Financial Data Model Overview Daniel Grieb Lori Silvestri

Financial Data Model Overview Daniel Grieb Lori Silvestri December 4, 2008 1

Agenda ■ ■ ■ ■ Reporting Solution Star Schema Primer Data Modeling Process Finance Data Models Design Challenges and Choices Implementation Conclusion December 4, 2008 2

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 Reporting 3. Drill from Summary to Transaction ■ ■ Need daily refresh of large data sets Anticipate analytical reporting December 4, 2008 3

Levels of Reporting Analytics Operational Transactional December 4, 2008 Enterprise Data Warehouse v. Combined information from multiple source systems. Current and historical information v. Much more sophisticated data structures to enable analysis: cubes and star schema Operational Reporting v. Tactical data from production systems that address operational needs v. Denormalized data structures with embedded business logic Transactional Reporting v. Supports day to day transactional users v. Requires knowledge of transactional data 4

REPORTING SOLUTION December 4, 2008 5

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 6

CSU Reporting Solution - Attributes ■ Attribute Tables – one set for each Set ID (XXCMP, XXCSU, XXGAP) – – – ■ ■ Fund Department Account Program Project Class CSU_R_FUND_TBL CSU_R_DEPT_TBL CSU_R_ACCT_TBL CSU_R_PRGM_TBL CSU_R_PROJ_TBL 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 7

CSU Reporting Solution - Transactions ■ ■ Transaction Tables – separate tables per Business Unit Campus Business Unit Transaction Tables – – ■ ■ Actuals Budgets Encumbrances Pre-Encumbrances CSU_R_ACTDT_CMP CSU_R_BUDDT_CMP CSU_R_ENCDT_CMP 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 8

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 9

Benefits of the Reporting Solution to 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 10

Benefits of the Reporting Solution to 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 11

STAR SCHEMA PRIMER December 4, 2008 12

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 13

Star Schema Database Design Dimension Table Star Schema - a data model that consists of one fact table and one or more dimension tables Dimension Table Fact Table 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. ) December 4, 2008 14

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? December 4, 2008 WHO? WHAT? THE FACTS WHERE? WHEN? 15

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 16

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 rows 3. 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 17

DATA MODELING PROCESS December 4, 2008 18

Data Modeling Process ■ ■ ■ Interactive/ Iterative Process Requirements Gathering Domain research Data profiling Modeling tool Design sessions with data steward December 4, 2008 19

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 20

Data Modeling Process: Research ■ Domain research – – ■ Finance Cal Poly Financials Cal Poly Reports (n. Vision, Brio) Industry Finance Models (Kimball) Data profiling – Querying reporting solution – Correlating fields/ values – Matrix of Attributes Across Document Sources December 4, 2008 21

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 22

FINANCE DATA MODELS December 4, 2008 23

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 December 4, 2008 24

Actual Fact Budget Fact Encumbrance Fact Summary Fact December 4, 2008 High Level Finance Data Model Diagram Who (Dept ID, Vendor, etc) What (Account, Fund, etc) When (Acctg. Period, Fiscal Year, etc. ) Where (Business Unit, etc) 25

Model Overview – Actual, Budget and Encumbrance Summary December 4, 2008 26

Model Overview – Actual Transactions December 4, 2008 27

Model Overview – Budget Transactions December 4, 2008 28

Model Overview – Encumbrance Transactions December 4, 2008 29

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 30

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 31

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 32

Department Dimension December 4, 2008 33

Presentation of Data Models December 4, 2008 34

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 35

Design Challenges and Choices December 4, 2008 36

Design Challenges Challenge ■ Reporting solution is denormalized – Poly. Data 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 37

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 38

Design Challenges Challenge ■ Uniqueness not enforced in the reporting solution Solution ■ Added an instance number for identical transactions December 4, 2008 39

Design Challenges Challenge ■ Nightly rebuild of the reporting solution potentially deletes rows Solution ■ Effective-dated transactions in the fact December 4, 2008 40

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 41

Design Challenges - Naming Challenge ■ Reporting Solution names did not conform with Poly. Data 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 42

Design Choices – Slowly Changing Dimensions ■ ■ Most dimensional attributes were determined by data steward to be slowly changing dimension Type 1 (SCD 1). Exception: Department Table – SCD 1 attributes such as department description – SCD 2 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 43

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 44

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 45

IMPLEMENTATION December 4, 2008 46

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 47

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 48

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 49

Nightly Build Job Source pull Reporting solution build Minutes (approximate) 30 70 Data model build 140 End user table refresh TOTAL 60 300 December 4, 2008 50

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 Poly. Data warehouse jobs December 4, 2008 51

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 52

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 53

CONCLUSION December 4, 2008 54

Future Work ■ ■ Labor Cost GAAP Reporting Management Dashboard/Analytics Integration with HR and Student Data December 4, 2008 55

Questions? Daniel Grieb Data Warehouse Architect, Analyst/Programmer Lori Silvestri Data Warehouse Analyst/Programmer December 4, 2008 56

Contact ■ OBIEE Technical Conference: http: //polydata. calpoly. edu/dashboards/obiee_conf/index. html ■ Email: polydata@calpoly. edu December 4, 2008 57