Best Practices for Data Warehousing

2 Agenda – Best Practices for DW-BI Best Practices in Data Modeling Best Practices in ETL Best Practices in Reporting

3 Best Practices in Data Modeling

4 ProductsDimension TimeDimension Oracle Order Management & Fulfillment Analytics Q. How many of my top customers bought products from my worst suppliers? Sales Orders Fact Table Dim Table DimensionTables Support for Cross-Application Analysis Supply Chain Analytics Purchase Orders Fact Table Dim Table DimensionTables Fundamental requirement that dimensions be common (conformed)

5 Features: Conformed dimensions Transaction data stored in most granular fashion Tracks full history of changes Prebuilt and extensible Built for speed Integrated Enterprise Analytics Data Model Benefits: Enterprise-wide business analysis (across entire value chain) Access summary metrics or drill to lowest level of detail Accurate historical representations Service Customers Sales Marketing Distribution Finance HR / Workforce Operations Procurement Customers Suppliers

6 The Result From this : To this : -Fewer, larger database tables rather than many smaller ones -Same piece of data appearing in several locations -Reduces need for join paths -Structure is denormalized for performance

7 Best Practices in ETL

8 Administration Metadata Presentation Dashboards by Role Reports, Analysis / Analytic Workflows Metrics / KPIs Logical Model / Subject Areas Physical Map BI Server Direct Access to Source Data Data Warehouse / Data Model DAC Federated Data Sources SiebelOracleSAP R/3PSFTEDW Other ETL Load Process Staging Area Extraction Process DAC ETL Architecture – Best Practice Load Extract SAPPeopleSoft Source Independent Layer Staging Tables Source Dependent Extract OtherSiebelOLTPOracle Special Connect Special Connect SQL App Layer ABAP App Layer Data DataWarehouse Transform

9 Use of a ETL platform Limited programming – GUI interface Re-usable components Easy data lineage tracking (where did data come from?) Pseudo-documentation – fast ramp-up for new resources Can build, test & implement the data flows more quickly

10 ETL Framework – Best Practices Generates surrogate key Does lookups for descriptions of code fields Does data driven updates – inserts for new rows, updates for old rows Reject Capture Keep track of effective dates and maintain history as required Handles Deletes

11 Best Practices in Reporting

12 The Semantic Layer Administration Metadata BI Presentation Services Dashboards by Role Reports, Analysis / Analytic Workflows Direct Access to Source Data Data Warehouse / Data Model ETL Load Process Staging Area Extraction Process DAC Federated Data Sources SiebelOracleSAP R/3PSFTEDW Other Multi-layered Abstraction Separation of physical, logical and presentation layers Logical modeling builds upon complex physical data structures Logical model independent of physical data sources, i.e. same logical model can be remapped quickly to another data source Metrics / KPIs Aggregate navigation Prebuilt hierarchy drills and cross dimensional drills Metrics / KPIs Logical Model / Subject Areas Physical Map BI Server

Object Security What parts of the application can you see? Business Logic Object Security Object Security Presentation Layer Physical Layer Semantic Object Layer Controls access to Subject Areas, Tables and Columns in Presentation Layer Limits access to Dashboards, Reports and Web Folders Web Object Security

14 DW -BI Architecture – Best Practice Extension of DW Schema for extension columns, additional tables, external sources, aggregates, indices, etc. Extension of ETL for extension columns, descriptive flexfields, additional tables, external sources, etc. Additional derived metrics, custom drill paths, exposing extensions in physical, logical and presentation layer, etc. Additional dashboards and reports, guided and conditional navigations, iBots, etc. Level of Effort Degree of Customization Easy Moderate Intermediate Involved Dashboards & Reports Semantic Metadata Layer DW Schema ETL

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