1. Components of DSS Data Management Subsystem Model Management Subsystem
User Interface (Dialog) Subsystem
Knowledge-based Management Subsys-tem
User

2. Components of DSS

3. DSS Components Data Management Subsystem
DSS database
DBMS
Data directory
Query facility

4. 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
industry data
market research data
census data
regional employment data
government regulations
tax rate schedules
national economic data

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 10 Key Ingredients of Data (Information) Quality Management
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

14. 10 Key Ingredients of Data (Information) Quality Management
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

15. DSS Components Model 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

16. DSS Components Model Management Subsystem
The four (4) functions
Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks
Generation of new routines and reports
Model updating and changing
Model data manipulation

17. Model Management Subsystem Categories of Models
Strategic Models – 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

18. 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

19. Model Management Subsystem Model Directory
Similar to database directory
A catalog of all models and other soft-ware in the model base
model definitions
functions
availability and capability

20. Model Management Subsystem Model Execution, Integration
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

21. Model Management Subsystem Model 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

22. 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 com-ponent

23. DSS Components User Interface (Dialog) Subsystem
Application interface
User Interface
Graphical User Interface (GUI)
DSS User Interface
Portal
Graphical icons
Dashboard
Color coding
Interfacing with PDAs, cell phones, etc.

24. 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

25. 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)]

26. 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

27. 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)

28. End of the Chapter
Questions / Comments…

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