Data warehouse implementation design for a Retail business
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Transcript of Data warehouse implementation design for a Retail business
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Datwarehouse and Business Intelligence
Dashboard Design for GAP
Company Perspective
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Outline
• Company Overview• Bus Matrix & Star Schema• Conformed Dimensions• Transformation Rules• Aggregate Tables • Cube• Visualizations/Dashboards• Conclusion
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What is GAP?The Gap, 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 storesRevenue over 3 billion
-Wikipedia
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Why do DW/BI for GAP?
Data consistencyDecision supportAnalysis and ReportsOLAP ToolsVisualizationsAnalytics and Data mining
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Order Management Schema
order_masterorder_nocustomer_iditem_locitem_priceitem_idpromo_idsales_hist_iditem_attributewarehouse_idshipment_id
order_detailorder_noitem_iddate_creationshipment_idorder_statusitem_typeorder_qty
Customercustomer_idcustomer_namecustomer_citycustomer_ctycustomer_type
shipmentshipment_iditem_idorder_noitem_qtyto_locfrom_loc
warehouse (wh)wh_idwh_namewh_citywh_countrywh_type
item_priceitem_iditem_typeitem_store
item_promositem_iditem_typepromo_typediscount_amountpromo_id
item_sale_histitem_idsale_iditem_storeitem_whorder_nodate_salepromo_idshipment_idsale_qtyitem_attrib
item_iditem_typeitem_coloritem_sizeitem_stypeitem_deptitem_brand
item_loc_invwh_idloc_typeloc_idstock_on_hand
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item_sales_mastersupplier_id(FK)order_no(FK)store_id(FK)promo_id(FK)item_idinvoice_id(PK)wh_iddate(FK)item_units
suppliersupplier_id(PK)supplier_countrysupplier_citysupplier_namedate_joinedsup_loc_idsupplier_type
order_detailorder_no(PK)item_iddate_creationshipment_iditem_typeorder_qty
storestore_id(PK)store_namestore_citystore_ctystore_type
item_promospromo_id (PK)item_typepromo_typediscount_amount
itemitem_id(PK)item_coloritem_sizeitem_deptitem_brand
navigation bridgeitem_idinvoice_idnumber_of_levelsbottom_flag
item_inventoryloc_typeloc_id (wh)stock_on_handitem_id(PK)
item_priceitem_id(PK)item_typeitem_price
warehousewh_id(PK)wh_namewh_citywh_countrywh_type
sale_datedate(PK)sale_yearsale_monthsale_day
Sales Transaction Schema
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Item_sales_master
Supplier
Order_ detail
Item_ inventor
y
Item_ price
Ware house
Store
Item_ promos
Sale_ date
Item
Logical Model : Sales Schema
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Confirmed Dimensions
warehouse (wh)wh_idwh_namewh_citywh_countrywh_type
storestore_id(PK)store_namestore_citystore_ctystore_type
suppliersupplier_id(PK)supplier_countrysupplier_citysupplier_namedate_joinedsup_loc_idsupplier_type
itemitem_id(PK)item_coloritem_sizeitem_deptitem_brand
order_detailorder_noitem_iddate_creationshipment_idorder_statusitem_typeorder_qty
Item_sale_master
Order_master
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E-commerceDimension Table Detailed Diagram : 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:CategoriesGenderSizeColorPrice Band
Collection
Review commentReview rate (9)
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BUS MATRIX Date
and time
Supplier Item Order 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 xSales x x x x x xInventory Management
x x x x
Delivery x x x x x Customer Services
x x x
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Business Process
Procurement Shipping Marketing Finance 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
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HIGH LEVEL BUS MATRIX
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
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Fact Tables Granularity Fact Date & Time
Supplier Item Order Store Customer Warehouse Promos
Sales Master Per Transaction
Items SoldAmount Earned
x x x x x x x
Order Details Per Item x x x x x x xInventory 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
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Business Process Sales
Fact table Item_sales_master
Grain region_month_itemtype
Facts Itemunits_totalsales
Sale_date Sale_month
Warehouse Region
Store Region
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Business Process
Sales Transaction
Order Processing
Delivery ProcessReturn Policy
Fact Tables Granularity Fact Date & Time
Supplier Item Order Store Customer Warehouse Promos
Sales Master Per Transaction
Items SoldAmount Earned
x x x x x x x
Order Details Per Item x x x x x x xInventory 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
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AGGREGATE TABLE
Business Process Sales
Fact table Item_sales_master
Grain region_month_itemtype
Facts Itemunits_totalsales
Sale_date Sale_month
Warehouse Region
Store Region
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TRANSFORMATION RULESData Type Conversion 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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CUBE
Month
City
Item Type
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DiceSlice
Jan Feb MarNY
CincinnatiSF
Shirt
Jan Feb
Shirt
Jacket
Trouser
NYCincinnati
SF
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Drill down Roll up
Jan Feb Mar
Shirt
Jacket
Trouser
USA
Jan Feb MarNY
CincinnatiSF
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NY SF Cincinnati
Jan Feb
March
TrouserJacket
Shirt
Pivot
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E-commerceUser & Task Analysis (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
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E-commerceUser & Task Analysis
• 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
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