Business Intelligence/
Decision Models
Week 2
IT Infrastructure
& Marketing Database
Design and Implementation
Outline
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Issues with Mkt Databases
DBMS
Database Design and Schemas
Data Integrity and Hygiene
Demo and Lab: Table redundancy and Queries
DB Marketing Problems
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Lack of a marketing strategy.
Focus on promotions instead of relationships.
Failure to have a 3600 picture of every customer.
Failure to personalize your communications.
Building a DB and sending e-mails in house.
Getting the economics wrong.
Failure to use tests and controls.
Lack of a forceful leader.
Bad DB architecture
Corrupted data
DB Environment
Traditional Environment:
Silo Approach
Source: Laudon and Laudon 2012
Data Warehouse
Technology
Marketing Datamart
Data Warehouse Architecture
Data Warehouse Architecture
Metadata
Database Management
Systems (DBMS)
Flat Files
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Sequential
A
Name
B
C
Address
D
A
1
Transactions
2
Fixed or variable length record
3
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DBMS with VSAM Index
TN
NE
NB
IPE
QC
ON
MB
SK
AB
BC
QC
ON
ON
QC
ON
Hierarchical Indexed
Direct Access DBMS
Cust_id
Name
Purchases
Top down
Products
Indexed Direct Access
DBMS
Key
107
110
145
167
234
267
Record
4
6
1
2
5
3
Records
1
145
2
167
3
267
4
107
5
234
6
110
……….
……….
……….
……….
……….
……….
Reversed Hierarchical DBMS
Cust_id
Name
Psyte Code
Lifestyle
Purchases
Bottom up/Top down
Products
Reversed Hierarchical DBMS
NAME
PSYTE
Dubé
Smith
Bertrand
White
Harris
Habib
Jones
18
34
18
56
34
18
34
PURCHASES
120
130
150
200
50
300
430
PSYTE
NAMES
18 Dubé; Bertrand;
Habib
34 Smith; Harris; Jones
56 White
Relational Database
CUSTOMERS
ORDERS
1
PRODUCTS
Customer ID PK
Order ID PK
Product ID PK
Cust First Name
Customer ID FK
Product Name
Cust Last Name
Product ID FK
Product Description
Street
Order Date
City
Order Amount
State
Zip
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Relational DBMS
Multiple Tables
Source: Laudon and Laudon 2012
Relational DBMS
with Query
Source: Laudon and Laudon 2012
Relational Design
An Unnormalized Relation
For Order (flat file)
An unnormalized relation contains repeating groups. For example, there can be
many parts and suppliers for each order. There is only a one-to-one
correspondence between Order Number and Order Date.
Source: Laudon and Laudon 2012
Normalized Tables Created
From Order
Pros: Data integrity and updating
Cons: Processing speed for large data sets
Source: Laudon and Laudon 2012
Charitable Contributions
The “Classic” Star Schema
S to re D im e n sio n
STORE KEY
S to re D e sc rip tio n
C ity
S ta te
D istric t ID
D istric t D e sc .
R e g io n _ID
R e g io n D e sc .
R e g io n a l M g r.
Le v e l
Fa c t Ta b le
S TO R E K E Y
PRO D UC T KEY
P E R IO D K E Y
D o lla rs
U n its
P ric e
P ro d u c t D im e n sio n
PRO D UC T KEY
P ro d u c t D e sc .
B ra n d
C o lo r
S ize
M a n u fa c tu re r
Le v e l
Tim e D im e n sio n
P E R IO D K E Y
P e rio d D e sc
Year
Q u a rte r
M o n th
Day
C u rre n t Fla g
R e so lu tio n
Sequence
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A single fact table, with detail
and summary data
Fact table primary key has
only one key column per
dimension
Each key is generated
Each dimension is a single
table, highly de-normalized
Tradeoff between data integrity, updating and speed
Some alternatives: Star and Snowflake structure
Benefits: Easy to understand, easy to define hierarchies, reduces # of physical joins, low
maintenance, very simple metadata
Source: Kishore-jaladi-DW.ppt
Data Integrity and Hygiene
Illustrating Data Hygiene
Customers
Undel. 15%
Dup. 20%
CPM = $500
Price = $60
GM 50%
Quantities
2,000,000
1,700,000 15%
1,360,000 20%
2,000,000
1,700,000
1,360,000
2,000,000
1,700,000
1,360,000
Response
29,000
29,000
29,000
Cost
$1,000,000
$850,000
$680,000
Revenue
$870,000
$870,000
$870,000
BE = FC / (P-C) 1,000,000 / 30 $
BE = FC / (P-C) 850,000 / 30 $
BE = FC / (P-C) 680,000 / 30 $
33,334
28,334
22,667
29,000
29,000
29,000
29,000
29,000
29,000
Response Rate
1.45%
1.71%
2.13%
CPO
$34.48
$29.31
$23.45
Profit
-$130,000
$20,000
$190,000
ROI
-13%
2%
28%
Data Hygiene Processes (1)
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Standardize names
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Standardize addresses
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Address 1, Address 2, City, Province, Postal Code
Abbreviations (apt., ave, p.o., province)
Replace prestige names with postal addresses (i.e.
Commerce Court)
Scrubbing
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Title, First name, Initials, Family name, Suffix
Ex. c/o, co, c/o
Delivery
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FSA/LDU, Postal walk
Address change database
Data Hygiene Processes (2)
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Data Comparison
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Duplicate (cost, abuse)
Householding
• Hyphenated Names, Maiden Names, Spouse’s Name
• Recomposed Families, Roommates
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Consolidation (merge/purge)
•
•
•
•
Multiple
Multiple
Multiple
Multiple
Accounts (financial Services)
policies (insurances)
phone numbers (telco)
divisions within firm
Wrap-up
Issues with Mkt Databases
 DBMS
 Database Design and Schemas
 Data Integrity and Hygiene
 Demo and Lab: Table redundancy
and Queries
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Next Week
Data Import
 Data Preparation
 Data Transformation
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