components of dss – data management subsystem – model management subsystem – user interface...
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Components of DSS
– Data Management Subsystem– Model Management Subsystem– User Interface (Dialog) Subsystem– Knowledge-based Management Subsys-tem – User
Components of DSS
DSS ComponentsData Management Subsystem
• DSS database • DBMS • Data directory • Query facility
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Data Management SubsystemThe DSS Database
Internal Data come mainly from the organization’s Transaction Processing System (TPS)
Private Data can include guidelines used by some decision makers assessments of specific data and/or situations
External Data includes industry datamarket research datacensus dataregional employment datagovernment regulationstax rate schedulesnational economic data
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Data Management SubsystemData Organization
• Data for DSS can be– entered directly into models– extracted directly from larger databases e.g.
Data Warehouse• Can include multimedia objects• OODBs in XML used in m-commerce
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Data Management SubsystemData Extraction (ETL)
• The process of – capturing data from several sources– synthesizing, summarizing– determining which of them are relevant – and organizing them
• resulting in their effective integration
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Data Management SubsystemDatabase Management System
• A database is created, accessed and updated by a DBMS– Software for establishing, updating, and
querying e.g. managing a database• record navigation• data relationships• report generation
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Data Management SubsystemQuery Facility
• The (database) mechanism that – accepts requests for data– accesses– manipulates– and queries data
• Includes a query language– e.g. SQL
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Data Management SubsystemData Directory
• A catalog of all the data in a database or all the models in a model base
• Contains– data definitions– data source– data meaning
• Supports addition and deletion of new entries
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Data Management SubsystemKey DB & DBMS Issues
• Data quality– “Garbage in/garbage out" (GIGO) – Managers feel they do not get the data they
need – 54% satisfied– Poor quality data leads to poor quality
information • waste• lost opportunities• unhappy customers
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Data Management SubsystemKey DB & DBMS Issues
• Data integration– For DSS to work, data must be integrated from
disparate sources– “Creating a single version of the truth”
• Scalability – Volume of data increases dramatically
• e.g. from 2001 – 2003, size of largest TPS DB increase two-fold (11 – 20 terabytes)
– Needs new storage and search technologies
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Data Management SubsystemKey DB & DBMS Issues
• Data security– data must be protected from unauthorized
access through security measures– tools to monitor database activities – audit trail
10 Key Ingredients of Data (Information) Quality Management
1. Data quality is a business problem, not only a systems problem
2. Focus on information about customers and suppliers, not just data
3. Focus on all components of data: definition, content, and presentation
4. Implement data/information quality management processes, not just software to handle them
5. Measure data accuracy as well as validity
10 Key Ingredients of Data (Information) Quality Management
6. Measure real costs (not just the percentage) of poor quality data/information
7. Emphasize process improvement/preventive maintenance, not just data cleansing
8. Improve processes (and hence data quality) at the source
9. Educate managers about the impacts of poor data quality and how to improve it
10. Actively transform the culture to one that values data quality
DSS ComponentsModel Management Subsystem
• Model base • MBMS • Modeling language • Model directory • Model execution,
integration, and command processor
Note: MBMS – model base management systems – software for establishing, updating, combining A DSS model base
DSS Components Model Management Subsystem
• The four (4) functions1. Model creation, using programming languages,
DSS tools and/or subroutines, and other building blocks
2. Generation of new routines and reports 3. Model updating and changing 4. Model data manipulation
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Model Management SubsystemCategories of Models
• Strategic Models – 5- 10 years planning– Models that represent problems for the strategic level– E.g Southwest Airlines – used its system to create accurate financial forecasts
to identify strategic opportunities. Can plan large, expensive equipment needed in future
– i.e. executive level of management• developing corporate objectives• forecasting sales target
• Tactical Models 1 month – 4 years– Models that represent problems for the tactical level – i.e. mid-level management– allocates and controls resources
• labour requirement planning• sales promotion planning
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Model Management SubsystemCategories of Models
Operational ModelModels that represent problems for the operational level of managementSupports day-to-day working activities
manufacturing targetse-commerce transaction acceptanceapproval of personal loans
Analytical Models – perform analysis of data – Mathematical models into which data are loaded for analysisStatistical modelsmanagement science modelsdata mining algorithmsFinancial models
Integrated with other models, e.g. strategic planning model
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Model Management SubsystemModel Directory
• Similar to database directory• A catalog of all models and other soft-
ware in the model base– model definitions– functions– availability and capability
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Model Management SubsystemModel Execution, Integration
• Model execution – the process of controlling the actual running
of the model• Model integration
– involves combining the operations of several models when needed
e.G use a DSS that contains six integrated models: planning and scheduling models, forecasting models
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Model Management SubsystemModel Command Processor
• A model command processor – accepts and interpret modeling instruc-tions
from the user interface component – and route them to
• the MBMS• model execution or integration functions
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Model Management SubsystemSome DSS Questions
• Which models should be used for what situations?– Cannot be done by MBMS
• What method should be used to solve a problem in a specific model class?– highly dependent on the knowledge com-
ponent
DSS ComponentsUser Interface (Dialog) Subsystem
• Interface– Application interface– User Interface
• Graphical User Interface (GUI)
• DSS User Interface– Portal– Graphical icons
• Dashboard
– Color coding
• Interfacing with PDAs, cell phones, etc.
DSS Components Knowledgebase Management System
• Incorporation of intelligence and expertise• Knowledge components:
– Expert systems, – Knowledge management systems,– Neural networks,– Intelligent agents,– Fuzzy logic, – Case-based reasoning systems, and so on
• Often used to better manage the other DSS components
DSS User• One faced with a decision that an MSS is designed
to support – Manager, decision maker, problem solver, …
• The users differ greatly from each other– Different organizational positions they occupy;
cognitive preferences/abilities; the ways of arriving at a decision (i.e., decision styles)
• User = Individual versus Group• Managers versus Staff Specialists [staff assistants,
expert tool users, business (system) analysts, facilitators (in a GSS)]
DSS ComponentsFuture/current DSS Developments
• Hardware enhancements– Smaller, faster, cheaper, …
• Software/hardware advancements– data warehousing, data mining, OLAP, Web
technologies, integration and dissemination technologies (XML, Web services, SOA, grid computing, cloud computing, …)
• Integration of AI -> smart systems
DSS Hardware
• Typically, MSS run on standard hardware• Can be composed of mainframe computers with
legacy DBMS, workstations, personal computers, or client/server systems
• Nowadays, usually implemented as a distributed/integrated, loosely-coupled Web-based systems
• Can be acquired from– A single vendor– Many vendors (best-of-breed)
End of the Chapter
• Questions / Comments…
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