Analyzed sales data using market analysis, SWOT analysis, GAP analysis and implemented matrices
Represented graphical charts for sales prediction(Current and Future) using Tableau and identified growth with future prediction
Designed data models, logical models, data mart, data warehouse, relational database, star schema, extended star schema, tables, columns, attributes, relationship (primary, foreign , composite keys), sorting
1. Design of Data Warehouse & Business Intelligence
System
Presented by:
Trupti shingala
For E-Commerce of jcpenney
Professor: Joseph Morabito
MIS 636 Data Warehousing and Business Intelligence
2. COMPANY PROFILE
J.C. Penney Corporation, Inc., is a chain of American mid-range department
stores based in Plano, Texas.
The company operates 1,060 department stores in 49 U.S Stores.
In addition to selling conventional merchandise, JCPenney stores often house
several leased departments such as Sephora, Seattle's Best Coffee, salons,
optical centers, portrait studios, and jewelry repair.
The company has been an Internet retailer since 1998. It has streamlined
its catalog and distribution while undergoing renovation improvements at
store level.
3. E-COMMERCE WITH DW AND BI
Enhance business intelligence
Feed business with
Right information at right time
Improve business decision
Accurate data consistency and analysis
5. OPPORTUNITY MATRIX - SALES
Business Processes
Customer
Service
Financial Operations HR Marketing
Strategy
Management
Store Sales x x x x x x
Online Sales x x x x x
Vendor Sales x x
Order Process x
Delivery Process x
Return Policy x x
6. REASON FOR E-COMMERCE:
PRIORITIZATION GRID
BP1: Store Sales BP3: Vendor Sales BP5: Deliver process
BP2: Online Sales BP4: Order Process BP6: Return Policy
7. HIGH LEVEL BUS MATRIX
Business
Processes
Date
and
Time
Product Customer Location Policy Employees Vendors
Transa
ction
Promotions
In-store Sales x x x x x x x x
Online Sales x x x x x x
Vendor Sales x x x x X x
Order
Processing
x x X x
Delivery x x X x x x
Return Policy x x x x x x
8. DETAILED BUS MATRIX
Business Process Fact tables Granularity Fact
Dat
e
Pro
duc
t
Cus
tom
er
Loc
atio
n
Serv
ice
poli
cy
Emp
loye
es
Ven
dor
Tra
nsa
ctio
n
Pro
mot
ions
In-store Sales Transaction
Sales transaction Per line item
purchase date key
purchase amount key
purchase unit price key
transaction number
x x x x x x x
Location Per location
Store key
Location key
Area key
x x x
Online Sales
Sales transaction Per line item
purchase date key
purchase amount key
purchase unit price key
transaction number
x x x x x x x
Location Per location
Store key
Location key
Area key
x x x
Vendors Sales Transaction
Sales transaction Per line item
purchase date key
purchase amount key
purchase unit price key
transaction number
x x x x x x x
Vendor information Per Vendor
Vendor key
Vendor item key
x x x x x x
Order Process
warehouse picking Per warehouse receipt
ship date key
requested date key
product key
vendor key
x x x x
billing and invoicing Per order
date key
order number
quantity key
product key
…
x x x x
Delivery Process
shipping notice Per line item
shipping date key
shipping cost key
tracking number
delivery company key
….
x x x x x
delivery operation Per line item
tracking number
shipping date
delivery cost key
….
x x x x
Return Policy
store return process Per line item
store number key
item number key
return date key
….
x x x x x
mail return process Per line item
tracking number
return date key
x x x x
9. LOGICAL FACT TABLE DIAGRAM:
SALES TRANSACTION
Sales Transaction
Fact Table
Grain:
Line Item on
Online and Store
sale Transaction
Vendors
Service
Policy
Shipment &
order
details
Product
Transaction
Date &
Time
Employee
Customer
Profile
Payment
Store
Location
Promotion
Inventory
10. DETAILED FACT TABLE:
SALES TRANSACTION
Dimensions are
collection of
reference information
about a fact table
Grain is depicts what
a single fact table
record represents
Sale Transaction Line Item Fact
Table
Transaction_Key
Product_Key
Inventory_Key
Date & Time_Key
Shipment & Order Details_Key
Customer Profile_Key
Promotion_Key
Payment_Key
Store Location_Key
Line_Item_Quantity
Line_Item_Total Price
11. START SCHEMA:
SALE TRANSACTION LINE ITEM FACT
Sale Transaction Line Item Fact
Table
Transaction_ID (PK)
Product_ID (PK)
Inventory_ID (PK)
Date & Time_ID (PK)
Shipment & Order Details_ID (PK)
Employee_ID (PK)
Cust_ID (PK)
Promotion_ID (PK)
Payment_ID (PK)
Line_Item_
Line_Item_Total Price
Product
Product_ID (PK)
Name
Category
Price
Gender
Description
Size
Review Rate
Shipment & Order Deatils
Shipment & Order Deatils_ID(PK)
Shipment method
Shipment Address
Order Tracking Detail
Store Location ID
Transaction
Transaction_ID(PK)
Transaction_Type
Transaction_Detail
Inventory
Inv_ID(PK)
Prod_Quant
Prod_Detail
Prod_Location
Date & Time
Date & Time_ID(PK)
Date
Week_Day
Calendar_Detail
Customer
Cust_ID (PK)
Name
Gender
Address
Contact details
Birth date
Promotion
Promotion_ID(PK)
Promo_Type
Promo_Disc
Promo_Duration
Payment
Payment_ID (PK)
Pay_Method
Pay_Accnt_Detail
Pay_Security
Cust_Promo_Bridge
Promotion_ID(PK)
Cust_ID(PK)
Percent_Cust_Promo
Location
Store_id(PK)
Location_name
Location_zipcode
12. DIMENSION ATTRIBUTE:
DETAILED DESCRIPTION:PRODUCT KEY
Attribute Name Attribute Description Cardinality Slowly Changing
Dimension
Sample Values
Product_ID Product ID uniquely identifies each product in
the system of JCPENNEY
20,000,000 (EST) Not updatable 15896457, 12325785,
05896412
Name Name of Product with Description 90,999 (EST) Not updatable Arizona, Biosilk, ALYX
Category Full descriptive name of category which is
belongs to particular product
60 Type 2 Clothes, Jewelry, Furniture
Price Numbers given to particular product to identify
price of that product
- Type 1 -
Gender Full description of gender that products suits 3 Not updatable Women, Men, Kids
Description Full description of content which depicts
detailed information of product and it’s
functionality
90,999(EST) Type 1 Bed & Bath
Size The size of product 26 Not updatable S, XS, L, XL, 8,16
Review Rate Number which depicts review rate of customer 6 Not updatable 1,2,3,4,5,6
14. CONFORMED DIMENSIONS
The following are the conformed Dimension
Date and Time Dimension
Product Dimension
Customer Dimension
Shipment and order details
Transaction Dimension
15. TRANSFORMATION RULE:
IN STORE SALES
Attribute Rule Type Details
Sales Date and Time Replace Transform the date format of sales
from DDMMYYYY to YYYYMMDD.
Sales Amount Formula Amount converts in to two decimal
point
Sales Discount Constant This transformation rule will fill the
target field with a specified value.
Sales Total Formula Derive the sum of purchased
products to get final amount
17. AGGREGATE TABLE
Sales Transaction
Product ID Location Time
Product 1 Product 2 Worldwide Year1 Year2
Product ID 1 Product Name 1 Product ID 2
Product Name
2
Country 1 Country 2 Quarter 1 Quarter 2 Q1 Q2
Region 1 Region 2 Region 1 Region 2 Month 1 Month 2 M1 M2
D1 D2 D1 D2 D1 D2
Line Item
Line_Item Total
Price
Location ID
Date & Time Key
Product ID
-------------
18. AGGREGATE TABLE
In Store Sales Transaction
Product ID Location Time
Product 1 Product 2 Worldwide Year1 Year2
Country 1 Country 2 Quarter 1 Quarter 2 Q1 Q2
Line Item
Line_Item Total Price
Location ID
Date & Time Key
Product_ID
-------------
…
19. In Store Sales Transaction
Product ID Location Time
Product 1 Product 2 Worldwide Year1 Year2
Country 1 Country 2 Quarter 1 Quarter 2 Q1 Q2
Line Item
M1 M2 M1 M2 … …
Line_Item Total Price
Location ID
Date & Time Key
Product_ID
-------------
…
AGGREGATE TABLE
22. USER ROLES AND DELIVERY
USER ROLE DELIVERY VALUE
Executives Portal, Reports Emailed
Exploratory and Informative Visualization
for Analysis
Business Analyst OLAP, Portal, Drill-Down, Drill-Across
Exploratory and Informative Visualization
for Analysis & Reporting
Knowledge Worker Portal, Reports Emailed Operational Reports
Managers Portal, Reports, Drill-Down Informative Visualization
Operational Workers Portal Low Level Entry Point, Drill-Down Informative Visualization
Customers In hand Receipt NA
A data warehouse provides a basis for online analytic processing and data mining for improving business intelligence by turning data into information and knowledge.
Since technologies for e-commerce are being rapidly developed and e-businesses are rapidly expanding, analyzing e-business environments using data warehousing technology could enhance significant business intelligence.
A well-designed data warehouse would feed business with the right information at the right time in order to make the right decisions in ecommerce environments.
J.C. Penney is in the midst of an e-commerce renaissance
J. C. Penney Company, Inc., incorporated on January 22, 2002, is a holding company whose operating subsidiary is J. C. Penney Corporation, Inc. (JCP). The Company’s business consists of selling merchandise and services to consumers through its department stores and through its Internet Website. Department stores and Internet serve the customers and provide the same mix of merchandise and department stores accept returns from sales made in stores and via the Internet. The Company sells family apparel and footwear, accessories, fine and fashion jewelry, beauty products through Sephora inside JCPenney and home furnishings. In addition, the Company’s department stores provide its customers with services, such as styling salon, optical, portrait photography and custom decorating.
As of February 1, 2014, the Company supply chain network operated 25 facilities at 14 locations, of which nine were owned, with distribution activities housed in owned locations. The Company’s network includes 11 store merchandise distribution centers, seven regional warehouses, three jcpenney.com centers and four furniture distribution centers.