Financial Data Model Overview

December 4, 2008 1

Financial Data Model Overview

Daniel Grieb

Lori Silvestri

December 4, 2008 2

Agenda

■ Reporting Solution ■ Star Schema Primer■ Data Modeling Process■ Finance Data Models■ Design Challenges and Choices■ Implementation■ Conclusion

December 4, 2008 3

Finance Data Modeling Guidelines

■ Campus Solution must use CSU Finance Reporting Solution as Source

■ Replace Existing 1. Revenue and Expense (P & L)2. Trial Balance Reporting3. Drill from Summary to Transaction

■ Need daily refresh of large data sets■ Anticipate analytical reporting

December 4, 2008 4

Levels of Reporting

Transactional

Transactional ReportingSupports day to day transactional usersRequires knowledge of transactional data

Enterprise Data Warehouse Combined information from multiple source systems. Current and historical informationMuch more sophisticated data structures to enable analysis: cubes and star schema

Analytics

Operational ReportingTactical data from production systems that address operational needsDenormalized data structures with embedded business logic

Operational

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REPORTING SOLUTION

December 4, 2008 6

CSU Reporting Solution

■ Attribute Tables– one set for each Set ID – XXCMP, XXCSU, XXGAP

■ Transaction Tables– separate tables per Business Unit

■ Summary Table– XXCMP and XXCSU

* Brothwell, Kist, and Yelland, “Finance 9.0 Reporting Solution Training” April, 2008

December 4, 2008 7

CSU Reporting Solution - Attributes

■ Attribute Tables – one set for each Set ID (XXCMP, XXCSU, XXGAP)– Fund CSU_R_FUND_TBL– Department CSU_R_DEPT_TBL– Account CSU_R_ACCT_TBL– Program CSU_R_PRGM_TBL– Project CSU_R_PROJ_TBL– Class CSU_R_CLASS_TBL

■ Can be joined to transaction and summary tables■ Department table contains “flattened” version of the

campus organization department tree

* Brothwell, Kist, and Yelland, “Finance 9.0 Reporting Solution Training” April, 2008

December 4, 2008 8

CSU Reporting Solution - Transactions

■ Transaction Tables – separate tables per Business Unit

■ Campus Business Unit Transaction Tables– Actuals CSU_R_ACTDT_CMP– Budgets CSU_R_BUDDT_CMP– Encumbrances CSU_R_ENCDT_CMP– Pre-Encumbrances CSU_R_PREDT_CMP

■ CSU Business Unit Transaction Tables■ GAP Business Unit Transaction Tables

* Brothwell, Kist, and Yelland, “Finance 9.0 Reporting Solution Training” April, 2008

December 4, 2008 9

CSU Reporting Solution - Summary

■ Summary Tables (XXCMP and XXCSU)■ Campus Business Unit Summary Table

– CSU_R_SUMBL_CMP

■ CSU Business Unit Summary Table– CSU_R_SUMBL_CSU

* Brothwell, Kist, and Yelland, “Finance 9.0 Reporting Solution Training” April, 2008

December 4, 2008 10

Benefits of the Reporting Solutionto the Dimensional Data Model

■ Validated independently– Reporting solution was validated between

January and September 2008– Finance was heavily invested in, helped

design and trusted the reporting solution– Sped up data model validation because we

could tie to the reporting solution» Finance validated within days, rather than

weeks» Validated using the dashboards

December 4, 2008 11

Benefits of the Reporting Solutionto the Dimensional Data Model

Reporting solution now used in parallel by Finance for internal querying and to fill ad hoc requests– Phase one of the data models did not have

to incorporate all of the reporting solution data

– Helped constrain project scope

December 4, 2008 12

STAR SCHEMA PRIMER

December 4, 2008 13

What Is a Star Schema

The star schema is perhaps the simplest data warehouse schema. It is called a star schema because the diagram of this schema resembles a star, with points radiating from a central table. The center of the star consists of a large fact table and the points of the star are the dimension tables.

December 4, 2008 14

Dimension Table

Dimension Table

Dimension Table

Dimension Table

Fact Table

Star Schema - a data model that consists of one fact table and one or more dimension tables

Contains:

facts and/or measures to be analyzed (i.e., amount, count, etc.)

and foreign keys (keys to dimension tables)

Dimension Table – Contains attributes describing a campus entity (i.e., department, account type, ledger, etc.)

Star Schema Database Design

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Star Schema

•Fact tables contain process activity located in the center (quantitative data) Some example facts are monetary amount, budget amount and statistics amount

•Dimensions tell the story and provide the detail to the facts. Which department’s budget? When was the last transaction posted for a given account?

THEFACTS

WHERE?

WHO?

WHAT?

WHEN?

December 4, 2008 16

Star Schema Benefits

■ Data model is easy to understand– Based on business process

■ Easy to define hierarchies– City-State-Country– Day-Accounting Period-Fiscal Year

■ Easy to navigate– Number of table joins reduced– Star schema recognized by leading query tools

■ Maintainable and Scalable– Dimension tables shared between data models– Can add new fact tables which use existing dimensions

December 4, 2008 17

Why Star Schema for Cal Poly Finance?

1. Dimensions can easily be reused ■ across current and future finance models

2. Superior query performance for large datasets

■ i.e., over 5 million rows3. Usability

■ Understandable for users ■ Better support unanticipated questions

4. Star schemas are extremely compatible with business intelligence query tools such as OBIEE.

December 4, 2008 18

DATA MODELING PROCESS

December 4, 2008 19

Data Modeling Process

■ Interactive/ Iterative Process■ Requirements Gathering■ Domain research ■ Data profiling■ Modeling tool■ Design sessions with data steward

December 4, 2008 20

Data Modeling Process: Requirements Gathering

■ Primarily Done by Reporting Solution Development

■ Our Requirement – Refashion Reporting Solution into a Dimensional Model– Performance– Accessibility

December 4, 2008 21

Data Modeling Process: Research

■ Domain research – Finance– Cal Poly Financials– Cal Poly Reports (nVision, Brio)– Industry Finance Models (Kimball)

■ Data profiling– Querying reporting solution– Correlating fields/ values– Matrix of Attributes Across Document Sources

December 4, 2008 22

Data Modeling Process: Design

■ Modeling tool– Needed a tool to support efficient design– Limitations of modeling tools like Visio– Embarcadero ER Studio

■ Design sessions with data steward – model reviews

» Validated groupings of attributes into dimensions» New (non-reporting solution) sources

(i.e., dept, prog and proj trees)

– prototyping dashboards

December 4, 2008 23

FINANCE DATA MODELS

December 4, 2008 24

Cal Poly Finance Data Models

■ 4 data models implemented to date■ 22 Dimensions

– Reused across models– Chart fields, Business unit, Ledger, etc

■ 4 Fact tables– Actual Transactions– Budget Transactions– Encumbrance Transactions– Actual, Budget and Encumbrance Summary

Who(Dept ID, Vendor,

etc)

What (Account,

Fund, etc)

Where (Business Unit,

etc)

When(Acctg. Period,

Fiscal Year, etc.)

Actual

Fact

Summary

Fact

Budget

Fact

High Level Finance Data Model Diagram

Encumbrance

Fact

December 4, 2008 26

Model Overview – Actual, Budget and Encumbrance Summary

December 4, 2008 27

Model Overview – Actual Transactions

December 4, 2008 28

Model Overview – Budget Transactions

December 4, 2008 29

Model Overview – Encumbrance Transactions

December 4, 2008 30

Closer Look at a Dimension

■ Department– FINANCE_DEPARTMENT

■ Initial source was CSU Reporting Solution Department Attribute table– PS_CSU_R_DEPT_TBL

December 4, 2008 31

Closer Look at a Dimension

■ Source Department table – contains “flattened” version of campus organization

department tree– Ragged hierarchy

■ Added additional source data – Cal Poly department tree– Non-ragged hierarchy– Robust hierarchy for data exploration– Supports reporting on department reorganization

or renaming– Cal Poly users are accustomed to using this tree

December 4, 2008 32

Closer Look at Department Dimension

■ Department Budget Specialist and Manager – Reporting Solution provides a single manager field

■ Cal Poly Needs Primary and Secondary Budget Specialists and Managers – Available for querying and display in reports– Used for access control in Finance dashboards - filtering /

ease of use

■ Source – Excel Spreadsheet – Provided by Finance– Updated weekly– Plan to create mini-web application to capture data in future

December 4, 2008 33

Department Dimension

December 4, 2008 34

Presentation of Data Models

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Transactional vs. Summary Models

■ Dimensions in the summary model are a subset of those in the transactional models– Allows for drill-across from summary to

transactional models– “Feels like” a drill-down

December 4, 2008 36

Design Challenges and Choices

December 4, 2008 37

Design Challenges

Challenge■ Reporting solution is denormalized

– PolyData typically sources normalized data sources and manages denormalization

Solution■ Took us a little outside of our comfort zone■ Deconstructed the reporting tables into unique

combinations of elements

December 4, 2008 38

Design Challenges

Challenge■ Attributes are “overloaded”

– For example, a document_id can represent an invoice number, a PO number, a journal identifier, etc.

Solution■ Preserved this concept in the dimensional

models because it is familiar to Finance

December 4, 2008 39

Design Challenges

Challenge■ Uniqueness not enforced in the

reporting solution

Solution■ Added an instance number for identical

transactions

December 4, 2008 40

Design Challenges

Challenge■ Nightly rebuild of the reporting solution

potentially deletes rows

Solution■ Effective-dated transactions in the fact

December 4, 2008 41

Design Challenges

Challenge■ Transactional and summary reporting tables

may not tie– journal vs. ledger sources– summing the detail may give the wrong answer

Solution■ This is a known issue to which Finance is

accustomed■ Opportunity for a dashboard integrity report

December 4, 2008 42

Design Challenges - Naming

Challenge■ Reporting Solution names did not conform with

PolyData Warehouse standards

Solution■ Data Warehouse standards

– Field and table names use full English words when possible for usability

– Codes precede corresponding description (Code, Descr)

■ Used reporting solution names with full spelling and adding ‘Code’ and ‘Descr’ where appropriate.

December 4, 2008 43

Design Choices – Slowly Changing Dimensions

■ Most dimensional attributes were determined by data steward to be slowly changing dimension Type 1 (SCD1).

■ Exception: Department Table – SCD1 attributes such as department description– SCD2 department tree data

■ *IF* you need to track historical changes to dimensions– You may need to source dimensions from source system(s)– Candidates include chart fields, vendors, customers

December 4, 2008 44

Design Choices – SCD Example

■ Cal Poly needs department tree history– Department tree data

» Slowly Changing Dimension Type 2 - preserves history

» Effective date rows (effective from and to dates)» Add new row for each change

– All other department attributes» Slowly Changing Dimension Type 1 –

overwrites history» Replace old/outdated data with current

December 4, 2008 45

Design Choices – New Sources

■ In design and prototyping sessions with end users, it became apparent that additional source data was needed

■ New non-reporting solution sources were needed to supplement existing source.– Department tree– Program tree– Project tree

■ Design change from using only reporting solution as source

December 4, 2008 46

IMPLEMENTATION

December 4, 2008 47

Time and Resources

■ Modeling/Domain familiarization– 2 data modelers– June through August 2008

■ Source-to-Target analysis and documentation– 2 analysts– July through September 2008

December 4, 2008 48

Time and Resources

■ Coding and system integration– 4 ETL programmers– August through October 2008

■ Total person-days– July through October 2008– Approximately 140 person-days

December 4, 2008 49

Time and Resources

■ Caveats– Established documentation methods and

coding standards– Slowly changing logic developed or

provided by toolset– 3 transactional models implemented

identically

December 4, 2008 50

Nightly Build

Job Minutes (approximate)

Source pull 30

Reporting solution build 70

Data model build 140

End user table refresh 60

TOTAL 300

December 4, 2008 51

Performance Tuning:Nightly Build

■ Coordination with Finance on their builds– Nightly processing– Reporting solution (in transactional database)

■ Approximately one month to level out on timing– Tuning specific to the finance jobs– Coordination with other PolyData warehouse jobs

December 4, 2008 52

Performance Tuning:End-User Tables

■ Performance was reasonable prior to indexing– Largely due to the dimensional structure

■ Performance screamed after indexing– Indexes on fields used in selection criteria

and drillable hierarchies– Bitmap indexes on foreign keys in facts

December 4, 2008 53

Implementation: Interface with Front End Developers

■ joins should be fully documented ■ front end developers may need some training

in interpreting models ■ we still have not come up with an ideal

method for documenting hierarchies■ challenge - knowledge of hierarchies is

shared – data steward– front end developers – modelers

December 4, 2008 54

CONCLUSION

December 4, 2008 55

Future Work

■ Labor Cost■ GAAP Reporting■ Management Dashboard/Analytics■ Integration with HR and Student Data

December 4, 2008 56

Questions?

Daniel GriebData Warehouse Architect, Analyst/Programmer

Lori SilvestriData Warehouse Analyst/Programmer

December 4, 2008 57

Contact

■ OBIEE Technical Conference:http://polydata.calpoly.edu/dashboards/obiee_conf/index.html

■ Email: [email protected]