Transcript Components of DSS

Components of DSS
– Data Management Subsystem
– Model Management Subsystem
– User Interface (Dialog) Subsystem
– Knowledge-based Management Subsys-tem
– User
Components of DSS
DSS Components
Data Management Subsystem
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DSS database
DBMS
Data directory
Query facility
Data Management Subsystem
The 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
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industry data
market research data
census data
regional employment data
government regulations
tax rate schedules
national economic data
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Data Management Subsystem
Data 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 Subsystem
Data 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
Subsystem
Database 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 Subsystem
Query 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 Subsystem
Data 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 Subsystem
Key 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 Subsystem
Key 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 Subsystem
Key DB & DBMS Issues
• Data security
– data must be protected from unauthorized
access through security measures
– tools to monitor database activities
– audit trail
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10 Key Ingredients of Data
(Information) Quality Management
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Data quality is a business problem, not only a
systems problem
Focus on information about customers and
suppliers, not just data
Focus on all components of data: definition,
content, and presentation
Implement data/information quality management
processes, not just software to handle them
Measure data accuracy as well as validity
10 Key Ingredients of Data
(Information) Quality Management
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Measure real costs (not just the percentage) of poor
quality data/information
Emphasize process improvement/preventive
maintenance, not just data cleansing
Improve processes (and hence data quality) at the
source
Educate managers about the impacts of poor data
quality and how to improve it
Actively transform the culture to one that values
data quality
DSS Components
Model Management Subsystem
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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) functions
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 Subsystem
Categories 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 Subsystem
Categories of Models
Operational Model
 Models that represent problems for the operational level of
management
 Supports day-to-day working activities
manufacturing targets
e-commerce transaction acceptance
approval of personal loans
Analytical Models – perform analysis of data –
Mathematical models into which data are loaded for analysis
Statistical models
management science models
data mining algorithms
Financial models
 Integrated with other models, e.g. strategic planning model
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Model Management Subsystem
Model Directory
• Similar to database directory
• A catalog of all models and other software in the model base
– model definitions
– functions
– availability and capability
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Model Management Subsystem
Model 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 Subsystem
Model Command Processor
• A model command processor
– accepts and interpret modeling instructions from the user interface component
– and route them to
• the MBMS
• model execution or integration functions
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Model Management Subsystem
Some 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 component
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DSS Components
User 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:
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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 Components
Future/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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