Have
you
gone
back
to
your
analysis
and
tried
to
figure
out
what
you
were
doing?
Maybe
the
analysis
was
done
six
months
ago
and
you're
trying
to
go
through
your
analysis
and
trying
to
figure
out
what
was
this
metric
again?
How
did
I
define
it,
what
were
the
assumptions
behind
it?
Now
think
about
your
audience.
If
they're
looking
at
something
that
you
don't
necessarily
understand
or
they
may
have
want
a
little
bit
more
nuance
about
what
the
definition
of
a
metric
is
in
the
analysis.
Well,
the
great
thing
is
we
can
do
that.
There
are
things
called
data
dictionaries
that
we
can
be
utilizing.
What
is
a
data
dictionary?
A
data
dictionary
is
really
a
catalog
that
communicates
the
structure
and
the
content
of
the
data.
More
importantly,
what
we're
going
to
be
talking
about
today
is
really
meaningful
descriptions
around
what
the
metrics
are
so
that
you
and
your
audience
can
understand
what
is
contained
in
that
analysis.
The
purpose
of
this
is
really
to
be
able
to
over
communicate
so
that
everyone's
on
the
same
page
of
what
the
variables
or
metrics
are
and
how
they
were
calculated
or
any
other
important
information
to
help
either
your
audience
or
your
future
self
understand
and
document
what
those
metrics
are.
We're
also
going
to
be
showcasing
how
to
be
able
to
centralize
these
definitions
in
one
place
and
be
able
to
digitalize
this
content.
What
I
mean
by
digitalized
is
just
like
you
take
data
from
raw
form
and
being
able
to
present
that
into
a
graph
or
an
analysis
for
people
to
consume,
we're
going
to
do
the
same
thing
with
a
data
dictionary.
We're
going
to
be
able
to
pull
the
data
dictionary
to
be
able
to
be
tied
into
your
analysis
so
that
your
audience
or
yourself
do
not
have
to
go
find
where
that
information
is.
It's
all
tied
together
for
easy
user
experience.
Okay,
so
today
we're
going
to
talk
about
I
have
three
options
here
for
folks.
The
first
solution
is
just
really
a
simple
no
code.
If
you're
one
of
the
people
who
love
the
GUI
around
JMP
and
you
do
not
want
to
have
anything
with
code,
this
is
your
solution.
Let
me
walk
you
through
a
quick
way
of
being
able
to
do
this
so
that
you
can
use
the
data
dictionary
on
concept.
The
second
part
is
if
you're
into
JSL
and
you
have
a
very
simple
data
dictionary
that
you
want
to
have
and
be
able
to
tie
on,
I'll
show
you
how
to
do
it
with
a
little
dose
of
JSL.
Now,
if
you're
a
supercharger
and
really
want
to
see
JSL
in
all
its
glory
and
being
able
to
essentially
be
able
to
have
a
way
of
being
able
to
categorize
your
work
along
with
defining
the
metrics
as
you
go
along
and
then
be
able
to
centralize
that,
really
be
able
to
digitalize
the
data
dictionary
and
then
be
able
to
pull
out
exactly
what
you
need
for
a
given
report.
We'll
do
this
with
the
supercharged
full
out
JSL.
Then
we'll
talk
about
a
call
to
action
at
the
end.
Let's
talk
quickly.
Let's
just
JMP
in
here
and
talk
about
the
simple
no
code
option.
First
we're
just
going
to
be
using
a
JMP
data
table,
and
then
we're
going
to
embed
the
definitions
into
that
data
table.
Let
me
open
up
the
data
and
the
definition
table.
Here's
what
a
data
dictionary,
a
very
simple
data
dictionary
looks
like.
You
have
the
variables
and
a
description,
which
each
of
these
variables
are
columns
here
in
the
data
table.
You
have
the
variable
or
the
column
name
along
with
the
meaning.
I
also
added
the
references
over
here.
We
can
come
over
here
and
just
plot
our
data
and
we
can
see
what
the
various
different
metrics
look
like
in
here.
But
maybe
your
audience
comes
here
and
goes,
what
does
abundance
mean?
Or
how
are
we
defining
richness?
Or
what's
the
Shannon
Metric?
Well,
they're
going
to
have
to
go
and
figure
out
what
that
actually
means.
What
we
can
do
here
is
we
can
tie
this
data
table
to
the
data
dictionary
table,
to
the
data
table,
and
we
can
do
this
really
quickly.
If
you
go
over
to
this
triangle
over
here,
click
on
the
red,
go
down
to
copy
data
table.
We
can
come
over
here,
do
the
same
thing
over
in
the
data
table.
But
this
time
we're
going
to
hit
new
script
and
we're
going
to
type
in
definition.
We're
going
to
then
paste.
I
have
command
v
or
control
v
on
here.
You'll
see
some
code.
You
do
not
need
to
understand
what
that
actually
means.
You're
going
to
say
save
and
then
you're
going
to
say
okay.
You're
going
to
see
that
you
have
the
definition.
If
your
audience
or
your
future
self
wants
to
come
in
here
and
say,
hey,
here's
my
plot,
that's
great,
I
see
my
data,
but
I
need
to
know
what
the
definition?
They
just
need
to
hit
the
plot
and
voila.
You
have
well
formulated
your
definitions.
Okay,
very
simple.
Hopefully
if
somebody
has
a
data
table,
come
up
with
some
metrics,
use
another
JMP
data
table
for
it
and
just
attach
it
to
your
data
table
and
you
have
a
great
reference.
Okay,
so
that
is
the
simple
notebook.
Now
let's
go
to
simple
with
a
little
dose
of
JSL.
All
right,
so
here
we're
going
to
publish
the
analysis
along
with
the
definition
table
to
a
window,
and
then
we're
going
to
export
the
analysis
to
a
PowerPoint
deck.
What
I'm
going
to
do
is
I'm
going
to
run
the
script
and
then
I'll
walk
you
through
how
I
did
the
script.
The
first
thing
is
let's
get
this.
This
is
what
the
outcome
of
the
script
does.
You
see
the
same
analysis
up
here,
but
what
you
see
down
here
is
we
have
a
definition
table,
and
you
can
just
open
up
the
definition
table,
and
then
you
can
see
what
the
different
rows
are
or
the
definitions
of
the
different
meanings
of
the
data
down
here
along
with
the
reference
if
people
wanted
to
go
and
know
where
to
go
to
look
for
more
of
that
information,
How
did
we
actually
do
this?
Essentially
the
first
thing
we
did
was
basically
opened
up
the
data
tables
here
and
then
the
next
thing
we
did
was
we
created
a
window.
This
is
basically
this
window
right
here
and
then
an
outline
box
with
the
analysis.
This
is
just
a
simple
graph
builder,
nothing
fancy.
Then
down
here
we
did
another
outline
box
here
where
I
had
it
closed.
But
we're
going
to
need
to
keep
that
open
for
right
now.
Then
simply
just
did
a
data
table
box
that
just
basically
pasted
that
into
the
window.
Okay,
now
let's
say
that
this
is
great,
but
we
want
to
export
this
to
a
PowerPoint.
We
can
very
well
easily
do
that
by
making
sure
you
have
the
definition
window
open.
Don't
have
it
closed
or
it
won't
export
to
the
PowerPoint.
Make
sure
it's
open
and
then
just
say
save
to
PowerPoint.
Voila.
There
you
go.
It
exports
the
data
to
the
PowerPoint.
Now
there's
a
little
bit
of
things
that
you're
going
to
need
to
do.
You
can
make
this
bigger,
you
can
make
this
change
the
color
on
it.
The
titles
up
here,
make
that
white
just
so
it
makes
it
stand
out
or
change
it.
Then
also
here's
the
data
dictionary
as
well.
All
you
need
to
do
is
this
is
all
edible
so
that
you
can
actually
make
this
a
little
bit
more
user
friendly.
I
find
that
12
font
makes
it
better,
but
you
can
have
the
definitions
in
the
backup
of
your
slides
so
that
people
can
come
back
and
be
able
to
reference
it.
It
doesn't
have
to
be
part
of
the
meet
of
the
presentation.
It
could
just
be
in
the
backup
slides.
Makes
it
really
easy
to
be
able
to
export
all
this
information
to
PowerPoint
and
just
have
it
tied
with
your
analysis.
All
in
really
essentially
a
few
lines
of
code.
Basically
all
I
did
to
export
this
was
I
created
the
button
save
the
PowerPoint.
Just
basically
save
the
graph
and
then
save
on
one
page,
on
page
two
and
then
on
page
three
I
had
the
data
dictionary
part.
This
is
the
object
for
the
data
dictionary
and
this
is
the
object
for
the
graph.
Pretty
straightforward
to
be
able
to
do
that.
We're
going
to
close
all
these
slides
files
and
we
can
close
this
as
well.
Now
there
is
a
drawback
with
the
data
table
box.
I'll
get
into
a
little
bit
of
that
in
my
next
example
on
that.
In
the
supercharger,
I'm going to talk about the drawbacks on the data table function itself,
but
I'm
also
going
to
showcase
how
to
capture
dynamic
metric
definitions
as
you
do
a
report
and
then
also
giving
you
a
little
bit
more
detail,
how
to
get
more
details
around
that.
Then
I'm
going
to
showcase
how
to
do
a
dynamic
definition
box
around.
This
is
going
to
take
a
little
bit
more
coding
than
just
specifying
that
data
table
box.
Let's
talk
about
what
the
drawback
is
and
let's
take
this
data
dictionary
from
the
World
Bank.
You
can
see
that
they
have
some
really
long
definitions
on
here
and
we
have
some
more
information
around
these
various
two
metrics.
But
if
I
just
do
a
data
table
box,
what
you'll
see
is
it
doesn't
take
any
of
the
cell
height,
width
or
set
wrap
on
here.
This
is
something
actually
I
had
to
go
back
and
to
JMP
support
and
they
confirmed
that
using
the
data
table
box,
it
doesn't
capture
these
aspects.
This
isn't
ideal
on
here.
Now,
I
did
put
a
request
in
the
JMP
wish
list
that
they
would
take
this
so
that
we
wouldn't
necessarily
have
to
do
the
coding
that
I
will
be
showing
you
in
a
few
more
minutes.
This
isn't
necessarily
ideal,
but
basically
this
data
table
box,
there's
some
drawbacks
on
that.
The
way
that
I
got
around
to
it,
just
to
give
you
an
idea
of
just
using
the
table
is
I
literally,
let
me
just
show
you
this.
This
was
my
attempt
on
here,
recreating
it.
This
is
the
the
desired
outcome
and
it
took
a
lot
of
coding
on
here.
Just
to
give
you
an
idea,
I
mean,
you
could
try
to
do
this,
but
this
isn't
really
dynamic.
This
is
just
basically
you
would
have
to
explicitly,
you're
basically
coding
each
one
of
these
cells,
the
titles
and
then
each
one
of
the
text
boxes
down
here.
For
example,
I
took
this
indicator
name
from
the
first
row
and
put
it
here,
long
definition
from
the
first
row
of
the
data
table
to
here,
blah,
blah,
blah,
and
just
gone
on
and
on
and
on
and
on.
This
isn't
ideal
at
all.
It
requires
a
lot
of
explicit
coding.
Also
the
other
thing
I
noticed
is
you
can't
do
for
loops
within
the
display
boxes
at
all.
I
went
back
to
JMP
support
and
said
is
there
anything
that's
just
not
documented
of
how
to
get
the
display
boxes
or
the
cell
heights
from
the
data
tables
translated
back
over?
They
actually
gave
me
a
solution
and
I'll
show
you
how
we
go
through
that
in
a
few
minutes.
But
because
basically
the
data
table
box
does
not
allow
you
to
set
the
cell
height,
the
cell
width
or
the
wrap.
Let's
look
at
how
if
we
did
want
to
do
something
that
is
a
little
bit
more
dynamic
and
building
out
a
data
dictionary
from
your
analysis.
Here,
what
I've
done
here
is
really
as
I
was
going
through
building
metrics,
I
define
the
different
aspects
that
I
would
want
in
my
data
dictionary.
Here
I
have
initial
metric
for
the
name
of
metric,
the
definition,
the
source,
the
frequency
of
the
metric,
and
the
specific.
I
will
be
capturing
that
every
time
I
go
through,
I
create
a
new
metric.
Let's
say
I
work
at
the
World
Bank,
I'm
creating
these
metrics
and
this
data and so forth.
Imagine
I
have
a
whole
bunch
of
code
that
basically
develops
a
metric
and
so
forth,
takes
that
data,
does
stuff,
I
create
this
GDP
growth,
annual
percentage
type
of
thing.
This
is
essentially
how
I
basically
would
have
captured
the
information.
I
just
take
the
information
for
the
definition
name.
I
would
put
the
definition
up
here
in
a
separate
line
and
then
carry
that
as
an
object
in,
and
just
do
an
insert
into
and
then
insert
this
into
the
list
and
so
forth.
That
essentially
if
I
run
the
script
just
so
that
and
make
sure
I
don't
have
anything
highlighted,
I'm
essentially
taking
each
one
of
these
metrics,
and
I
have
four
metrics
down
here
and
just
basically
defining
each
one
of
these
metrics
with
the
different
information
as
we're
going
through
and
building
out.
One
of
the
things
I
did
put
in
here
is
this
report.
Up
here and
why
I
have
this,
and
this
is
important
is
maybe
you
want
to
have
an
idea
where
that
report
is
going
to.
Basically
this
metric
is
going
to
be
used
for
this
report.
These
metrics
are
going
to
be
for
that
report
so
that
you
can
centralize
all
your
definitions
in
one
place.
Like
you
can
go
and
put
this
into
a
database
or
consolidate
it
all
into
one
location
so that
everybody
can
have
access
to
that
information
and
understand,
have
one
working
definition
and
know
where
that
is.
If
you're
doing
multiple
reports,
to say
what
reports
those
are
in
and
then
also
know
if
they
need
to
make
changes,
which
reports
are
these
metrics
in.
It's
a
really
great
way
to
keep
track
of
things
and
also
keep
some
governance
around
those
names.
What
I
did was,
essentially
did,
as
you
can
see,
is
I
built
out,
I
just
basically
came
up
with
a
list,
and
then
essentially
for
each
one
of
these
lists,
I
just
set
those
values
into
that
column.
If
we
go
back
to
here,
each
one
of
these
objects,
which
is
a
list,
would
be
basically
everything
for
the
indicator
name.
There
should
be
four
things.
If
we
go
over
here
and
look
at
the
death
name,
and
I
go
to
my...
You
can
see
that
I
have
a
list
here
of
names
and
that
just
becomes
all
the
content
within
the
indicator
name.
That
was
just
basically
done
by
creating
a
new
table
and
then
just
setting
that
list
for
that
column.
I
did
that
for
each
one
of
those.
Then
once
you
have
these
all
consolidated
into
one
place,
you
can
then
just
go
save
this
to
a
database
or
some
central
location
so that
not
just
you,
but
anybody
else
can
get
it.
Then
you
can
also
pull
this
into
your
reports
based
on
what
you
want
to
have
done.
That's
what
we're
going
to
talk
about
next.
Let
me
just
close
this
out.
Go
back
to
...
Close
this.
Close
this.
All
right,
so
we
just
built
a
definition
and
now
let's
talk
about
kind
of
building
a
dynamic
analysis.
We
just
created
that
table
of
indicators.
What
we're
going
to
want
to
do
is
we
want
to
create
a
report,
but
we
don't
want
all
maybe
we
don't
want
necessarily...
maybe
we
have
like
100
indicators
and
now
we
just
want
just
a
few
of
those
indicators.
The
first
thing
we
can
do
is
just
basically,
we
can
open
up
these
data
tables.
Now
I'm
adding
in,
I
have
another
data
dictionary,
which
is
basically
the
different
countries
here.
I
have
the
data
dictionary
of
the
indicator
or
the
metrics
that
we
just
created.
Then
we
actually
have
the
data
over
here,
which
is
all
the
actual
data
along
with
the
countries
on
there.
But
we
want
to
create
a
report
with
the
definitions.
Once
we
do
that,
we
can
then
go
and
figure
out
we
can
get
the
values
of
the
variables
here
from
the
data
dictionary.
We
basically
are
going
to
pull
all
these
metrics
and
then
we're
going
to
combine
that
with
the
other
information
that
we
are
getting
from
the
actual
metric
definitions.
I
got
a
lot
of
things
going
on
here.
Basically
I'm
going
to
be
pulling
on
here
these
four
data
points.
But
I
also
need
to
be
from
this
I
want
to
also
be
pulling
this
information,
the
country
name,
the
country
code,
and
the
year.
I
basically
added
those
variables
in
there
and
I
did
another
insert
here.
That
essentially
I'm
just
pulling
the
columns
from
our
metrics.
I
won't
be
getting
all
the
metrics
from
this
data
table.
Let's
run
this
and
then
I'm
going
to
just
subset
the
data
based
on
what
we
need.
Okay,
so
this
is
our
subset
of
data.
W
e
have
our
metadata
over
here
along
with
those
four
data
metric
variables
that
we
wanted.
The
next
part
is
we're
going
to
just
do
a...
We
want
to
come
up
with
our
analysis.
I'm
just
doing
something
really
simple.
Just
run
that.
Then
here
comes
where...
So
if
I
go
back
to
this
data
dictionary,
we
saw
that
there's
already
some
cell
height
width
that
are
really
specific
for
each
one
of
these
cells
that
make
it
easy
reading
for
your
audience.
We
want
to
be
able
to
capture
that
and
be
able
to
translate
that
back
into
the
presentation
that
we
have
when
we
take
these
tables
and
put
this
on.
We
can't
do
that
with
the
data
table
because
basically
the
data
table
doesn't
allow
that
for
that
formatting.
Thank
you
to
Jay
Sun
from
Tech
Support,
who
helped
figure
out
how
to
do
this
because
there
was
a
couple
of
things.
One,
as
I
said
before,
you
can't
do
for
loops
within
table
boxes
or
other
displays,
you
need
to
be
able
to
do
this
by
a
lot
of
object.
I'm
struggling
with
the
terminology
here,
but
essentially
you
really
have
to
be
taking
this
and
building
the
table
box.
Then
also,
depending
on
how
many
columns
and
also
how
many
indicators
you
have,
you
want
to
have
this
dynamic.
You
don't
want
to
be
coding
this
specifically.
Essentially
we're
capturing
both
the
cell
heights,
we're
getting
the
names,
I
have
coded
the
wisp
explicitly.
The
code
that
we
had
was
working
and
then
for
some
reason
yesterday
it
wasn't
working.
The
only
way
I
could
get
around
with
this
was
to
explicitly
map
that
out
to
make
this
work.
Then
basically
building
out
the
data
table
with
some
columns
and
then
indicators
and
basically
just
basically
having
some
for
loops,
basically
taking
the
formatting
and
the
content
and
building
out
the
table.
Let's
run
this
and
see
what
this
actually
looks
like.
This
is
what
this
actually
looks
like.
Again,
similarly,
we
had
the
analysis,
I
keep
the
boxes
closed
and
basically
all
you
need
to
do
is
open
up
and
then
here's
all
your
definitions
on
here
along
with
the
indicator
name,
which
is
the
y
axis
over
here
along
with
the
long
definition.
Folks
can
really
geek
out
on
that,
where
the
source
is,
where
the
annual,
how
frequently
the
metric
is
actually
taken,
and
the
other
metadata
around
it,
like
how
these
metrics
were
actually
calculated.
You
can
do
this
for...
Here,
I
have
it
for
the
metric
definitions,
but
I
also
have
the
definitions
for
the
different
countries
too.
For
example,
I
was
looking
at
this
going
Eurozone,
what's
Euro
zone.
I
know
what
Europe
is
and
I
know
there's
some
countries
that
take
euros,
but
they
may
not
necessarily
be
in
the
euro.
For
example,
I
think
Montenegro
is
one
of
the
countries
that
uses
the
Euro,
but
they're
not
considered
the
Eurozone.
Same
thing
with
Europe
and
Central
Asia.
What
does
that
mean?
Is
Mexico
in
North
America
or
not?
Depends
on
different
definitions.
This
really
helps
the
audience
hopefully
get
the
information
that
they
want
so
they
can
interpret
this
data
fairly
well.
As
you
can
see,
this
took
a
bit
more
coding
on
that.
I
won't
necessarily
go
through
all
the
detail
of
that
coding,
but
if
you
want
to
get
that
coding,
feel
free
to...
I
would
be
more
than
willing
to
be
able
to
share
this
coding
with
you.
Just
email
me
at
sarahcallison@achievemorewithdata.com
and
I'll
be
more
than
glad
to
share
the
data
that
I
have
with
you
along
with
the
code.
Okay,
so
call
to
action.
Well,
my
call
to
action
to
everyone
is
try
to
build
in
data
dictionaries
with
your
analysis.
It
really
helps
your
audience
really
understand
the
data.
It
really
helps
them
be
able
to
go,
oh
yeah,
that's
what
it
means,
and
be
able
to
focus
versus
questioning
what
they're
actually
seeing.
Also,
number
two,
it
helps
you
too,
as
the
analyst,
remember,
oh
yeah,
this
metric
was
percentages
or
dollars
versus
euros
or
whatever
else
that
you
may
need
to
know
about
that
analysis.
The
other
aspect
too
is
vote
for
on
the
JMP
wish
list.
Enable
cell
formatting
for
Data
table
box.
I
totally
believe
that
this
could
help
simplify
the
code
that
we
use
versus
having
to
have
for
loop
and
having
to
understand
a
lot
of
JSL.
I
think
this
would
really
open
up
things
for
getting
more
people
to
do
data
dictionaries
if
we
can
enable
simpler
coding
just
with
the
data
table
box.
So,
I
really
appreciate
everyone's
time
in
listening
to
this
and
seeing
an
alternative
way
of
being
able
to
present
your
data
and
add
more
contextual
information
to
your
data.
Please
let
me
know
how
you're
using
the
data
dictionary
and
if
you
have
other
unique
ways
of
being
able
to
present
that
information.
So
thank
you
very
much
and
hopefully
I'll
see
you
at
the
Junk
Discovery
Summit
in
October.
Thank
you.
