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Information Systems

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Information System (CS507)
LESSON 10
Support Systems
Seeing the benefits of MIS for middle level managers, Computerised systems have been devised for other
employees in the organization to help them complete their work efficiently and effectively.
10.1 Support systems can be classified into two categories
 Office automation systems
 Decision support systems
10.1.1 Office Automation Systems
Office automation system includes formal and informal electronic systems primarily concerned with the
communication of information to and from persons both inside and outside the firm. It supports data
workers in an organization.
For Instance
 Word processing
 Desktop publishing
 Imaging & Web publishing
 Electronic calendars ­ manager's appt. calendars
 Email
 Audio & video conferencing ­ establishing communication between geographically dispersed
persons.
10.1.2 Decision Support Systems
Before moving forward with the concept of decision support system, we would take a look at the definition
of MIS
"An integrated man-machine system for providing information to support the operations, management and
decision making functions in an organization."
(Prof. Gordon Davis University of Minnesota)
Four Criteria for designing models and systems to support management decisions making were laid down
by J.D.C. Little. These were
Robustness
Ease of Control
Simplicity
Completeness of relevant detail
Decision Support Systems was defined by Bill Inmon, father of data warehouse, as
"a system used to support managerial decisions. Usually DSS involves the analysis of many units of data
in a heuristic fashion. As a rule, DSS processing does not involve the update of data"
Heuristic simply means a particular technique of directing one's attention in learning, discovery or problem
solving. It assists in non-routine decision making process due to powerful analytical abilities.
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For Instance
For any scenario all the related factors with their ranges of variability are entered into DSS, which helps
guide managers for any new scenario that emerges. DSS can stimulate innovation in decision making by
helping managers to existing decision making procedures.
An example of Decision Support System
An outfit store maintains ready made garments and stitched clothes for various classes of society. Due to
fluctuating changes in fashion trends, pre-seasonal planning becomes critical.
A Planning and forecasting software can be used by management to
Measure customer reactions to re-pricing
When to initiate clearance sales for old stock
Deciding about discount percentages
When to order new stock for the season
10.2 Functionalities of MIS and DSS
Sr. No. MIS
DSS
1
Provides information on
Helps in non routine decision making.
monitoring and controlling the
business.
2
Fixed and regular reports are
Users are not linked with the structured
generated from data kept in
information flows.
TPS.
3
Report formats are predefined. Greater emphasis on models, display
graphics & ad hoc queries.
4
User is part of the system
DSS is a small part of users' actions.
5
Controlled by IT Dept.
Directly used by middle level managers
Table 10.1
10.3 Types of DSS
DSS, may either be
Model Driven DSS
Data Driven DSS
10.3.1 Model Driven DSS
Model driven DSS uses following techniques
 What-If analysis
Attempt to check the impact of a change in the assumptions (input data)
on
the
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proposed solution
e.g. What will happen to the market share if the advertising budget increases by 5 % or
10%?
Goal Seek Analysis
Attempt to find the value of the inputs necessary to achieve a desired level of output. It
uses "backward" solution approach
e.g. a DSS solution yielded a profit of $2M. What will be the necessary sales volume to
generate a profit of $2.2M?
These are primarily stand alone systems isolated from major organizational information systems (finance,
manufacturing, HR, etc). They are developed by end users and are not reliant on central information
systems control. These systems combine
Use of a strong model, and
Good user interface to maximise model utility
They are not usually data intensive, hat is very large data bases are usually not need for model-driven DSS.
They use data and parameters usually provided by decision makers to aid in analyzing a situation.
10.3.2 Data Driven DSS
As opposed to model driven DSS, these systems use large pools of data found in major organizational
systems. They help to extract information from the large quantities of data stored. These systems rely on
Data Warehouses created from Transaction Processing systems.
They use following techniques for data analysis
Online analytical processing, and
Data mining
Components of DSS
There are two major components
DSS data base ­ is a collection of current and historical data from internal external sources. It can be a
massive data warehouse.
Decision Support Software system ­ is the set of software tools used for data analysis. For instance
Online analytical processing (OLAP) tools
Data mining tools
Models
Data Warehouse
A data warehouse is a logical collection of information.
It is gathered from many different operational databases used to create business intelligence that
supports business analysis activities and decision-making tasks.
It is primarily, a record of an enterprise's past transactional and operational information, stored in a
database designed to favour efficient data analysis and reporting.
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The term data warehouse generally refers to the combination of many different databases across an
entire enterprise.
Data warehouses contain a wide variety of data that present a coherent picture of business conditions at
a single point in time.
Data warehouses are generally batch updated at the end of the day, week or some period. Its contents
are typically historical and static and may also contain numerous summaries.
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Table of Contents:
  1. Need for information, Sources of Information: Primary, Secondary, Tertiary Sources
  2. Data vs. Information, Information Quality Checklist
  3. Size of the Organization and Information Requirements
  4. Hierarchical organization, Organizational Structure, Culture of the Organization
  5. Elements of Environment: Legal, Economic, Social, Technological, Corporate social responsibility, Ethics
  6. Manual Vs Computerised Information Systems, Emerging Digital Firms
  7. Open-Loop System, Closed Loop System, Open Systems, Closed Systems, Level of Planning
  8. Components of a system, Types of Systems, Attributes of an IS/CBIS
  9. Infrastructure: Transaction Processing System, Management Information System
  10. Support Systems: Office Automation Systems, Decision Support Systems, Types of DSS
  11. Data Mart: Online Analytical Processing (OLAP), Types of Models Used in DSS
  12. Organizational Information Systems, Marketing Information Systems, Key CRM Tasks
  13. Manufacturing Information System, Inventory Sub System, Production Sub System, Quality Sub system
  14. Accounting & Financial Information Systems, Human Resource Information Systems
  15. Decision Making: Types of Problems, Type of Decisions
  16. Phases of decision-making: Intelligence Phase, Design Phase, Choice Phase, Implementation Phase
  17. Planning for System Development: Models Used for and Types of System Development Life-Cycle
  18. Project lifecycle vs. SDLC, Costs of Proposed System, Classic lifecycle Model
  19. Entity Relationship Diagram (ERD), Design of the information flow, data base, User Interface
  20. Incremental Model: Evaluation, Incremental vs. Iterative
  21. Spiral Model: Determine Objectives, Alternatives and Constraints, Prototyping
  22. System Analysis: Systems Analyst, System Design, Designing user interface
  23. System Analysis & Design Methods, Structured Analysis and Design, Flow Chart
  24. Symbols used for flow charts: Good Practices, Data Flow Diagram
  25. Rules for DFDs: Entity Relationship Diagram
  26. Symbols: Object-Orientation, Object Oriented Analysis
  27. Object Oriented Analysis and Design: Object, Classes, Inheritance, Encapsulation, Polymorphism
  28. Critical Success Factors (CSF): CSF vs. Key Performance Indicator, Centralized vs. Distributed Processing
  29. Security of Information System: Security Issues, Objective, Scope, Policy, Program
  30. Threat Identification: Types of Threats, Control Analysis, Impact analysis, Occurrence of threat
  31. Control Adjustment: cost effective Security, Roles & Responsibility, Report Preparation
  32. Physical vs. Logical access, Viruses, Sources of Transmissions, Technical controls
  33. Antivirus software: Scanners, Active monitors, Behavior blockers, Logical intrusion, Best Password practices, Firewall
  34. Types of Controls: Access Controls, Cryptography, Biometrics
  35. Audit trails and logs: Audit trails and types of errors, IS audit, Parameters of IS audit
  36. Risk Management: Phases, focal Point, System Characterization, Vulnerability Assessment
  37. Control Analysis: Likelihood Determination, Impact Analysis, Risk Determination, Results Documentation
  38. Risk Management: Business Continuity Planning, Components, Phases of BCP, Business Impact Analysis (BIA)
  39. Web Security: Passive attacks, Active Attacks, Methods to avoid internet attacks
  40. Internet Security Controls, Firewall Security SystemsIntrusion Detection Systems, Components of IDS, Digital Certificates
  41. Commerce vs. E-Business, Business to Consumer (B2C), Electronic Data Interchange (EDI), E-Government
  42. Supply Chain Management: Integrating systems, Methods, Using SCM Software
  43. Using ERP Software, Evolution of ERP, Business Objectives and IT
  44. ERP & E-commerce, ERP & CRM, ERP Ownership and sponsor ship
  45. Ethics in IS: Threats to Privacy, Electronic Surveillance, Data Profiling, TRIPS, Workplace Monitoring