1. Decision Support and Business Intelligence Systems (9th Ed
Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall)
Chapter 3:
Decision Support Systems Concepts, Methodologies, and Technologies: An Overview

2. Learning Objectives
Understand possible decision support system (DSS) configurations
Understand the key differences and similarities between DSS and BI systems
Describe DSS characteristics and capabilities
Understand the essential definition of DSS
Understand important DSS classifications
Understand DSS components and how they integrate

3. Learning Objectives
Describe the components and structure of each DSS component
Explain Internet impacts on DSS (and vice versa)
Explain the unique role of the user in DSS versus management information systems
Describe DSS hardware and software platforms
Become familiar with a DSS development language
Understand current DSS issues

4. Opening Vignette: “Decision Support System Cures for Health Care”
Company background
Problem
Proposed solution
Results
Answer and discuss the case questions

5. DSS Configurations Many configurations exist; based on
management-decision situation
specific technologies used for support
DSS have three basic components
Data
Model
User interface
(+ optional) Knowledge

6. DSS Configurations Each component has several variations
Managed by a commercial of custom software
Typical types:
Model-oriented DSS
Data-oriented DSS

7. DSS Description An early definition of DSS
A system intended to support managerial decision makers in semistructured and unstructured decision situations
meant to be adjuncts to decision makers (extending their capabilities but not replacing their judgment)
aimed at decisions that required judgment or at decisions that could not be completely supported by algorithms
would be computer based; operate interactively; and would have graphical output capabilities…

8. Overall Capabilities of DSS
A DSS is typically built to support the solution of a certain problem (or to evaluate a specific opportunity).
DSS is an approach (or methodology) for supporting decision making
uses an interactive, flexible, adaptable computer-based information system (CBIS)
developed (by end user) for supporting the solution to a specific nonstructured management problem
uses data, model and knowledge along with a friendly (often graphical; Web-based) user interface
incorporate the decision maker's own insights
supports all phases of decision making
can be used by a single user or by many people

9. Overall Capabilities of DSS
Easy access to data/models/knowledge
Proper management of organizational experiences and knowledge
Easy to use, adaptive and flexible GUI
Timely, correct, concise, consistent support for decision making
Support for all who needs it, where and when he/she needs it
- See Table 3.2 for a complete list...

10. A Web-Based DSS Architecture

11. DSS Characteristics and Capabilities
DSS is not quite synonymous with BI
DSS are generally built to solve a specific problem and include their own database(s)
BI applications focus on reporting and identifying problems by scanning data stored in data warehouses
Both systems generally include analytical tools (BI called business analytics systems)
Although some may run locally as a spreadsheet, both DSS and BI uses Web

12. DSS Characteristics and Capabilities

13. DSS Characteristics and Capabilities
Business analytics
Analytics is the science of analysis, Its refers to analysis data.
Business analytics (BA) is abroad category of applications and techniques for gathering ,storing , analyzing , and providing access to data to help enterprise users make better business and strategic decisions. See Ch6
Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture
Example:
An analytic application used for credit scoring for a loan application might:
Automatically accept or deny the loan application

14. DSS Characteristics and Capabilities
Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce
Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur

15. Components of DSS

16. Components of DSS Data Management Subsystem Model Management Subsystem
User Interface Subsystem
Knowledgebase Management Subsystem

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

18. Database Management Subsystem
The Database
Is a collection of interrelated data, organized to meet the needs and structure of an organization.
The data are extracted from internal and external data sources.
Data Organization
A standalone database?
Data Extraction (ETL)
Importing of files, summarizing, standardization, filtration, and condensation of data.
Managed by DBMS
The Database Management System
A database is created , accessed and updated by a DBMS (technology insights 3.3)
Most DSS built with a standard commercial relational DBMS
Major vendors are (Oracle, IBM, Microsoft)
The Query Facility

19. Database Management Subsystem Key Data Issues
The Directory
Is a catalog of all the data in a database
It contains data definitions
Its main functions is to answer questions about the availability of data items, their source and their exact meaning.
Key Data Issues
Data quality
A key issue in data management
Poor quality data, leads to poor quality information leads to waste.
“Garbage in/garbage out" (GIGO)
E.g., given inaccurate data in a CRM system, customers may be contacted many times.
Data quality clean up (e.g., validation and verification) tools for data cleansing

20. Database Management Subsystem Key Data Issues
Data integration
“Creating a single version of the truth”
Scalability
Is ability of a system , network, or process to handle a growing a mount of work in a capable manner
E.g., increased size of database
Data Security
Data must be protected from unauthorized access through security measures such as ID and password
Data can be encrypted, in the case of unauthorized access
New tools developed to monitor database activity and provide audit trail
E.g., SQL Guard from Guardium,Inc.

21. 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

22. 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

23. DSS Components Model Management Subsystem
Model base
MBMS
Modeling language
Model directory
Model execution, integration, and command processor

24. DSS Components Model Management Subsystem
The Model base
Contains routine and special statistical, financial, forecasting ,management science and other quantitative models that provide the analysis capabilities in a DSS
Model Types
Strategic
Used to support top managers’ strategic planning responsibilities
Potential applications include an e-commerce venture, developing corporate objectives, selecting a plant location, creating a noun routine capital budget, ..
Tactical
Used mainly by middle managers to assist in allocating and controlling the organization’s resources
E.g., sales promotion planning, web server selection routine capital budgeting.

25. DSS Components Model Management Subsystem
Operational
Used to support day to day working activates of the organization
E.g., e-commerce transaction acceptance (e.g., purchases), approval of personal loans, production scheduling, enventory control, maintenance planning
Analytic models
Used to perform analysis on data
They include statistical models, management science models, data mining algorithms, financial models and more
They can integrated with others
They can be classified by functional areas or by discipline
Model building blocks and Routines
The model base can contain model building blocks and routines such as the functions and add- ins in excel.

26. DSS Components Model Management Subsystem
Modeling Tools
For semistructured and unstructured problems : Programming tools and languages used to customize models such as .Net Framwork, C++,Java
For samll and meduim sized DSS or less complex ones: Excel usually used.
The MBMS has four main 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
Model directory
Model execution, integration and command

27. DSS Components Model Management Subsystem
Model directory
Its role is similar to that of database
It’s a catalog of all the models and other software in the model base
Contains definitions and its main function to answer questions about the availability and capabilities of the models.
Model execution, integration and command
Model execution is the process of controlling the actual running of the model
Model integration involves combining the operations of several models when needed or integrating the DSS with other applications
A model command processor is used to accept and interpret modeling instructions from the user interface component and route them to the model execution or model integration functions

28. 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.

29. DSS Components User Interface (Dialog) Subsystem
The term User Interface covers all aspects of communication between a user and the DSS or any MSS
It includes H\W and S\W and all factors that deal with ease to use , accessibility, and human machine interaction.
User Interface Management System (UIMS)
Software(Composed of several programs) that managed the user interface subsystem
The user Interface Process
See figure 3.7
DSS User Interfaces
Access provided through Web browsers including ( Voice input, portable devices)
Web browsers provides a portal or dashboard to access the system.

30. DSS Components Knowledgebase Management System
Incorporation of intelligence and expertise
Because many semistructured and unstructured problems are so complex
Knowledge components:
Expert systems,
Knowledge management systems,
Neural networks,
Intelligent agents,
Fuzzy logic,
Case-based reasoning systems, and so on

31. DSS User
One faced with a decision that an MSS is designed to support is called :
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 Specialists such as Staff assistants, Business analysts, Expert tool users, Facilitators (in a GSS )use computers more than managers
They tend to be more detail oriented, more willing to use complex system in their work
They are intermediaries between managers and MSS

32. Intermediary: allows a manager to benefit from a DSS without actually having to use the keyboard
Staff assistants: have a specialized knowledge about management problems and some experience with decision support technology
Expert tool users: are skilled in the application of one or more types of specialized problem solver tools
Business (system) analysts: have a general knowledge of the application area and considerable skill in using DSS tools
Facilitators (GSS) :support the work of people working in groups

33. DSS Components Future/current DSS Developments
DSS have evolved with advances in Computer H\W and S\W
Hardware enhancements
Smaller, faster, cheaper, …
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

34. DSS Classifications
The Association for information System Special Interest Group on Decision Support Systems (AIS SIGDSS) Classification for DSS:
Communications-driven and group DSS (GSS)
Includes how computer, collaboration, and communication technology support groups in tasks
Meetings, design collaboration
E.g., SCM
Data-Driven DSS
Rely on data and their processing into information
E.g., OLAP
Strong |Report and Query capabilities
Document-Driven DSS
Rely on knowledge coding, analysis, search and retrieval for decision support
Include text based

35. DSS Classifications Model-Driven DSS Compound DSS
Knowledge-Driven DSS, Data mining, and Management Expert Systems Applications
Involve the application of knowledge technologies to address specific decision support needs
All Artificial Intelligence-based DSS fall into this category
Model-Driven DSS
The major emphases of DSS that are primarily developed around one or more optimization or simulation model
E.g., Excel includes dozens of statistical packages, Solver, and many financial and management models.
Compound DSS
Or Hybrid DSS includes two or more of the major categories of DSS
Custom-Made Systems Versus Ready-Made Systems

36. A DSS Modeling Language Planners Lab (plannerslab.com)
Generating Assumptions

37. A DSS Modeling Language Planners Lab (plannerslab.com)
Creating a new model

38. A DSS Modeling Language Planners Lab (plannerslab.com)

39. A DSS Modeling Language Planners Lab (plannerslab.com)

40. A DSS Modeling Language Planners Lab (plannerslab.com)

41. A DSS Modeling Language Planners Lab (plannerslab.com)

42. A DSS Modeling Language Planners Lab (plannerslab.com)

43. A DSS Modeling Language Planners Lab (plannerslab.com)

44. A DSS Modeling Language Planners Lab (plannerslab.com)

45. A DSS Modeling Language Planners Lab (plannerslab.com)

46. A DSS Modeling Language Planners Lab (plannerslab.com)

47. A DSS Modeling Language Planners Lab (plannerslab.com)

48. A DSS Modeling Language Planners Lab (plannerslab.com)

49. A DSS Modeling Language Planners Lab (plannerslab.com)

50. End of the Chapter
Questions / Comments…

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