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Data warehouse implementation design for a Retail business
The document outlines the design of a data warehouse and business intelligence dashboard for Gap, Inc., detailing its company overview, dimensions, transformation rules, and a series of schemas used for data management. It emphasizes the importance of data consistency and decision support for analysis through OLAP tools and dashboards. Additionally, it presents various business processes, fact tables, and transformation strategies essential for effective data analysis and reporting.
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Data warehouse implementation design for a Retail business
- 1. Datwarehouse and Business IntelligenceDashboard Design for GAP Company Perspective
- 2. Outline • Company Overview •Bus Matrix & Star Schema • Conformed Dimensions • Transformation Rules • Aggregate Tables • Cube • Visualizations/Dashboards • Conclusion
- 3. What is GAP? TheGap, Inc., commonly known as Gap Inc. or Gap, is an American multinational clothing and accessories retailer. It was founded in 1969 by Donald Fisher and Doris F. Fisher and is headquartered in San Francisco, California. Brands • Old Navy • Banana Republic • GAP • Athleta • Piperlime More than 3000 stores Revenue over 3 billion -Wikipedia
- 4. Why do DW/BIfor GAP? Data consistency Decision support Analysis and Reports OLAP Tools Visualizations Analytics and Data mining
- 5. Order Management Schema order_master order_no customer_id item_loc item_price item_id promo_id sales_hist_id item_attribute warehouse_id shipment_id order_detail order_no item_id date_creation shipment_id order_status item_type order_qty Customer customer_id customer_name customer_city customer_cty customer_type shipment shipment_id item_id order_no item_qty to_loc from_loc warehouse(wh) wh_id wh_name wh_city wh_country wh_type item_price item_id item_type item_store item_promos item_id item_type promo_type discount_amount promo_id item_sale_hist item_id sale_id item_store item_wh order_no date_sale promo_id shipment_id sale_qtyitem_attrib item_id item_type item_color item_size item_stype item_dept item_brand item_loc_inv wh_id loc_type loc_id stock_on_hand
- 6. item_sales_master supplier_id(FK) order_no(FK) store_id(FK) promo_id(FK) item_id invoice_id(PK) wh_id date(FK) item_units supplier supplier_id(PK) supplier_country supplier_city supplier_name date_joined sup_loc_id supplier_type order_detail order_no(PK) item_id date_creation shipment_id item_type order_qty store store_id(PK) store_name store_city store_cty store_type item_promos promo_id (PK) item_type promo_type discount_amount item item_id(PK) item_color item_size item_dept item_brand navigation bridge item_id invoice_id number_of_levels bottom_flag item_inventory loc_type loc_id(wh) stock_on_hand item_id(PK) item_price item_id(PK) item_type item_price warehouse wh_id(PK) wh_name wh_city wh_country wh_type sale_date date(PK) sale_year sale_month sale_day Sales Transaction Schema
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- 9. E-commerce Dimension Table DetailedDiagram : Product Dimension Gender Categories Styles Color Description Product Price Band Price (3) (13) (89,243) (26) (89,243) (11,237,312 est.) (18) (5) Size (38) Slowly Changing Dimensions: Categories Gender Size Color Price Band Collection Review comment Review rate (9)
- 10. BUS MATRIX Date and time Supplier ItemOrder Store Customer Warehouse Promos Procurement x x x x Warehouse management x x x x x Shipping to stores x x x x Marketing x x x x x Sales x x x x x x Inventory Management x x x x Delivery x x x x x Customer Services x x x
- 11. Business Process Procurement Shipping MarketingFinance Customer Service Online sales transaction X X X X X In store Sales Transaction X X X X Supplier sales Transaction X X X Order processing X X X Delivery process X X Return Policy X X X
- 12. HIGH LEVEL BUSMATRIX Date and Time Supplier Item Order Store Customer Warehouse Promos Sales Transaction X X X X X X X Order Processing X X X X X X X Delivery Process X X X X X X Return Policy X X X X X X X
- 13. Fact Tables GranularityFact Date & Time Supplier Item Order Store Customer Warehouse Promos Sales Master Per Transaction Items Sold Amount Earned x x x x x x x Order Details Per Item x x x x x x x Inventory Management Per Order x x x x x x Billing Per Transaction x x x x x x Shipping Per Order x x x x x x Store return policy Per Item x x x x x x X
- 14. Business Process Sales Facttable Item_sales_master Grain region_month_itemtype Facts Itemunits_totalsales Sale_date Sale_month Warehouse Region Store Region
- 15. Busines s Process Sales Transac tion Order Processi ng Delivery Process Return Policy Fact Tables GranularityFact Date & Time Supplier Item Order Store Customer Warehouse Promos Sales Master Per Transaction Items Sold Amount Earned x x x x x x x Order Details Per Item x x x x x x x Inventory Management Per Order x x x x x x Billing Per Transaction x x x x x x Shipping Per Order x x x x x x Store return policy Per Item x x x x x x X
- 16. AGGREGATE TABLE Business ProcessSales Fact table Item_sales_master Grain region_month_itemtype Facts Itemunits_totalsales Sale_date Sale_month Warehouse Region Store Region
- 17. TRANSFORMATION RULES Data TypeConversion The source data type is generalized and changed into the destination data type. Constant It will add a predefined value to the destination field. Missing Values Missing fields will be filled with an appropriate value. Duplicate Rows This transformation rule will identify and delete duplicate rows. Look-Up Incorrect values and unknown values can be looked up from the table.
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- 19. Dice Slice Jan Feb Mar NY Cincinnati SF Shirt JanFeb Shirt Jacket Trouser NY Cincinnati SF
- 20. wn Roll up JanFeb Mar Shirt Jacket Trouser USA Mar NY Cincinnati SF
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- 22. E-commerce User & TaskAnalysis (con’t) • Operational – Lowest level entry point with limited data on a specific business process – Informative explanatory visualization • External Customer – Lower level entry point with both summary and drill down capability – Hybrid persuasive exploratory – informative explanatory visualization
- 23. E-commerce User & TaskAnalysis • Executive/Management – High level entry point with summary data and drill down capability – Primarily informative explanatory visualization • Business Analyst – High level entry point with summary data, drill down and drill across capabilities – Hybrid exploratory – informative explanatory visualization