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Topic:

Different Components of DSSDifference between Data-driven & model driven DSS

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DSS Components

1. Data Management Subsystem2. Model Management Subsystem3. Knowledge-based (Management) Subsystem4. User Interface Subsystem5. The User

1.Data Management Subsystem

DSS databaseDatabase management systemData directoryQuery facility

User

User Interface

DBMS MBMS

KBS3KBS2

KBS1

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DSS Database Issues

Data warehouseData miningSpecial independent DSS databasesExtraction of data from internal, external, and private sourcesWeb browser data accessWeb database serversMultimedia databasesSpecial GSS databases (like Lotus Notes / Domino Server)Online Analytical Processing (OLAP)Object-oriented databasesCommercial database management systems (DBMS)

2. Model Management Subsystem

Analog of the database management subsystem

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Model baseModel base management systemModeling languageModel directoryModel execution, integration, and command processor

Model Management System

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Strategic Models: Non routinemergers, impact analysis, capital budgetingTactical Models: Allocation & Controllabor requirements, sales promotion planningOperational Models: Routine-day-to-dayproduction scheduling, inventory control, quality controlAnalytical Models: SAS, SPSS, OR, data mining

3. Knowledge-based (Management) Subsystem

Provides expertise in solving complex unstructured and semi-structuredproblemsExpertise provided by an expert system or other intelligent systemAdvanced DSS have a knowledge based ( management) componentLeads to intelligent DSSExample: Data mining

4. User Interface Subsystem

Includes all communication between a user and the MSSGraphical user interfaces (GUI)Voice recognition and speech synthesis possible

To most users, the user interface is the system

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5. The User

Different usage patterns for the user , the manager , or the decisionmaker

ManagersStaff specialists

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Intermediaries

Staff assistant Expert tool user Business (system) analyst GSS Facilitator

Difference between Data-driven & modeldriven DSS

Data-driven DSS:

Data driven DSS are targeted at managers, staff and also product/servicesuppliers. It is used to query a database or data warehouse to seek specificanswers for specific purposes. It is deployed via a main frame system,client/server link, or via the web. Examples: computer-based databases thathave a query system to check (including the incorporation of data to add valueto existing databases.

How many washing-up liquids were sold in total in December 2003?

Compare the turnover of our washing-up liquid in St. Gallen to theturnover of this product in Geneva for the last four months.

Which washing-up liquid is favoured by business women over fifty whobuy the magazine Gala regularly

Such questions can be answered by a data-driven DSS. Data-driven DSS helpprepare decisions by showing developments in the past and by identifyingrelations or patterns.

This also explains the two sub-types of data-driven DSS :

The first sub-type OLAP provides the means for interactive ad-hocexploration of huge amounts of data to gather the information necessaryfor the decision.

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The second one, Data-mining , provides the means for automatically findstructure in the form of relations and/or patterns in large databases.

These can then be used as models of the data to understand the datasstructure (e.g., what item is usually bought together with spaghettis),make predictions (what is the probability that a customer who buysspaghetti also buys red wine), find a most likely explanation (given theshopping of this person she is likely to have a dinner party tonight).

The aim of data-driven DSS is to give the user a simple access to largeamounts of data from various sources (both internal and external) on whichhe or she can run real-time queries . A data-driven DSS helps to transformlarge data sets into meaningful, easy to read business information.

Even if a data-driven DSS has models, they are in general very simple and onlyused to compute information like averages or totals.

Nowadays, such large amounts of data are stored in data warehouses which areintroduced on the following page.

Model-driven DSS:

Model driven DSS is a decision support system used by managers, staff members and people who interact with the business organization for analyzingsemi structured and unstructured decisions. They are usually deployed assoftware; hardware in standalone PCs.DSS basically has 4 analysis models:

what if analysissensitivity analysis

goal based analysisoptimized analysis

Model driven DSS

Analytical capabilities; Can answer what-if scenariosCan be used for deciding which path to take (Goal seek)Can be used to determine what inputs will get you the desiredoutput (Solving)

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