Datwarehouse and Business Intelligence

Dashboard Design for GAP

Company Perspective

Outline

• Company Overview• Bus Matrix & Star Schema• Conformed Dimensions• Transformation Rules• Aggregate Tables • Cube• Visualizations/Dashboards• Conclusion

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

Why do DW/BI for GAP?

Data consistencyDecision supportAnalysis and ReportsOLAP ToolsVisualizationsAnalytics and Data mining

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

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

Item_sales_master

Supplier

Order_ detail

Item_ inventor

y

Item_ price

Ware house

Store

Item_ promos

Sale_ date

Item

Logical Model : Sales Schema

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

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)

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

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

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

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

Business Process Sales

Fact table Item_sales_master

Grain region_month_itemtype

Facts Itemunits_totalsales

Sale_date Sale_month

Warehouse Region

Store Region

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

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

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.

CUBE

Month

City

Item Type

DiceSlice

Jan Feb MarNY

CincinnatiSF

Shirt

Jan Feb

Shirt

Jacket

Trouser

NYCincinnati

SF

Drill down Roll up

Jan Feb Mar

Shirt

Jacket

Trouser

USA 

Jan Feb MarNY

CincinnatiSF

NY SF Cincinnati

Jan Feb

March

TrouserJacket

Shirt

Pivot

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

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