rise Edition (FDMEE) to automate movement of metadata and data between ground and EPM Cloud. We will have a detailed discussion on the makeup of the FDMEE server configurations, allowing for invoking automated jobs to pull dimensions from an on-premises Essbase ASO application, allowing for custom scripting to invoke EPM Automate jobs to build dimensions and move data between different pods, finally allowing for movement of data in and out of the Oracle Profitability and Cost Management Cloud Service (PCMCS) application. A live demo will be used to illustrate various components of this interesting FDMEE-driven EPM process management architecture.

1. Oracle FDMEE is the EPM Cloud Data Hub at
Legg Mason
Tuesday, 6/25/19
10:00AM – 11:00AM
#211, Level 2
Seema Shah
Regional Manager
Corey Munch
AVP

2. Who We Are
Project Overview
Staging Area Overview
Use of FDMEE
Agenda
Gotchas/Challenges
Next Steps/Improvements
Questions?

3. Ranzal & Alithya join forces in
November 2018 to offer EPM,
ERP, & Analytics solutions
Proven business analytics leader
with a history of successful
implementations & growth
20+ years of implementations
Advisory
Services
Implementation
Services
Technical
Services
Hosting &
Support
Training
Services
Intellectual
Property
> Seema Shah
> Regional Manager
> 14+ Years with Alithya
> Integration Focused For 10+ Years
> Washington, DC
> seema.shah@alithya.com

4. • Corey Munch
• AVP, Manager/Technical Expert - Business
Data Solutions
• 6+ Years with Legg Mason
• Baltimore, MD
• cnmunch@leggmason.com
Guided by a mission of Investing to
Improve Lives™, Legg Mason helps
investors globally achieve better financial
outcomes by expanding choice across
investment strategies, vehicles and
investor access through independent
investment managers with diverse
expertise in equity, fixed income,
alternative and liquidity investments.

5. Three Primary Project Objectives
#1
To provide an enterprise wide solution that consistently measures profitability, in a timely manner,
across multiple dimensions: Product, Strategy, Firm, Channel, Vehicle, Location, etc.
#2
To create a ‘What If” tool that can assess profitability by changing existing attributes
#3
To create a process focused on resource utilization by better understanding costs and related
drivers

6. Essbase, ASO – Assets and revenue by product, strategy and domicile
Essbase, ASO – Distribution sourced assets by company and channel
Essbase, ASO – Expenses, by project code, for data enrichment
Apptio (Flat file) – Tech expenses, allocated by department, for attribution
IPD (Flat file) – Distribution expense, allocated by company, for attribution
HFM – Expenses
Staging Area - Data Sources

7. Essbase, ASO – Assets and revenue by product, strategy and domicile
Essbase, ASO – Distribution sourced assets by company and channel
Essbase, ASO – Expenses, by project code, for data enrichment
Apptio (Flat file) – Tech expenses, allocated by department, for attribution
IPD (Flat file) – Distribution expense, allocated by company, for attribution
HFM – Expenses
Staging Area - Data Sources

8. Staging Area - Data Sources

9. Staging Area - Data Sources
Batch file calling MaxL script
Staging area – Planning app
User input driver data via forms

10. Staging area – Planning app
Staging Area - Data Sources
Batch file calling MaxL script
User input driver data via forms

11. Staging area – Planning app
Calc script via batch file
Staging area – ASO cube
Staging Area - Design

12. Staging area – ASO cubeStaging area – Planning app
Calc script via batch file
Staging Area - Design

13. Staging area – ASO cube PCMCS
FDMEE
Staging Area - Design

14. Data Flow Diagram
PLN: LMStage
Cloud
Detail ESB: LMPCMSTG LMPCMC
R_LMPCMC ESB: PCMRPT

15. Metadata Refresh
PLN: LMStage
Cloud
Detail ESB: LMPCMSTG LMPCMC
R_LMPCMC ESB: PCMRPT
1) Execute Outline Extractor to pull out each dimension individually for the chosen app
2) Create new reformatted files that are in required format for PCMCS dimension updates
3) Execute series of EPM Automate commands to update metadata and redeploy cube

16. Metadata Refresh
Choose PCMCS application:
• Calculation Pod
• Reporting Pod
Execution Options:
• Immediate – Online
• Immediate – Offline
• Scheduled

17. Batch Scheduling

18. FDMEE
passes
variables
selected by
user to .bat
script
1
.Bat script calls
outline
extractor to
export files
from on-prem
ASO cube for
each
dimension
2
Perl scripts
used to
convert
dimension files
3
EPMAutomate
downloads,
deletes and
uploads files
to/from the
profit
inbox/outbox
4
Metadata Refresh Script
5
EPMAutomate
builds out
PCMCS
dimensions
using
previously
created files
6
Powershell
sends an email
to alert admins
that the
process has
completed
Occurring throughout:
- Logging
- File copying/archiving
- Error handling

19. On Premise to Calculation Pod
PLN: LMStage
Cloud
Detail ESB: LMPCMSTG LMPCMC
R_LMPCMC ESB: PCMRPT
1) Series of Data Load Rules that break out the data in appropriate groupings run in parallel
2) Execute rulesets after the data loads

20. On Premise to Calculation Pod
Update Default Period
Execution Options:
• Immediate
• Scheduled

21. FDMEE
passes
variables
selected by
user to data
load rules and
python scripts
1
If selected,
PreBatch
python script
clears out data
in calc pod
prior to data
load via
EPMAutomate
2
Individual data
load rules are
run from
FDMEE using
source/target
settings
3
If selected,
PostBatch
python script
runs
aggregation
rule in PCMCS
via
EPMAutomate
4
On-Premise to Calculation Pod – Data Movement
Occurring throughout:
- Logging
- File copying/archiving
- Error handling

22. Calculation to Reporting Pod
PLN: LMStage
Cloud
Detail ESB: LMPCMSTG LMPCMC
R_LMPCMC ESB: PCMRPT
1) Run the model if desired, and then export level 0 data to file for upload to on-premise Essbase application.
2) Export current POV using stub snapshot methodology for import to reporting pod.
3) Load Level 0 data files to reporting pod and run a rule to optimize data in the reporting pod.

23. Calculation to Reporting Pod
Select the Fiscal Year to copy
Execution Options:
• Immediate
• Scheduled
Select the Scenario to copy
Select the Version to copy
Confirm if Calculation Rules should
run prior to the data copy
Confirm if a data extract file should
be created for upload to the on
premise application

24. FDMEE
passes
variables
selected by
user to .bat/.py
script
1
Python script
collects and
outputs
variable file
2
.Bat script
called which
runs
EPMAutomate
commands
3
EPMAutomate
exports data
files,
downloads/up-
loads between
pods and loads
data
4
Calculation to Reporting Pod – Data Movement
5
Aggregation
rule run in
PCMCS to
optimize pod
6
Powershell
sends an email
to alert admins
that the
process has
completed
Occurring throughout:
- Logging
- File copying/archving
- Error handling
- Loading of on-prem
ASO cube

25. Data Flow Diagram
PLN: LMStage
Cloud
Detail LMPCMC
R_LMPCMC ESB: PCMRPT
ESB: LMPCMSTG

26. PCMCS On-Prem Essbase – ASO cube
FDMEE
Cloud to On-Prem Data Movement

27. MaxL scripts
called from
within Calc to
Reporting
process kicked
off in FDMEE
1
Level 0 data
from on-prem
ASO reporting
cube exported
and saved
2
On-prem ASO
reporting cube
dropped and
recreated
using staging
cube
3
Level 0 data
loaded back
into cube
4
Calculation to On-Prem ASO cube – Metadata Update and Data Movement
5
Fully allocated
calc pod data
file loaded into
cube
6
Aggregate
views created
to improve
performance
Occurring throughout:
- Logging
- File copying/archiving
- Error handling

28. FDMEE Challenges
1 Many moving parts in the process
FDMEE is the wrapper and calls other processes occurring elsewhere. Logs are written to a number of
different places and must be reviewed in different places
3 FDMEE clogs with too much data
Need to break data into smaller slices to ensure successful data loads
2 FDMEE logs are separate from PCMCS logs
Need to review FMDEE logs to ensure process success and PCMCS logs to ensure build/load success
4 Must validate success of each data load rule
Individual rules may try to run concurrently and result in one or more of them failing. Then must reload
individual rules manually

29. FDMEE Challenges
5 Pulling dynamically calculated data through FDMEE is very slow
Stats/KPIs/etc
Had to create calc scripts to store data in the Planning app, then push stored data to the ASO cube
7 Processing time
Time needed to move data up/down/between can be hours. Need to bake that time into any
maintenance estimates
6 Keeping managed infrastructure in sync with cloud
Servers are managed by a vendor, some challenges ensuring infrastructure remains in sync with the
cloud
(Cont’d)

30. Improvements/Lessons Learned
1 Scheduling FDMEE jobs to run later is beneficial
Schedule jobs in sequence once you find the normal amount of time they take to complete or schedule
jobs to run at off-peak or off-hours times, just be sure not to bump into the nightly maintenance window
3 Use the member selector in FDMEE windows
Typing values directly into the fields in FDMEE windows can result in the correct parameters not being
set
2 Make sure you can access the ODI Console
May need to kill a job running in FDMEE which must be done through the ODI Console
4 Use “offline” Execution mode
Allows you to navigate within Workspace while batch/script is running

31. Improvements/Lessons Learned
5 Would the processes benefit from using a REST API?
(Cont’d)

32. Q&A
Seema Shah
Regional Manager
Corey Munch
AVP