The document contains an end to end data warehouse design - from SKU procurement to SKU Sale. Additionally, a BI dashboard has been created in Tableau, to mine the warehouse, with SKU as the grain. The data can be aggregated at levels of Supplier/Store/Location/Inventory/Sale Date/Time in Warehouse etc.

1. Datwarehouse and Business
Intelligence Dashboard
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?
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 stores
Revenue over 3 billion
-Wikipedia

4. Why do DW/BI for 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

7. Item_sales_master
Supplier
Order_
detail
Item_
inventor
y
Item_
price
Ware
house
Store
Item_
promos
Sale_
date
Item
Logical Model : Sales Schema

8. Confirmed Dimensions
warehouse (wh)
wh_id
wh_name
wh_city
wh_country
wh_type
store
store_id(PK)
store_name
store_city
store_cty
store_type
supplier
supplier_id(PK)
supplier_country
supplier_city
supplier_name
date_joined
sup_loc_id
supplier_type
item
item_id(PK)
item_color
item_size
item_dept
item_brand
order_detail
order_no
item_id
date_creation
shipment_id
order_status
item_type
order_qty
Item_sale_master
Order_master

9. E-commerce
Dimension 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:
Categories
Gender
Size
Color
Price Band
Collection
Review comment
Review rate (9)

10. 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 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 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

12. 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

13. Fact Tables Granularity Fact 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
Fact table 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 Granularity Fact 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 Process Sales
Fact table Item_sales_master
Grain region_month_itemtype
Facts Itemunits_totalsales
Sale_date Sale_month
Warehouse Region
Store Region

17. TRANSFORMATION RULES
Data 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.

18. CUBE
Month
City
Item Type

19. Dice
Slice
Jan Feb Mar
NY
Cincinnati
SF
Shirt
Jan Feb
Shirt
Jacket
Trouser
NY
Cincinnati
SF

20. wn Roll up
Jan Feb Mar
Shirt
Jacket
Trouser
USA
Mar
NY
Cincinnati
SF

21. NY SF Cincinnati
Jan
Feb
March
Trouser
Jacket
Shirt
Pivot

22. E-commerce
User & 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

23. E-commerce
User & 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