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  put…

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.