1 Dr. Chen. I n t r o d u c t i o n t o Decision Support Systems Professor Jason Chen School of...

1 Dr. Chen.

I n t r o d u c t i o n t o Decision Support Systems

Professor Jason Chen School of BusinessGonzaga UniversitySpokane, WA 99258chen@gonzaga.edu

mbus633 Copyright ©, Dr. Chen

Decision Support,

E-Business, and

OLAP

Decision Support,e-Business, andOLAP

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Objectives

• Identify the changes taking place in the form and use of decision support in E-Business enterprises.

• Identify the role and reporting alternatives of management information systems.

• Describe how online analytical processing can meet key information needs of managers.

• Explain the decision support system concept and how it differs from traditional management information systems.

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Objectives (cont.)

• Explain how executive information systems can support the information needs of executives and managers.

• Explain organizations are warehousing and mining data.

• Give examples of several ways expert systems can be used in business decision-making situations.

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Enterprise Information Portals and DSS

Enterprise Information Portal GatewayEnterprise Information Portal User Interface

SearchAgents

SearchAgents OLAPOLAP Data

Mining

Data Mining

KnowledgeManagement

KnowledgeManagement

Database Management Functions

DataMart

OtherBusiness

Applications

OperationalDatabase

AnalyticalDatabase

KnowledgeBase

DSS

What-If ModelsSensitivity ModelsGoal-Seeking ModelsOptimization Models

Internet Intranet Extranet

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Decisions in the E-Business

StrategicManagement

TacticalManagement

OperationalManagement

Dec

isio

ns

Information

Decision Characteristics

Unstructured

Semi-structured

Structured

Planning and Control of Overall Organizational Direction by Top Management

Planning and Control of Organizational Subunits by Middle Management

Planning and Control of Day to Day Operations by Supervisory Management

DATA WORKERSDATA WORKERS

KIND OF SYSTEM GROUPS SERVEDKIND OF SYSTEM GROUPS SERVED

STRATEGIC LEVEL SENIOR STRATEGIC LEVEL SENIOR (ESS,EIS,DSS) (ESS,EIS,DSS) MANAGERSMANAGERS

MANAGEMENT LEVEL MIDDLE MANAGEMENT LEVEL MIDDLE (DSS, MIS)(DSS, MIS) MANAGERSMANAGERS

OPERATIONAL OPERATIONAL

OPERATIONAL LEVEL (TPS,OAS) OPERATIONAL LEVEL (TPS,OAS) MANAGERS MANAGERS

KNOWLEDGE LEVEL KNOWLEDGE & KNOWLEDGE LEVEL KNOWLEDGE &

(KWS)(KWS)

SALES & MANUFACTURING FINANCE ACCOUNTING HUMANSALES & MANUFACTURING FINANCE ACCOUNTING HUMAN RESOURCESRESOURCESMARKETINGMARKETING

Types of the Information Systems Types of the Information Systems

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Management Information System (DBMS) Reports

Periodic ScheduledReports

Periodic ScheduledReports

Exception ReportsException Reports

Demand Reportsand Responses

Demand Reportsand Responses

Push ReportsPush Reports

MajorManagementInformation Systems (DBMS) Reports

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Task Environment

User

SoftwareSystem

DBMS MBMS

DGMS

The Decision Support Systems

DBMS: DataBase Management Systems

MBMS: ModelBase Management Systems

DGMS: DialoGue Management Systems

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Decision Support Systems

What If-AnalysisWhat If-Analysis

Sensitivity AnalysisSensitivity Analysis

Goal-Seeking AnalysisGoal-Seeking Analysis

Optimization AnalysisOptimization Analysis

ImportantDecision SupportSystemsAnalytical Models

ImportantDecision SupportSystemsAnalytical Models

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OnLine Analytical Processing (OLAP)

OLAPServerOLAPServer

Multi-dimensional

database

CorporateDatabases

Client PC

Web-enabled OLAPSoftware

Data is retrieved from corporate databasesand staged in an OLAP multi-dimensional database

•Operational DB•Data Marts•Data Warehouse

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Tools used in the User Interface

A variety of tools used by OLAP to query and analyze data stored in data warehouse and data marts:

Traditional query and reporting tools (SQL, QBE, QBF)

Spreadsheets Data Mining tools. Data Visualization tools.

*

COMPONENTS OF DATA WAREHOUSE

INFORMATIONDIRECTORY

INTERNALDATASOURCES

EXTERNALDATASOURCES

OPERATIONAL,HISTORICAL DATA

DATA WAREHOUSE

EXTRACT,TRANSFORM

DATAACCESS &ANALYSIS

QUERIES &REPORTS

OLAP

DATA MINING

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Three-layer data warehouse architecture

1. Operational data and systems

2. EDW

3. DM

Quality,Integrity, and

Historical Data

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Definitions

• Data Warehouse: An integrated and consistent store of subject-oriented data that is obtained from a variety of sources and formatted into a meaningful context to support decision-making in an organization.

• Bill Inmon, the acknowledged father of the Data Warehouse, defines it as an integrated, subject-oriented, time-variant, non-volatile database that provides support for decision making.

A Comparison of Data Warehouse andOperational Database Characteristics

Characteristics Operational Database Data Data Warehouse Data

Integrated Similar data can have differentrepresentations or meaning (e.g.,social security numbers, businessentities)

Provide a unified view of all dataelements with a common definitionand representation for alldepartments.

Subject-Oriented Data are stored with a functional orprocess orientation (e.g., invoices,credits, debits, etc.)

Data are stored with a subjectorientation that facilitates multipleviews for data and decision making(e.g., sales, products, sales byproducts, etc.)

Time-Variant Data represent current transaction(e.g., the sales of a product in a givendate).

Data are historic in nature. A timedimension is added to facilitate dataanalysis and time comparisons.

Non-Volatile Data updates and deletes are verycommon.

Data cannot be changed. Data areonly added periodically fromoperational systems. Once data arestored no changes are allowed. (Butcomputed data are updated)

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OLAP Activities

• Generating queries

• Requesting ad hoc reports

• Conducting statistical and other analyses

• Developing multimedia applications

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Using SQL for Querying

• SQL (Structured Query Language)Data language English-like, nonprocedural, very user friendly language,Free format

Example:SELECT Name, SalaryFROM EmployeesWHERE Salary >2000

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Data Mining

• Knowledge discovery in databases

• Knowledge extraction

• Data archeology

• Data exploration

• Data pattern processing

• Data dredging

• Information harvesting

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Data Mining Examples

• A telephone company used a data mining tool to analyze their customer’s data warehouse. The data mining tool found about 10,000 supposedly residential customers that were expending over $1,000 monthly in phone bills.

• After further study, the phone company discovered that they were really small business owners trying to avoid paying business rates

*

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Other Data Mining Examples

• 65% of customers who did not use the credit card in the last six months are 88% likely to cancel their accounts.

• If age < 30 and income <= $25,000 and credit rating < 3 and credit amount > $25,000 then the minimum loan term is 10 years.

• 82% of customers who bought a new TV 27" or larger are 90% likely to buy an entertainment center within the next 4 weeks.

Brand Package Size SalesSoftTowel 2-pack $75SoftTowel 3-pack $100SoftTowel 6-pack $50

Brand Package Size Color SalesSoftTowel 2-pack While $30SoftTowel 2-pack Yellow $25SoftTowel 2-pack Pink $20SoftTowel 3-pack While $50SoftTowel 3-pack Yellow $25SoftTowel 3-pack Pink $25SoftTowel 6-pack While $30SoftTowel 6-pack Yellow $20

Example of drill-down

(b) Drill-down with color added

(a) Summary report

$75

TM 14-21

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Multidimensionality

• 3-D + Spreadsheets (OLAP has this)

• Data can be organized the way managers like to see them, rather than the way that the system analysts do

• Different presentations of the same data can be arranged easily and quickly

• Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry

• Measures: money, sales volume, head count, inventory profit, actual versus forecast

• Time: daily, weekly, monthly, quarterly, or yearly

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Slicing a data cube

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Slicing a data cube

Regions

Salespersons

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Multidimensionality Limitations

• Extra storage requirements

• Higher cost

• Extra system resource and time consumption

• More complex interfaces and maintenance

Multidimensionality is especially popular in executive information and support systems (EIS and ESS)

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Data Visualization and Multidimensionality

Data Visualization Technologies

• Digital images

• Geographic information systems

• Graphical user interfaces

• Multidimensions

• Tables and graphs

• Virtual reality

• Presentations

• Animation

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Geographic Information Systems (GIS)

• A computer-based system for capturing, storing, checking, integrating, manipulating, and displaying data using digitized maps

• Spatially-oriented databases

• Useful in marketing, sales, voting estimation, planned product distribution

• Available via the Web

• Can use with GPS (Global Positioning System)

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Business Intelligence on the Web

• Can capture and analyze data from Web• Tools deployed on Web

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Current dataShort database transactionsOnline update/insert/deleteNormalization is promotedHigh volume transactionsTransaction recovery is necessary

Low number of concurrent usersVarious ad hoc queries

Current and historical dataLong database transactionBatch update/insert/deleteDe-normalization is promotedLow volume transactionsTransaction recovery is not necessaryLow number of concurrent usersMore predefined queries, but are efficient in processing numerous ad hoc queries. Requires numerous indexing (approx. 50% data)

OLTP OLAP(On Line Transaction Processing On Line Analytical Processing)

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Enterprise Information Portals and DSS

Enterprise Information Portal GatewayEnterprise Information Portal User Interface

SearchAgents

SearchAgents OLAPOLAP Data

Mining

Data Mining

KnowledgeManagement

KnowledgeManagement

Database Management Functions

DataMart

OtherBusiness

Applications

OperationalDatabase

AnalyticalDatabase

KnowledgeBase

DSS

What-If ModelsSensitivity ModelsGoal-Seeking ModelsOptimization Models

Internet Intranet Extranet

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Artificial Intelligence Applications

CognitiveScience

Applications

CognitiveScience

Applications

ArtificialIntelligenceArtificial

Intelligence

RoboticsApplications

RoboticsApplications

NaturalInterface

Applications

NaturalInterface

Applications

•Expert Systems•Fuzzy Logic•Genetic Algorithms•Neural Networks

•Visual Perceptions•Locomotion•Navigation•Tactility

•Natural Language•Speech Recognition•Multisensory Interface•Virtual Reality

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Intelligent Agents

InterfaceTutors

InterfaceTutors

PresentationAgents

PresentationAgents

NetworkNavigation

Agents

NetworkNavigation

Agents

Role-PlayingAgents

Role-PlayingAgents

UserInterfaceAgents

InformationManagement

Agents

SearchAgentsSearchAgents

InformationBrokers

InformationBrokers

InformationFilters

InformationFilters

N

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Components of Expert Systems

The Expert SystemThe Expert System

KnowledgeBase

User Workstation

ExpertAdvice User

InterfacePrograms

UserInterfacePrograms

InferenceEngine

Program

InferenceEngine

Program

Expert System DevelopmentExpert System Development

Workstation

KnowledgeEngineering

KnowledgeAcquisition

Program

KnowledgeAcquisition

Program

Expert and/orKnowledge Engineer

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Expert System Applications

Decision ManagementDecision Management

Diagnostic/TroubleshootingDiagnostic/Troubleshooting

Maintenance/SchedulingMaintenance/Scheduling

Design/ConfigurationDesign/Configuration

Selection/ClassificationSelection/Classification

Major ApplicationCategoriesof Expert Systems

Process Monitoring/ControlProcess Monitoring/Control

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eBusiness Key Concepts

• eBusiness– The strategy of how to automate old business models

with the aid of technology to maximize customer value• eCommerce

– The process of buying and selling over digital media• eCRM (eCustomer Relationship Management)

– The process of building, sustaining, and improving eBusiness relationships with existing and potential customers through digital media

E-ChannelManagement

ProcurementNetwork

TradingNetwork

E-Customer Relationship

E-Commerce

E-Portal ManagementE-Services

SCM/ERP/Legacy Appls

Bu

sinesses

Bu

sinesses &

C

onsu

mers

1:NM:1 M:N

Knowledge Management/Business Intelligence

Focus on e-Business Applications

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The E-Business Application Architecture

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Data Mining Application Areas

• Marketing• Banking• Retailing and sales• Manufacturing and production• Brokerage and securities trading• Insurance• Computer hardware and software• Government and defense• Airlines• Health care• Broadcasting• Law enforcement

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Intelligent Data Mining

• Use intelligent search to discover information within data warehouses that queries and reports cannot effectively reveal

• Find patterns in the data and infer rules from them

• Use patterns and rules to guide decision making and forecasting

• Five common types of information that can be yielded by data mining: 1) association, 2) sequences, 3) classifications, 4) clusters, and 5) forecasting

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Main Tools Used in Intelligent Data Mining

• Case-based Reasoning

• Neural Computing

• Intelligent Agents

• Other Tools

– Decision trees

– Rule induction

– Data visualization

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Major Data Mining Characteristics and Objectives

• Data are often buried deep• Client/server architecture• Sophisticated new tools--including advanced

visualization tools--help to remove the information “ore”

• End-user miner empowered by data drills and other power query tools with little or no programming skills

• Often involves finding unexpected results• Tools are easily combined with spreadsheets, etc.• Parallel processing for data mining

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How does a Company to Survive and/or Prosper?

• To survive and/or prosper in the turbulent e-Age, an organization should focus on three areas:– Core competencies,– Business models, and– Execution

• Operations• People• Strategies

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Summary

• Decision support systems in business are changing. The growth of corporate intranets, extranets, and other web technologies have increased the demand for a variety of personalized, proactive, web-enabled analytical techniques to support DSS.

• Information systems must support a variety of management decision-making levels and decisions. These include the three levels of management activity: strategic, tactical, and operational.

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Summary (cont’d)

• Online analytical processing (OLAP) is used to analyze complex relationships among large amounts of data stored in multidimensional databases. Data mining analyzes large stores of historical data contained in data warehouses.

• Decision support systems are interactive computer-based information systems that use DSS software and a model base to provide information to support semi-structured and unstructured decision making.

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Summary (cont’d)

• The major application domains in artificial intelligence include a variety of applications in cognitive sciences, robotics, and natural interfaces.

• Organizations are warehousing and mining data.

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Homework

• Complete an OLAP assignment on #2 of Chapter 6 (p.211) of the text

• Copy the file CardiologyCategorical.xls• What you should turn in

– A floppy contains the file with the work done (label with your name)

– A hardcopy with answers for questions #2 (i.e., a thru e) [use of Word is required]

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Break !

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Virtual Reality

• An environment and/or technology that provides artificially generated sensory cues sufficient to engender in the user some willing suspension of disbelief

• Can share data and interact

• Can analyze data by creating a landscape

• Useful in marketing, prototyping aircraft designs

• VR over the Internet through VRML

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AI Application Areas in Business

Neural NetworksNeural Networks

Fuzzy Logic SystemsFuzzy Logic Systems

Virtual RealityVirtual Reality

Expert SystemsExpert Systems

AI ApplicationAreas inBusiness

AI ApplicationAreas inBusiness

Intelligent AgentsIntelligent Agents

Genetic AlgorithmsGenetic Algorithms

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Data Warehouse

DBMS

Data ExtractData Cleanup

Data Load

MRDB

MDDB

Data Marts

InformationDeliverySystem

Legacy & External

Data

AdminPlatform

Repository

UpdateProcess

ODS Metadata

Applications& Tools

ReportQuery

EISTools

OLAPTools

Data MiningTools

Management Platform

Tra

nsf

orm

L

oad

Data Warehouse and Operational Data Stores.

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