21741638 the crm data warehouse

7/29/2019 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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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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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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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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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

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

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

21741638 the crm data warehouse

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