Business Intelligence/ Decision Models Week 2 IT Infrastructure & Marketing Database Design and Implementation

Outline Issues with Mkt Databases DBMS Database Design and Schemas Data Integrity and Hygiene Demo and Lab: Table redundancy and Queries

DB Marketing Problems Lack of a marketing strategy. Focus on promotions instead of relationships. Failure to have a picture of every customer. Failure to personalize your communications. Building a DB and sending s 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

Metadata

Database Management Systems (DBMS)

Flat Files Sequential Fixed or variable length record ABC D NameAddress Transactions  A

DBMS with VSAM Index QC ON QC ON TN NE NB IPE QC ON MB SK AB BC

Hierarchical Indexed Direct Access DBMS Cust_id NamePurchasesProducts Top down

Indexed Direct Access DBMS Key Record Records 1145………. 2167………. 3267………. 4107………. 5234………. 6110……….

Reversed Hierarchical DBMS Cust_id NamePurchases Products Psyte Code Lifestyle Bottom up/Top down

Reversed Hierarchical DBMS NAME PSYTE PURCHASES Dubé18120 Smith34130 Bertrand18150 White56200 Harris34 50 Habib18300 Jones34430 PSYTE NAMES 18 Dubé; Bertrand; Habib 34 Smith; Harris; Jones 56 White

Relational Database CUSTOMERS ORDERS PRODUCTS Customer ID PKOrder ID PKProduct ID PK Cust First NameCustomer ID FKProduct Name Cust Last NameProduct ID FKProduct Description StreetOrder Date CityOrder Amount State Zip 1 

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

Source: Kishore-jaladi-DW.ppt The “Classic” Star Schema  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

Data Integrity and Hygiene

Data Integrity Issues Duplicates (with variations) Individuals with similar names Customer reappearances Change of addresses Incomplete addresses Transcription errors Change of names

Illustrating Data Hygiene Quantities ResponseResponse Rate Customers2,000,000 29, % Undel. 15%1,700,00015%29, % Dup. 20%1,360,00020%29, % Cost CPO CPM = $5002,000,000$1,000,00029,000$ ,700,000$850,00029,000 $ ,360,000$680,00029,000 $23.45 Revenue Profit ROI Price = $602,000,000$870,00029,000-$130, % GM 50%1,700,000$870,00029,000$20,000 2% 1,360,000$870,00029,000$190,000 28% BE = FC / (P-C)1,000,000 / 30 $ 33,334 BE = FC / (P-C) 850,000 / 30 $ 28,334 BE = FC / (P-C) 680,000 / 30 $ 22,667

Data Hygiene Processes (1) Standardize names Title, First name, Initials, Family name, Suffix Standardize addresses 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 Ex. c/o, co, c/o Delivery FSA/LDU, Postal walk Address change database

Data Hygiene Processes (2) Data Comparison Duplicate (cost, abuse) Householding Hyphenated Names, Maiden Names, Spouse’s Name Recomposed Families, Roommates Consolidation (merge/purge) Multiple Accounts (financial Services) Multiple policies (insurances) Multiple phone numbers (telco) Multiple 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

Next Week Data Import Data Preparation Data Transformation