1 / 57

Financial Data Model Overview

Financial Data Model Overview. Daniel Grieb Lori Silvestri. Agenda. Reporting Solution Star Schema Primer Data Modeling Process Finance Data Models Design Challenges and Choices Implementation Conclusion. Finance Data Modeling Guidelines.

dionysius
Download Presentation

Financial Data Model Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Financial Data Model Overview Daniel Grieb Lori Silvestri

  2. Agenda • Reporting Solution • Star Schema Primer • Data Modeling Process • Finance Data Models • Design Challenges and Choices • Implementation • Conclusion

  3. Finance Data Modeling Guidelines • Campus Solution must use CSU Finance Reporting Solution as Source • Replace Existing • Revenue and Expense (P & L) • Trial Balance Reporting • Drill from Summary to Transaction • Need daily refresh of large data sets • Anticipate analytical reporting

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

  5. REPORTING SOLUTION

  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

  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

  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

  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

  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

  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

  12. STAR SCHEMA PRIMER

  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.

  14. Star Schema Database Design Star Schema - a data model that consists of one fact table and one or more dimension tables Dimension Table 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 Dimension Table Dimension Table – Contains attributes describing a campus entity (i.e., department, account type, ledger, etc.)

  15. Star Schema WHO? • 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? THE FACTS WHAT? WHERE? WHEN?

  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

  17. Why Star Schema for Cal Poly Finance? • Dimensions can easily be reused • across current and future finance models • Superior query performance for large datasets • i.e., over 5 million rows • Usability • Understandable for users • Better support unanticipated questions • Star schemas are extremely compatible with business intelligence query tools such as OBIEE.

  18. DATA MODELING PROCESS

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

  20. Data Modeling Process: Requirements Gathering • Primarily Done by Reporting Solution Development • Our Requirement – Refashion Reporting Solution into a Dimensional Model • Performance • Accessibility

  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

  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

  23. FINANCE DATA MODELS

  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

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

  26. Model Overview – Actual, Budget and Encumbrance Summary

  27. Model Overview – Actual Transactions

  28. Model Overview – Budget Transactions

  29. Model Overview – Encumbrance Transactions

  30. Closer Look at a Dimension • Department • FINANCE_DEPARTMENT • Initial source was CSU Reporting Solution Department Attribute table • PS_CSU_R_DEPT_TBL

  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

  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

  33. Department Dimension

  34. Presentation of Data Models

  35. 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

  36. Design Challenges and Choices

  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

  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

  39. Design Challenges Challenge • Uniqueness not enforced in the reporting solution Solution • Added an instance number for identical transactions

  40. Design Challenges Challenge • Nightly rebuild of the reporting solution potentially deletes rows Solution • Effective-dated transactions in the fact

  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

  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.

  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

  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

  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

  46. IMPLEMENTATION

  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

  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

  49. Time and Resources • Caveats • Established documentation methods and coding standards • Slowly changing logic developed or provided by toolset • 3 transactional models implemented identically

  50. Nightly Build

More Related