21741638 the crm data warehouse
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CHAPTER 4
THE CRM DATA WAREHOUSE
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WHAT IS A DATA WAREHOUSE?
A large reservoir of detailed and summary datathat describes the organization and its activities,organized by the various business dimensions ina way to facilitate easy retrieval of information
describing activities data marta subset of the data warehouse,
tailored to meet the specialized needs of aparticular group of users
Top-down approach
bottom-up approach to data warehousedevelopmentthe data marts are created firstand then integrated.
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Data Warehousing Objectives
(1) keep the warehouse data current;
(2) ensure that the warehouse data is accurate;
(3) keep the warehouse data secure;
(4) make the warehouse data easily available to
authorized users;
(5) maintain descriptions of the warehouse data
so that users and system developers canunderstand the meaning of each element
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Data Warehouse vs. DBMS
OLTP (on-line transaction processing)Major task of traditional relational DBMS
Day-to-day operations: purchasing, inventory, banking,
manufacturing, payroll, registration, accounting, etc.
OLAP (on-line analytical processing)Major task of data warehouse system
Data analysis and decision making
Distinct features (OLTP vs. OLAP):
User and system orientation: customer vs. market
Data contents: current, detailed vs. historical, consolidated
Database design: ER + application vs. star + subject
View: current, local vs. evolutionary, integrated
Access patterns: update vs. read-only but complex queries
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OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision supportDB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
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DATA WAREHOUSE ARCHITECTURE
staging area datais prepared to bemoved into the warehouse data repository
and the metadata repository
metadata dataabout data, ordescriptions of the data in the data
warehouse
Exhibit 4.1: A Data Warehouse System
Model
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EXHIBIT 4.1 A DATA WAREHOUSE SYSTEM MODEL
Data gathering
systemStaging
area
Warehouse
data
repository
Information
Delivery
system
Management
and control
Metadata
repository
Data Warehouse System
Legend :
Data flow
Control flow
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A Data Warehouse System Model
Management and Controlmanagement and control componentlike a traffic
officer standing in the middle of a street intersection,controlling the flow of traffic through the intersection
Staging AreaETL extraction, transformation, and loading as the
activities of this staging area
extraction obtaining data from the internal databasesand files of systems, accomplished according to a
scheduletransformation a process that includes cleaning,
standardizing, reformatting, and summarizing
loading writing the data into the data warehouse
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A Data Warehouse System Model
WAREHOUSE DATA REPOSITORY where the warehouse data is stored within the computer system
or systems
Data Content
customer picturea compilation of geographic, demographic,activity, psychographic, and behavioral data
Data Characteristics the types of data to be processed, including considerations of
data granularity, data hierarchies, and data dimensions
Data Types fixed-length format
variable-length format
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A Data Warehouse System Model
Data Granularity
thedegree of detail that is represented by the data, where thegreater the detail, the finer the granularity
Data Hierarchies
since multiple attributes can describe a single entity, an attribute is
a data element that identifies or describes an occurrence of a dataentity (i.e., a particular customer is identified by a customer numberattribute)
Exhibit 4.2: An Example of a Data Hierarchy
Data Dimensions
for example, a manager can query the data warehouse for adisplay of data according to salesperson, customer, product,and time
Exhibit 4.3: Every Data Record Contains the Time Element
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EXHIBIT 4.2 AN EXAMPLE OF A DATA HIERARCHY
Customer
Customer number
Customer age
Customer gender
Customer marital statusCustomer number of dependents
Customer education
Customer dwelling typeCustomer state
Customer city
Customer zip code
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EXHIBIT 4.3 EVERY DATA RECORD CONTAINS THE TIME ELEMENT
Warehouse shipping order
Sales order
date
Statement
date
Date
shipped
Customer
Payment
date
Invoice
date
Customer sales order
Customer payment
Customer statement
Customer invoice
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A Data Warehouse System Model
METADATA REPOSITORY
describes the flow of data from the time that the data is captureduntil it is archived, i.e., metadata in the metadata repository forthe customer number attribute would describe its format, editingrules, and so on
TYPES OF METADATA
Metadata for Users
(analysis) identification of the source systems, the time of thelast update, the different report formats that are available, andhow to find data in the data warehouse
Metadata for Systems Developers data to allow developers to maintain, revise, and reengineer the
data warehouse system, including the various rules that wereemployed in creating the warehouse data repository, and therules for extraction, cleansing, transforming, purging, andarchiving
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A Data Warehouse System Model
Data and Process Modelsobject diagrams and entity-relationship diagrams
use cases, use case diagrams, and data flowdiagrams
CASE Toolsstands for computer-aided system engineering and is
a way to use the computer to develop systems
DBMS Systemsinclude a data dictionary component, which contains
excellent descriptions of the data in the database ordata warehouse.
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HOW DATA IS STORED IN THE DATA
WAREHOUSEdimension table a list of all of the
attributes that identify and describe a
particular entity
Exhibit 4.4: A Sample Dimension Table
fact table alist of all the facts that relateto some type of the organizations activity
Exhibit 4.5: A Sample Fact Table
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EXHIBIT 4.4 A SAMPLE DIMENSION TABLE
Customer
Customer number
Customer name
Customer phone number
Customer e-mail address
Customer territory
Customer credit code
Customer standard industry code
Customer city
Customer state
Customer zip code
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EXHIBIT 4.5 A SAMPLE FACT TABLE
Commercial Sales FactsActual sales units
Budgeted sales units
Actual sales amountBudgeted sales amount
Sales discount amount
Net sales amount
Sales commission amount
Sales bonus amount
Sales tax amount
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INFORMATION PACKAGES
atable that is maintained in the data warehouserepository that identifies both the dimensions
and the facts that relate to a business activity
Exhibit 4.6: Information Package Format keya number, such as a customer number,
that identifies a particular occurrence of the
dimension
Exhibit 4.7: A Sample Information Package
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EXHIBIT 4.6 INFORMATION PACKAGE FORMAT
Subject : Name of business activity being measured
Dimension Name Dimension Name Dimension Name Dimension Name
Dimension Key Dimension Key Dimension Key Dimension Key
Dimension 1 Dimension 1 Dimension 1 Dimension 1
Dimension 2 Dimension 2 Dimension 2 Dimension 2
Dimension 3 Dimension 3 Dimension n Dimension 3
Dimension 4 Dimension n Dimension 4
Dimension n Dimension n
Facts : Numberic measures of the business activity
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EXHIBIT 4.7 A SAMPLE INFORMATION PACKAGE
Subject : Commercial salesTime Salesperson Customer Product
Time Key Salesperson key Customer key Product key
Hour Salesperson name Customer name Product name
Day Sales branch Customer territory Product model
Month Sales region Customer credit code Product brand
Quarter Subsidiary Product line
Year
Facts : Actual sales units, budgeted sales units, actual sales amount,
budgeted sales amount, sales discount amount, net sales amount,
sales commission amount, sales bonus amount, sales tax amount
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STAR SCHEMAS
the arrangement of an information package that usually
identifies multiple dimension tables for a single fact table
and has the appearance of a star, with the fact table in
the center and the dimension tables forming the points
Exhibit 4.8: Star Schema Format
foreign keys a means of linking the fact table to the
dimension tables by means of the keys identified at the
top of the fact table where the keys identify other,
foreign tables as opposed to the fact table
Exhibit 4.9: A Sample Star Schema
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EXHIBIT 4.8 STAR SCHEMA FORMAT
Dimension 1 name
Dimension 2 name
Dimension nname
Dimension 1 key
Dimension 1
hierarchy
Dimension 2 key
Dimension 2
hierarchy
Dimension 1 key
Dimension 2 keyDimension n key
Measurable fact 2
Measurable fact 4
Measurable fact 5
Measurable fact n
Dimension n key
Dimension n
hierarchy
Business activity name
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EXHIBIT 4.9 A SAMPLE STAR SCHEMA
Customer key
Customer name
Customer type
Customer credit code
Salesperson number
Sales territory
Standard industry code
Product key
Customer key
Salesperson keyTime key
Sales units
Gross sales amount
Sales discount amount
Net sales amountSales commission amount
Salesperson keySalesperson name
Sales region
Sales branch
Product keyProduct name
Product unit price
Product quantity
Time key
Day
Month
Quarter
Year
Customer
Customer payment
Product sales facts
Time
Salesperson
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Example of Star Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
streetcity
province_or_street
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_namebranch_type
branch
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Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_solddollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
item
branch_key
branch_namebranch_type
branch
supplier_key
supplier_type
supplier
city_key
city
province_or_stree
country
city
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Example of Fact
Constellation
time_key
day
day_of_the_week
month
quarter
year
time
location_key
streetcity
province_or_street
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_namebranch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_key
shipper_name
location_keyshipper_type
shipper
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DATA WAREHOUSE NAVIGATION
summary information preprocessed data that providesthe user with exactly the content that is needed
top-down navigation the user seeks more detail in aneffort to understand the summary information
roll up navigationthe user summarizes data to seethe forest rather than the trees or to prepare summarygraphs
drill across navigation the user moves from one datahierarchy to another, i.e., information on customer sales,salesperson sales, and then product sales
Exhibit 4.10: Navigation Paths
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EXHIBIT 4.10 NAVIGATION PATHS
Summary information(Net sales for the Western sales region)
Hierarchy 1
(customer)
Hierarchy 2
(salesperson)
Hierarchy n
(product)
Detailed information
(Net sales for salesperson 3742)
Detailed data
(Sales units for salesperson 3742)
Roll up
Drill across
Drill down
Drill through
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DATA WAREHOUSE SECURITY
information systems security damage, destruction, theft, andmisuse Exhibit 4.11: The Security Action Cycle The Corporate Security Environment deterrence security policies and procedures that are intended to
deter security violations, such as guidelines for proper system use
and the requirement that users change their passwords periodically prevention measures aimed at those persons who ignore
deterrence, and include such things as locks on computer rooms,user passwords, file permissions
detection proactive actions include system audits, reports ofsuspicious activity, and virus scanning software and reactive actions
take the form of investigations remedies respond with warnings, reprimands, termination of
employment, or legal action.
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EXHIBIT 4.11 THE SECURITY ACTION CYCLE
1.
Deterrence
2.
Prevention
3.
Detection
4.
Remedies
Maximize
Deterred abuse
Maximize
Prevented abuse
Maximize
Undetected abuse
Maximize
Unpunished abuse
Deterred
abuse
Prevented
abuse
Undetected
abuse
Unpunished
abuse
Deterrencefeedback
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DATA WAREHOUSE SECURITY
Data Warehouse Security Measures network security usingprocedures such as firewalls to
restrict access to the network that houses the serversand data files, databases, data warehouses, and data
marts data security obtaining access to data onceaccess tothe network has been achieved; where, data files may belocated on multiple servers on the network, and the usermust provide a second password
database or data warehouse security thesecuritychecks of the database management system (DBMS)that may include a third password, verification of username, and also verification of access to particular datatables, records, and even record fields
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