
where they can see
exactly what we saw in JMP,
and
they
can
see that
the
performance
of
the
machine
has
been
drifting
over
time,
and that a calibration
needs to be performed.
This
also
represents
the
first
time
that
management
is
starting
to
use
analytics,
and
they're
now
starting
to
see
the
value
of
data
in
their
organization
and
how
that
can
help
them
improve
their
business
decision
making.
And
they
have
a
new
ask for us.
They
want
to
know
what
else
can
be
done
with
their
data,
and what else can be done
in terms of analytics
to
improve
their
manufacturing
processes.
So
now
the
analytics
journey
has
evolved,
and
JMP
is
very
much
a
part
of
that
journey.
It's
not
just
a
tool
that
offers
you
the
ability
to access individual
analytical capabilities,
but
it's
also
very
much
a
part of the process
so
that
you
know
how
and
when
to
implement
certain
strategies.
So
you
spend
some
time
reviewing
a
variety of JMP resources,
and
you
review
white papers
to
learn
about
best
practices.
You
also
read
through
customer
success
stories
to
see
how
others
in
your
industry
are
leveraging
analytics
and
how that's
improving
their
business.
You
also
participate
in
a
complimentary statistics course
where
you
learn
about
many
things.
You learn about predictive modeling,
and
how
that
can
help you
root
cause
production
issues.
You
learn
about
reliability
analysis
and
how
that
can
help
you
understand
how your
product
is
going
to
perform
in
the
field
over
time.
But
one
of
the
most
informative
things
that
you
learn
about
is
the
field
of
quality
analytics.
And
by
taking
that
learning
now,
you
apply
that
to
exactly
what
you're
responsible
for
in
your
process.
So
with
this
new
learning
now
you
have
a
more
advanced
analytic
workflow.
So
here
in
our
more
advanced
workflow,
we
have
some
new
iterations.
Now,
data
is
no
longer
being
stored
and
accessed
as
Excel
files.
All
the
data
is
centralized
into
a
database
so that the integrity of the data
is never affected,
but
also
so
that
everybody
can
access the data
without
having
to
work with
these
individual
files.
In
terms
of
analytical
capabilities,
you
now
have
a
better
understanding of
what
statistics
can
do
and
what
analytics
can
do.
So
your
questions
are
more
refined
and
specific.
The
question
that
you
want
to
answer
now is,
is
the
machine
experiencing
special
cause
variation?
Because
you've
learned
about
the
difference
between
common cause variation
and special cause variation,
and you
know
that
it's
a
special
cause variation
that
ends
up
being
problematic
to
your
processes.
And
the
last
thing
is
the
shareable
insights.
Before,
when
you
were
sharing
your
reports
as
Word
documents,
what
it
was
doing
was
it
was
creating
a
lot
of
additional
work
for
yourself.
As
people
were
consuming
these
reports,
you're
now
getting
inundated
with
requests
to
make
modifications
to
graphs.
You're
also
getting
inundated
with requests
to
the
location of
the
most
recent
outputs.
And
then
so
what
you
want
now
is
a
better
tool,
one that allows you to centrally store
all those reports in one location,
but
offers
the
people
who
are
consuming those reports
additional
capabilities,
so that they can perform
their own exploration
without
having
to
come
back
to
you
for
additional
requests.
So
the
analytic
workflow
that
we're
preparing
now
involves
these
steps.
Our
data
now
begins
by
being
accessed
from
a
database.
We
leverage
JMP's
database
utility
to
get
access
to
the
data
imported
into
JMP.
We
continue
our
data
exploration
and visualization,
but
we
also
incorporate some
quality
and
process
engineering elements
that
we've
recently learned about
from
JMP
resources.
And
when
we
share
analyses,
we
want
to
both
manage
the
content
but also share these analyses
with the wider audience
in
a
way
that's
going
to
offer
them
greater capabilities,
which
they
weren't
able
to
do
with
their
Word
documents.
And
then
so
we'll
be
using
the
JMP
Live
platform
to
do
this.
So
we
begin
the
process
by
accessing
our
data.
We
use
JMP's
built-
in
query
builder tool
to
access
our
data
connection.
Once
we're
connected
to
the
database,
we
can
access
any
of
the
data
tables.
Here,
we've
selected
the
data
table
that
contains
the
data
that
we're
interested
in.
And
now,
unlike
before,
we're able to pull data
from everywhere in the factory,
not
just
on
an
individual
piece
of
equipment.
We
import
the
data
into
JMP,
and
we
can
now
perform
our
new
analysis,
which leverages some new tools
that we've learned about
under
the
Quality
and
Process
module
in the
JMP.
In
order
to
get
an
answer
to
the
question
that
we
have,
it
will
require
us
building
a
control
chart.
The
control
chart
allows
us
to
set
up a visual
that
looks
very
similar
to
what we
created
before,
but
the
control
chart
allows
us
to
access
some
additional
capabilities
that
we
weren't
able
to
do
in
just
a
normal
graphical
visualization.
Built
into
the
control
chart
are
some
rules
that
we
can
leverage
to
determine
if
we're
experiencing
some
special
cause
variation.
And
that's
the
question
that
we're
trying
to
get
answered.
So
we
will
enable
some
warnings,
which are some special customized tests,
to signal to
us
if
we
are
experiencing
special
cause
variation.
Now
that
we've
turned
on
that
test,
we can see that there are
many batches where
we're facing special cause variation.
And
had
we
been
monitoring
our equipment using this tool,
very
early
on
in
the
process
we could have detected that
there was an issue
and
we
could
have
taken
the
appropriate
action.
So
this
is
quite
a
significant
finding,
and
this
is
something
that
we
want
to
share
with
a
wider
audience.
So
now
when
we
share
this
report,
we
share
it
with
the
JMP
Live
tool.
So
we're
going
to
publish
this
to
JMP
Live.
So
I'm
going
to
connect
with
my
account
to
JMP
Live,
and
I'm
going
to
create
a
new
post.
And
I'm
going
to
share
this
post
with
everybody
on
my
equipment
team
who
's
interested
in
these
results
and needs to know these
new
insights
that
we've
just
discovered.
I'm
going
to
publish
this
report
to
our
JMP
Live.
And
now
we
can
take
a
look
at
that
report
in
JMP
Live.
So
JMP
Live
is
a
web-
based
tool
that
allows
anybody
to
access
the
report
from
their
browser.
So
here
now
we
can
see
that
report
in
JMP
Live.
JMP
Live
also
allows
for
all
the
reports
to
be
centralized.
So
we
no
longer
have
to
pass
and
share around
static
documents
and
Word
docs
,
where sometimes people can be consuming
old results
and
not
be
up
to
date
with
the
latest
findings.
Now
because
everything
is
centralized
in JMP Live,
there's
one
version
of
the
truth,
and
you'll
always
have
access
to
the
most
recent
files.
You
can
also
do
things
tha
t
you
would
not
be
able to do
in
static
versions
of
the
analyses.
So
JMP
Live
is
still
very
interactive,
and
anybody
consuming
the
report
can perform
their
own
exploration
and
get
answers
to
their
own
questions
without
having
to
come
back
to
you,
the analysis,
the person who prepared the report,
for
additional
modifications.
And
as
management
is
consuming
this
result
and they're getting additional value,
a
very
often
thing
is
occurring,
and their needs are now changing.
And
instead
of
seeing
this
information
once
a
week in a weekly report,
they
want to see
this
information
more
rapidly.
They
want
to
see
this
information
daily or
even
hourly,
and
they
don't
want
a
chart
for
just
one
piece
of
equipment.
They
want
a
chart
like
this
for
every
piece
of
equipment in the factory,
because
they
recognize
how
powerful
this
analysis
is,
and they ask us,
"Is
there
a
way
that
we
can
do
this?"
And
JMP
tool
offers
that
flexibility
to
do
this,
because
a
critical
part of
the
JMP
Analytic
Workflow
is the ability to automate.
As
we
were
building
these
analyses,
in the background,
JMP
was
actually
capturing
the
JMP scripting language
to
automate
all
of
these
steps.
So
by
simply
saving
the
script,
we
can
stitch
together
all
of
these
actions.
We
can
stitch
together
the
action to
connect to the database
and
import
the
data.
We
can
stitch
together
the
action
to
generate
the
chart,
and
we
can
stitch together the analysis
to
upload the analysis to JMP Live.
And
in
the
click
of
a
button,
we can have those analyses
automatically created by JMP.
And in our case,
we want these analyses produced
every hour
so
we
can
leverage
the
Windows
Task
Scheduler
to automatically run the script
on our behalf,
so
that
we
don't
even
have
to
do
it
manually.
So
very
quickly,
you've
seen
a variety of different examples of how
the
JMP
Analytic
Workflow
can
be
leveraged
to
solve
a
variety
of
different
problems,
depending
on
where
you
are
in
your
analytic
journey.
We
can
put
together
the
workflow
to
save
both
time
and
effort.
We
can
easily
access
data
from
a
variety of different sources
and
share
your discoveries
with
other
team
members.
We
can
get
more
from
your
investment.
We
can
increase
your
efficiency
without increasing the head count,
and
also
eliminate
the
need
for
multiple
tools.
We
can
remove
barriers
in
complexity.
We
can
tackle
problems
of
any
size,
like
we've
seen
today,
by
using
JMP's
extensive suite of
analytical
platforms
And we can accelerate process improvement
by leveraging the automation
to reduce time
spent
on
repetitive
tasks
and get to those
actionable insights faster.
As
we've
seen
firsthand
today,
your analytical needs
might start off as being very simple,
but
when
you're
ready
to
grow,
we'll
be
ready
for
you.
