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Overview of PeopleSoft Data Warehouse Implementation

Overview of PeopleSoft Data Warehouse Implementation. August 3, 2006. EPM Architecture. Multidimensional Warehouse. Facts – typically numeric values to quantify or calculate a company’s activities. In star schema development it is the central table used to connect dimensions.

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Overview of PeopleSoft Data Warehouse Implementation

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  1. Overview of PeopleSoft Data Warehouse Implementation August 3, 2006

  2. EPM Architecture

  3. Multidimensional Warehouse • Facts – typically numeric values to quantify or calculate a company’s activities. In star schema development it is the central table used to connect dimensions

  4. Multidimensional Warehouse • Dimensions - Allow analytics across subject matter areas • Uses Conformed dimensions - dimensions that mean the same thing in every possible fact table to which they can be joined—and therefore the same thing in every functional warehouse. • Common (Calendar, Time, Business Unit, Time Zone, Unit of Measure, Currency, Language) • Shared (Department, Item, Account, Person, Jobcode, etc.) • Subject Area (Billing Status –GL & Prof, Aging Category- Payables)

  5. PeopleSoft Mart • Mart refers to a PeopleSoft product that contains specific subject areas related to one of the delivered PeopleSoft functional warehouses. • Marts are derived from the MDW and are modeled to support analytic requirements, but not limit you to reporting only in that subject area. • Must license a supported reporting tool separately (e.g. Hyperion Reports) to access data – open reporting structure

  6. Supports reporting for the following business processes Order Fulfillment Procurement Datamarts Procurement Spend Inventory Sales Orders Supply Chain Warehouse

  7. Supports reporting for the following business processes Procurement Financial Control and Reporting Project Management Financial Control and Reporting Datamarts Payables General Ledger and Profitability Financial Warehouse

  8. Hyperion Integration

  9. Implementation Consideration • Our project timeline and scope will be dependent on Transaction applications. • Understanding Source data, customizations and configuration is necessary to validate delivered transformations, dimensions, and facts.

  10. Datamarts Payables General Ledger and Profitability Overview of reporting environment

  11. Overview of reporting environment Rept Rqmt 2 Rept Rqmt 5 Rept Rqmt 1 Rept Rqmt 3 Rept Rqmt 4 Rept Rqmt 6

  12. GL & Profitability D_DEPT D_DET_PERIOD F_JOURNAL D_JRNL_SOURCE D_ACCOUNT

  13. GL & Profitability D_DEPT_TBL D_DET_PERIOD F_LEDGER D_BOOK_CODE D_ACCOUNT

  14. GL & Profitability D_ABM_OBJECT D_DET_PERIOD F_PROFITABILITY D_PRJ D_ACCOUNT

  15. Payables D_DEPT D_PATTERN_DAY F_AP_ACCOUNT_LN D_BU_LED_GRP_TBL D_ACCOUNT

  16. Payables D_PERSON_AP_OPID D_DAY D_AP_VTR_TYPE F_AP_TRAN D_AP_PTR_TYPE D_AP_DOC_TYPE D_ACCOUNT

  17. Payables D_SUPPLIER D_DAY F_VCHR_MTCH_EXP D_PERSON_APOPID D_ MATCH_RULE

  18. Inventory D_LOT D_DAY F_INV_LDGR D_UOM D_ INV_ITEM

  19. Inventory D_LOT D_DAY F_INV_TRANS D_RECV_LN_SHP D_DEMAND_INF_INV D_ PHYSICAL_INV

  20. Inventory D_LOT D_DAY F_PHYSICAL_INV D_INV_LOCATION D_PO_STATUS D_ SUPPLIER

  21. Inventory D_LOT F_INV_CYCLE_CNT D_INV_LOCATION D_ INV_ITEM

  22. Procurement D_PO_LINE F_PO_SHIP_RCPT D_ACCOUNT D_ DLVRY_STATUS

  23. Procurement D_RTVLN_STATUS F_RTV_DIST D_SUPPLIER D_ VOUCHER

  24. Procurement D_MTCH_STATUS F_MTCH_ANLYS D_MATCH_RULE D_ INV_ITEM

  25. Procurement

  26. Spend D_PO_HDR D_DAY F_VCHR_LN D_SUPPLIER D_ INV_ITEM

  27. Justification: Done Planning: Need Project Plan Business Analysis: Not done Design: Not done but a huge head start Construction: Not done but a huge head start Deployment: Not done Delivered Vanilla Design and Construction is STRONG - Huge Head Start

  28. Recommended reporting solutions • Divided into major categories • Data Warehouse • On-line Lookup • Delivered report (i.e. SQR) • Defer until later phase • Download • Not needed • Considerations: • Sufficient granularity • Sufficient timeliness (i.e. Data Warehouse 1 day lag) • Has all the fields? • Issues – decision still pending on data capture • Issues – requires further development in Data Warehouse • Historical data will not be there day one • Goal: To have the best solution for this phase, with time and resource constraints.

  29. Status of report development • Data in Warehouse • Models in Warehouse • % complete (i.e. 7 out of 40) • Design status • Design pending issues, Design Not signed off, Design signed off • Development status • In development • Developed, ready to be QA’d • Development QA’d and Signed off • Naming standards used

  30. Technical Side

  31. Tip for organizing documentation

  32. ETL Ascential Jobs

  33. Tip for organizing ETL Ascential Jobs

  34. Tip for organizing ETL Ascential Jobs

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