The CRM Data Warehouse. I. Introduction to data warehouse II. Data warehouse architecture III. Data and process models

The CRM Data The CRM Data WarehouseWarehouse

The CRM Data WarehouseThe CRM Data Warehouse

I.I. Introduction to data warehouseIntroduction to data warehouse

II.II. Data warehouse architectureData warehouse architecture

III.III. Data and process modelsData and process models

I. Introduction to data I. Introduction to data warehousewarehouse

1-1 Definition of data warehouse1-1 Definition of data warehouse

1-2 Data warehouses and data marts1-2 Data warehouses and data marts

1-3 Data warehousing objectives1-3 Data warehousing objectives

1-1 Definition of data 1-1 Definition of data warehousewarehouse

data warehouse—a large reservoir of data warehouse—a large reservoir of detailed and summary data that detailed and summary data that describes the organization and its describes the organization and its activities, organized by the various activities, organized by the various business dimensions in a way to business dimensions in a way to facilitate easy retrieval of information facilitate easy retrieval of information describing activitiesdescribing activities

1-2 Data warehouse and data 1-2 Data warehouse and data martsmarts

data mart –a subset of the data data mart –a subset of the data warehouse, tailored to meet the warehouse, tailored to meet the specialized needs of a particular specialized needs of a particular group of usersgroup of users

bottom-up approach to data bottom-up approach to data warehouse development—the data warehouse development—the data marts are created first and then marts are created first and then integrated.integrated.

1-3 Data warehouse objectives1-3 Data warehouse objectives

five objectives of data warehousing—five objectives of data warehousing—(1) keep the warehouse data current; (1) keep the warehouse data current; (2) ensure that the warehouse data is (2) ensure that the warehouse data is accurate; (3) keep the warehouse data accurate; (3) keep the warehouse data secure; (4) make the warehouse data secure; (4) make the warehouse data easily available to authorized users; easily available to authorized users; and, (5) maintain descriptions of the and, (5) maintain descriptions of the warehouse data so that users and warehouse data so that users and system developers can understand the system developers can understand the meaning of each elementmeaning of each element

II. Data warehouse architectureII. Data warehouse architecture

staging area—datastaging area—data is prepared to be is prepared to be moved into the warehouse data moved into the warehouse data repository and the metadata repository and the metadata repositoryrepository

metadata—datametadata—data about data, or about data, or descriptions of the data in the data descriptions of the data in the data warehouse warehouse • Exhibit 4.1: A Data Warehouse System Exhibit 4.1: A Data Warehouse System

ModelModel

II. Data warehouse architectureII. Data warehouse architecture

2-1 Management and control2-1 Management and control

2-2 Staging area2-2 Staging area

2-3 Warehouse data repository2-3 Warehouse data repository

2-4 Metadata repository2-4 Metadata repository

2-1 Management and control2-1 Management and control

management and control componentmanagement and control component—like a traffic officer standing in the —like a traffic officer standing in the middle of a street intersection, middle of a street intersection, controlling the flow of traffic through controlling the flow of traffic through the intersectionthe intersection

2-2 Staging area2-2 Staging area

ETL—extraction, transformation, and ETL—extraction, transformation, and loading as the activities of this staging arealoading as the activities of this staging area

extraction—obtaining data from the internal extraction—obtaining data from the internal databases and files of systems, databases and files of systems, accomplished according to a scheduleaccomplished according to a schedule

transformation—a process that includes transformation—a process that includes cleaning, standardizing, reformatting, and cleaning, standardizing, reformatting, and summarizingsummarizing

loading—writing the data into the data loading—writing the data into the data warehousewarehouse

2-3 Warehouse data repository(1/4)2-3 Warehouse data repository(1/4)

warehouse data repository—where the warehouse data repository—where the warehouse data is stored within the warehouse data is stored within the computer system or systemscomputer system or systems

2-3-1 Data content2-3-1 Data content customer picture—a compilation of :customer picture—a compilation of :

• Geographic dataGeographic data• Demographic dataDemographic data• Activity dataActivity data• Psychographic dataPsychographic data• Behavior dataBehavior data


2-3-2 Data Characteristics2-3-2 Data Characteristics data characteristics—the types of data characteristics—the types of

data to be processed, including data to be processed, including considerations of data granularity, considerations of data granularity, data hierarchies, and data data hierarchies, and data dimensionsdimensions


Data TypesData Types• fixed-length format—for example, a customer number element may fixed-length format—for example, a customer number element may

be specified as a field that is always 8 positions with the positions be specified as a field that is always 8 positions with the positions always consisting of numeric dataalways consisting of numeric data

• variable-length format—for example would be a customer’s name variable-length format—for example would be a customer’s name which might vary from 20 positions to 50 depending on name which might vary from 20 positions to 50 depending on name length or would be textual data, such as comments that a customer length or would be textual data, such as comments that a customer might entermight enter

Data GranularityData Granularity• data granularity—thedata granularity—the degree of detail that is represented by the degree of detail that is represented by the

data, where the greater the detail, the finer the granularitydata, where the greater the detail, the finer the granularity


Data HierarchiesData Hierarchies• data hierarchy—sincedata hierarchy—since multiple attributes can describe a single multiple attributes can describe a single

entity, an attribute is a data element that identifies or describes an entity, an attribute is a data element that identifies or describes an occurrence of a data entity (i.e., a particular customer is identified occurrence of a data entity (i.e., a particular customer is identified by a customer number attribute)by a customer number attribute)

• Exhibit 4.2: An Example of a Data HierarchyExhibit 4.2: An Example of a Data Hierarchy Data DimensionsData Dimensions

• dimensional structure—for example, a manager can query the data dimensional structure—for example, a manager can query the data warehouse for a display of data according to salesperson, warehouse for a display of data according to salesperson, customer, product, and timecustomer, product, and time

• Exhibit 4.3: Every Data Record Contains the Time ElementExhibit 4.3: Every Data Record Contains the Time Element

2-4 Metadata repository2-4 Metadata repository

metadata repository—describes the flow of metadata repository—describes the flow of data from the time that the data is data from the time that the data is captured until it is archived, i.e., metadata captured until it is archived, i.e., metadata in the metadata repository for the in the metadata repository for the customer number attribute would describe customer number attribute would describe its format, editing rules, and so onits format, editing rules, and so on

Types of metadataTypes of metadata• Metadata for usersMetadata for users• Metadata for systems developersMetadata for systems developers• Sources of metadataSources of metadata

Types of metadataTypes of metadata Metadata for UsersMetadata for Users

• user metadata examples—identification of the source systems, user metadata examples—identification of the source systems, the time of the last update, the different report formats that the time of the last update, the different report formats that are available, and how to find data in the data warehouseare available, and how to find data in the data warehouse

Metadata for Systems DevelopersMetadata for Systems Developers• system developer metadata examples—data to allow system developer metadata examples—data to allow

developers to maintain, revise, and reengineer the data developers to maintain, revise, and reengineer the data warehouse system, including the various rules that were warehouse system, including the various rules that were employed in creating the warehouse data repository, and the employed in creating the warehouse data repository, and the rules for extraction, cleansing, transforming, purging, and rules for extraction, cleansing, transforming, purging, and archivingarchiving

SOURCES OF METADATASOURCES OF METADATA• metadata sources—occur naturally as a byproduct of the metadata sources—occur naturally as a byproduct of the

organization’s previous and ongoing systems development organization’s previous and ongoing systems development efforts; can come from data and process models, CASE tools, efforts; can come from data and process models, CASE tools, and database management systemsand database management systems

III. Data and process modelsIII. Data and process models data model examples—object diagrams and data model examples—object diagrams and

entity-relationship diagrams entity-relationship diagrams process models examples—use cases, use case process models examples—use cases, use case

diagrams, and data flow diagrams diagrams, and data flow diagrams CASE ToolsCASE Tools

• CASE—stands for computer-aided system engineering CASE—stands for computer-aided system engineering and is a way to use the computer to develop systems and is a way to use the computer to develop systems

DBMS SystemsDBMS Systems• Database management systems—include a data Database management systems—include a data

dictionary component, which contains excellent dictionary component, which contains excellent descriptions of the data in the database or data descriptions of the data in the database or data warehouse. warehouse.

III. Data and process modelsIII. Data and process models

3-1 How data is stored in data 3-1 How data is stored in data warehousewarehouse

3-2 Information packages3-2 Information packages

3-3 Data warehouse navigation3-3 Data warehouse navigation

3-4 Data warehouse security3-4 Data warehouse security

3-1 How data is stored in data 3-1 How data is stored in data warehousewarehouse

dimension table—a list of all of the dimension table—a list of all of the attributes that identify and describe attributes that identify and describe a particular entitya particular entity• Exhibit 4.4: A Sample Dimension TableExhibit 4.4: A Sample Dimension Table

fact table—afact table—a list of all the facts that list of all the facts that relate to some type of the relate to some type of the organization’s activityorganization’s activity• Exhibit 4.5: A Sample Fact TableExhibit 4.5: A Sample Fact Table

3-2 Information packages3-2 Information packages

information package—ainformation package—a table that is table that is maintained in the data warehouse maintained in the data warehouse repository that identifies both the repository that identifies both the dimensions and the facts that relate dimensions and the facts that relate to a business activityto a business activity• Exhibit 4.6: Information Package FormatExhibit 4.6: Information Package Format• Exhibit 4.7: A Sample Information Exhibit 4.7: A Sample Information

PackagePackage

3-2 Information packages3-2 Information packages star schema—the arrangement of an information package star schema—the arrangement of an information package

that usually identifies multiple dimension tables for a single that usually identifies multiple dimension tables for a single fact table and has the appearance of a star, with the fact fact table and has the appearance of a star, with the fact table in the center and the dimension tables forming the table in the center and the dimension tables forming the points points • Exhibit 4.8: Star Schema FormatExhibit 4.8: Star Schema Format• Exhibit 4.9: A Sample Star SchemaExhibit 4.9: A Sample Star Schema

key—a number, such as a customer number, that identifies key—a number, such as a customer number, that identifies a particular occurrence of the dimensiona particular occurrence of the dimension

foreign keys—a means of linking the fact table to the foreign keys—a means of linking the fact table to the dimension tables by means of the keys identified at the top dimension tables by means of the keys identified at the top of the fact table where the keys identify other, “foreign” of the fact table where the keys identify other, “foreign” tables as opposed to the fact tabletables as opposed to the fact table

3-3 Data warehouse navigation3-3 Data warehouse navigation

Guidelines for OLAP addressed ease of uGuidelines for OLAP addressed ease of use by Dr. Codd’s in 1993se by Dr. Codd’s in 1993

3-3 Data warehouse navigation3-3 Data warehouse navigation summary information—preprocessed data that summary information—preprocessed data that

provides the user with exactly the content that is provides the user with exactly the content that is neededneeded

top-down navigation—the user seeks more detail top-down navigation—the user seeks more detail in an effort to understand the summary in an effort to understand the summary informationinformation

roll up navigation—the user summarizes data to roll up navigation—the user summarizes data to “see the forest rather than the trees” or to “see the forest rather than the trees” or to prepare summary graphsprepare summary graphs

drill across navigation—the user moves from one drill across navigation—the user moves from one data hierarchy to another, i.e., information on data hierarchy to another, i.e., information on customer sales, salesperson sales, and then customer sales, salesperson sales, and then product salesproduct sales• Exhibit 4.10: Navigation PathsExhibit 4.10: Navigation Paths

3-4 Data warehouse security(1/5)3-4 Data warehouse security(1/5)

information systems security—those information systems security—those measures that are instituted to measures that are instituted to reduce or eliminate the risks that reduce or eliminate the risks that information systems face, including information systems face, including such acts as damage, destruction, such acts as damage, destruction, theft, and misusetheft, and misuse• Exhibit 4.11: The Security Action CycleExhibit 4.11: The Security Action Cycle


The Corporate Security Environment The Corporate Security Environment (1/2)(1/2)• deterrence—security policies and procedures deterrence—security policies and procedures

that are intended to deter security violations, that are intended to deter security violations, such as guidelines for proper system use and such as guidelines for proper system use and the requirement that users change their the requirement that users change their passwords periodicallypasswords periodically

• prevention—measures aimed at those persons prevention—measures aimed at those persons who ignore deterrence, and include such things who ignore deterrence, and include such things as locks on computer rooms, user passwords, as locks on computer rooms, user passwords, file permissions, or hiring outside consultants file permissions, or hiring outside consultants to “break into” the systemto “break into” the system


The Corporate Security Environment The Corporate Security Environment (2/2)(2/2)• detection—andetection—an alert to breaches or (more alert to breaches or (more

ideally) potential breaches in security; where, ideally) potential breaches in security; where, proactive actions include system audits, proactive actions include system audits, reports of suspicious activity, and virus reports of suspicious activity, and virus scanning software and reactive actions take scanning software and reactive actions take the form of investigationsthe form of investigations

• remedies—usingremedies—using knowledge that a security knowledge that a security breach has occurred and who committed it, the breach has occurred and who committed it, the organization can respond with warnings, organization can respond with warnings, reprimands, termination of employment, or reprimands, termination of employment, or legal action. legal action.


onion measures—suggest that to access onion measures—suggest that to access the data, you have to go through the the data, you have to go through the various security layers which protect the various security layers which protect the network, files, and the database or data network, files, and the database or data warehousewarehouse

network security—usingnetwork security—using procedures such procedures such as firewalls to restrict access to the as firewalls to restrict access to the network that houses the servers and data network that houses the servers and data files, databases, data warehouses, and files, databases, data warehouses, and data marts data marts


data security—obtaining access to data oncedata security—obtaining access to data once access to the network has been achieved; where, access to the network has been achieved; where, data files may be located on multiple servers on data files may be located on multiple servers on the network, and the user must provide a second the network, and the user must provide a second password and also be screened in terms of which password and also be screened in terms of which files may be made available and/or which files may be made available and/or which operations can be performed on the file dataoperations can be performed on the file data

database or data warehouse security—thedatabase or data warehouse security—the security checks of the database management security checks of the database management system (DBMS) that may include a third system (DBMS) that may include a third password, verification of user name, and also password, verification of user name, and also verification of access to particular data tables, verification of access to particular data tables, records, and even record fieldsrecords, and even record fields

Documents

The CRM Data Warehouse. I. Introduction to data warehouse II. Data warehouse architecture III. Data and process models