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Best Practices in HFM Application Design Chris Barbieri Consolidation Practice Director Oracle ACE Ranzal & Associates

Personal Background Chris Barbieri • Esta Establ blis ishe hed d HFM HFM perf perfor orma manc nce e tuni tuning ng tech techni niqu ques es and statistics widely used today • 4+ yea years rs as as Sr. Sr. Pro Produ duct ct Iss Issue uess Mana Manage gerr at Hype Hyperi rion on  – HFM, Smart View, Shared Services, MDM

, HFM and Enterprise • MBA, Babson College • B.S. B.S. Fina Financ nce e & Acco Accoun unti ting ng,, Bos Bosto ton n Col Colle lege ge • Co-f Co-fou ound nded ed the the HFM HFM Per Perfo form rman ance ce Tun Tunin ing g Lab Lab at at Ranzal with infrastructure expert Kurt Schletter

Application Design: the Foundation of  Performance • Hyperion Financial Management • Metadata design as it impacts  – Volume of members  – Impact of structures

• Data  – Content  – Density

Metadata

Designing HFM’s 12 Dimensions Application Profile

User controlled

1. Year

5.

Entity

2. Period

6.

Account

3. View

7.

ICP

8.

Scenario

4. Value dimension, includes currencies

User defined 9.

Custom 1

10. Custom 2 11. Custom 3 12. Custom 4

Application Profile Year  – No inherent impact on performance  – Cannot be changed after the application is built  – Impacts the number of tables that can be created in the database

Period  –

e ase per o s compr se t e co umn structure o every table, whether you use them or not.  – For this reason, avoid weekly or yearly profiles unless it is key to your entire application’s design

View  – No impact, but only YTD is stored and Periodic, QTD are on-the-fly derivations

System Dimension Value Dimension  – Can not directly modify this  – “” is a simple variable directing you to the current entity’s default currency  – “” points back to the currency of the entity’s parent

Currencies  – Don’t add currencies you aren’t using • Sets of calc status records for (every entity * every currency) • Impact of loading metadata with entity or currency changes

 – Normally translate from the entity’s currency only into it’s parent’s currency.  – Beware of non-default translations • Impacted calc status • Data explosion

User Controlled Dimensions Entity  – Sum of the data of the children  – Avoid Consolidate All or All With Data on each hierarchy  – Assign Adj flags sparingly  – “Hidden” dimension

Scenario  – Number of tables

Impact of Account Depth

6- Net Income

4- Net Income

5- EBIT

3- Optg Income 2- Gross Margin 1- Sales

4- Optg Income 3- Gross Profit 2- Gross Margin





Effect is multiplied when you consider the custom dimensions Parent accounts don’t lock

1- Sales

User Defined Dimensions Custom 1..4  – Think dozens or hundreds, but not thousands  – Avoid: • Employees • Products • Anything that is very dynamic • One to one relationship with the entities

Metadata Efficiency Ratio What does the average entity have in common with the top entity?  – Density measurement of re-use of the accounts and customs across all entities top entity children unique custom 1

Metadata Volumes (Americas) Dimension

Average Volume

Recorded High

Comments

Accounts

2,132

14,409

Entities

1,165

22,882

16

233

Custom1

388

19,410

use Custom 1 96%

Custom2

153

15,188

use Custom 2 86%

Custom3

61

26,816

use Custom 3 86%

Custom4

39

11,389

use Custom 4 62%

Scenarios

11

78

3

24

ICP Accounts with Plug

41

1,223

use automated intercompany matching 56%

Accounts with Line Item Detail

36

1,667

16% use this, but only 10% have more than 1 account flagged

Consolidation Rules

-

-

Consolidation methods

5

10

Currencies

Entity hierarchies

OrgByPeriod ICP Members

use only

1 currency 30%

the equivalent of Organizations in Hyperion Enterprise

use consolidation rules 28% use methods 14% use organization by period 9%

86

1,407

track intercompany activity 81%

Entities flagged for Parent Adjs

143

7,698

Allow [Parent Adj] or [Contribution Adj] journals30%

Scenarios using Process Mgmt

5

53

use process management 46%

Data

What’s a Subcube? • HFM data structure • Database tables stored by  – Each record contains all periods for the [Year]  – All records for a subcube are loaded into memory together

Parent subcube, stored in DCN tables Currency subcubes, stored in DCE tables

Take it to the Limit Reports, Grids, or Forms that:  – Pull lots of entities  – Lots of years  – Lots of scenarios

Not so problematic:  – Lots of accounts  – Or Custom dimension members

Smart View  – Cell volume impacts bandwidth  – Subcubes impact server performance

HFM Urban Legends • 100,000 records per subcube • Increase MaxNumDataRecordsInRAM = better performance • 500 children to a arent • System 9 allows an unlimited sub cube size • Customs should be ordered largest to smallest • Limit to the Account dimension depth • 64 bit is faster (this requires some explanation)

Data Design

“Metadata volume is interesting, but it’s how you • Density • Content  – Specifically: zeros  – Tiny numbers  – Invalid Records

it that matters most”

Data Volume Measurement • No perfect method Method

How-To

Pros

Cons

Data Extract

Extract all data, count per entity

Simple, easy to see input from calculated

Can only extract

FreeLRU

Parse HFM event logs

Good sense of average cube, easy to monitor monthly growth

Can’t identify individual cubes, harder to understand

Database Analysis

Query DCE, DCN tables and count

Easy for a DBA, see all subcubes

Doesn’t count dynamic members, includes invalid records

Data Density Using FreeLRU • Survey of data density using FreeLRU method Number of applications reviewed: 32

NumCubesInRAM NumDataRecordsInRAM NumRecordsInLargestCube Average records per cube Average metadata efficiency: average cube/densest cube

Average

Min

Max

10,206

Median

ABC Customer

2,672

72

1,345

577

1,502,788

247,900

5,627,748 1,170,908

1,107,614

86,415

2,508

593,924

53,089

31,446

6,309

24

91,418

1,352

2,288

7.3%

0.3%

39.7%

3.4%

7.3%

Loaded Data • What percent of the loaded data is a zero value?  – No hard rule, but -1 and < 1 % values > -1 and < 1

2,031,976 Total 18,024 Calculated zeros 0.9% % zeros calculated at base 373,226 Values > -1 and < 1 calculated 18.4% % values > -1 and < 1 calculated

4,387,520

116 %

413,837

2,196 %

9.4% 593,981 13.5%

59 %

Effect of Sparsity on Record Volume • Most dense data is at the top entity  – Greatest number of populated intersections (account _ custom 1..4 combinations)

Consolidated Data • Total volume of data in any subcube • How many zeros are generated by the consolidation process?  – Intercompany eliminations

Consolidated Base Records

Total

991,587

Consolidated zeros

194,204

% zeros Values > -1 and < 1 % values > -1 and < 1

 – Allocations  – Empty variables

Consolidated 19.6%

Calculated 9.4% Loaded 0.9%

19.6% 84,251 8.5%

Data Density Calc Time Average Rule Execution Time in Contrast with Data Volume 900

2.500

800 2.000

700 600      s        d      r      o      c      e        R

1.500 500 400 1.000

     s        d      n      o      c      e        S

300 200

0.500

100 -

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

correlation between density and calc times • Most applications are rules bound

Invalid Records • Type 1: Orphaned records from metadata that has been deleted  – Member is removed from dimension_Item table, but not from the data tables  – These can be removed b Database > Delete Invalid Records

• Type 2: the member still exists, but is no longer in a valid intersection  – Most often from changing CustomX Top Member on an account  – These cannot be removed by HFM, but are filtered out in memory

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