Transcript systems

Exam 2 Review
October 28, 2015
Info. Systems in Organizations
Decision Making
IS & Hierarchical Organizational structure
• .
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Administrative Information Systems
• Transaction Processing Systems (TPS)
– Basic business system that serves the operational level
(including analysts) in organizations
– Capture & process data generated during day-to-day activities
• Office Automation Systems (OAS)
– Systems designed to help office workers in doing their job.
• Decision Support Systems (DSS)
– Systems designed to support middle managers and business
professionals during the decision-making process
• Executive Information Systems (EIS) or Executive
Support Systems (ESS)
– Specialized DSS that help senior level executives make decisions.
• GDSS: computer-based systems that facilitate solving
of unstructured problems by set of decision makers
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Organization & IS: another view
Types of Information Systems:
Top
Management
Office
workers
Office
workers
- Transaction Processing Systems
- Office Automation Systems
- Knowledge Worker Systems
- Management Information Systems
- Decision Support Systems
- Executive Information Systems
Middle Management
Office
workers
Knowledge
workers
Questions
Office
workers
Lower Management
Operational workers
Q: What kind of IS are designed to provide help for decision makers?
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Decision Making process
Simon’s decision-making process model
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Intelligence
Design
Choice
(Implementation)
Simon, H. (1955), A Behavioral Model of Rational Choice, Quarterly Journal of Economics, vol. 69, 99–188
Newell, A., and Simon, H. A. (1972). Human problem solving Englewood Cliffs, Prentice-Hall, New Jersey.
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Intelligence Phase
• Scan the environment for
a problem.
• Determine if problem is
real, important enough,
solvable
• Determine if problem
within their scope of
influence?
• Fully define the problem
by gathering more
information.
Data source
Scan Environment for
problem to be solved
or decision to be made
Problem ?
No
Organizational
IS & external
data
END
Yes
Problem within
scope of influence?
No
END
Yes
Gather more information
about the problem
Internal &
External
data
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Design Phase
• Develop a model of
the problem.
– Determine type of
model.
• Verify model.
• Develop and
analyze potential
solutions.
Develop a model of
problem to be solved
Verify that the
model is accurate
Develop potential
solutions
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Choice Phase
• Evaluate solutions and select the solution
to implement.
– More detailed analysis of selected solution
might be needed.
– Verify initial conditions.
– Analyze proposed solution against real-world
constraints.
Questions
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DSS structure
Systems designed to help middle
managers make decisions
Major components
– Data management subsystem
• Internal and external data sources
– Analysis subsystem
User
Interface
Analysis
- Sensitivity Analysis
- What-if Analysis
- Goal-seeking Analysis
-Data-driven tools
-> Data mining
-> OLAP*
• Typically mathematical in nature
– User interface
• How the people interact with the DSS
• Data visualization is the key
– Text
– Graphs
– Charts
Data Management
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Transactional Data
Data warehouse
Business partners data
Economic data
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* OLAP: OnLine Analytical Processing
DSS Analysis Tools
Simulation is used to examine proposed solutions
and their impact
– Sensitivity analysis
– Determine how changes in one part of the model influence other
parts of the model
– What-if analysis
– Manipulate variables to see what would happen in given scenarios
– Goal-seeking analysis
– Work backward from desired outcome
Determine monthly payment given various
interest rates.
11to
Works backward from a given monthly payment
determine various loans that would give that payment.
Executive Information Systems
 Specialized DSS that supports senior level
executives within the organization
 Most EISs offer the following capabilities:
 Consolidation – involves the aggregation of
information and features simple roll-ups to complex
groupings of interrelated information
 Drill-down – enables users to get details, and
details of details, of information
 Slice-and-dice – looks at information from different
perspectives
 Digital dashboards are common features
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Artificial Intelligence (AI) systems
Common categories of AI systems:
1. Expert system – computerized advisory programs that
imitate the reasoning processes of experts in solving
difficult problems
2. Neural Network – attempts to emulate the way the human
brain works
–
–
Analyses large quantities of info to establish patterns and
characteristics in situations where logic or rules are unknown
Uses Fuzzy logic – a mathematical method of handling imprecise or
subjective information
3. Genetic algorithm – an artificial intelligent system that
mimics the evolutionary, survival-of-the-fittest process to
generate increasingly better solutions to a problem
4. Intelligent agent – special-purposed knowledge-based
information system that accomplishes specific tasks on
behalf of its users
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Expert Systems
Artificial Intelligence systems that codify human
expertise in a computer system
– Main goal is to transfer knowledge from one person to
another
– Wide range of subject areas
• Medical diagnosis
• Computer purchasing
• Finance
– Knowledge engineer elicits the expertise from the expert
and encodes it in the expert system
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Expert Systems Components
 Knowledge base: database of the expertise, often in IF THEN rules.
 Inference engine: derives recommendations from knowledge base and problem-specific
data
 User interface: controls the dialog between the user and the system
 Explanation system: Explain the how and why of recommendations
User
Domain
Expert
Expertise
Knowledge
Engineer
Encoded
expertise
Knowledge
base
Example of rules
User
Interface
Inference
Engine
Explanation
System
System
Engineer
IF
family is albatross AND
color is white
THEN
bird is laysan albatross.
IF
family is albatross AND
color is dark
THEN
bird is black footed albatross
- Knowledge engineer codify the human expert’s expertise into the systems’
knowledge base.
- System engineer is the IT professional who develop the user interface, the
inference engine, and the explanation system.
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Database & Data Warehouse
Basic Concepts of Database systems
Accounts table
AccountID
Customer
Type
Balance
660001
John Smith
Checking
$120.00
660002
Linda Martin
Saving
$9450.00
660003
Paul Graham
Checking
$3400.00
Each table has:



Fields
Records
1 Primary key
 Table
– Two-dimensional structure composed of rows and columns
 Field
– Like a column in a spreadsheet
 Field name
– Like a column name in a spreadsheet
– Examples: AccountID, Customer, Type, Balance
 Field values
– Actual data for the field
 Record
– Set of fields that describe an entity (a person, an account, etc.)
 Primary key
– A field, or group of fields, that uniquely identifies a record
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Basic Concepts in Data Management
 A Primary key could be a single field like in these tables
Primary key
AccountID
Customer
Type
Balance
660001
John Smith
Checking
$120.00
660002
Linda Martin
Saving
$9450.00
660003
Paul Graham
Checking
$3400.00
 Primary key could be a composite key, i.e. multiple fields
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Traditional File Systems
System of files that store groups of records
used by a particular software application
Simple but with a cost
– Inability to share data
– Inadequate security
– Difficulties in maintenance and expansion
– Allows data duplication (e.g. redundancy)
Application 1
Application 2
Program 1
Program 2
Program 1
Program 2
File 1
File 1
File 1
File 1
File 2
File 2
File 2
File 2
File 3
File 3
File 3
File 3
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Traditional File System Anomalies
Insertion anomaly
– Data needs to be entered more than once if
located in multiple file systems
Modification anomaly
– Redundant data in separate file systems
– Inconsistent data in your system
Deletion anomaly
– Failure to simultaneously delete all copies of
redundant data
– Deletion of critical data
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Database Advantages
Database advantages from a business
perspective include
– Ease of data insertion
• Example: can insert a new address once; and the address is
updated in all forms, reports, etc.
– Increased flexibility
• Handling changes quickly and easily
– Increased scalability and performance
• Scalability: how the DB can adapt to increased demand
– Reduced information redundancy & inconsistency
– Increased information integrity (quality)
• Can’t delete a record if related info is used in other container
– Increased information security
Desktop
Types of DBMSs
Server / Enterprise
Desktop
Handheld
– Designed to run on desktop computers
– Used by individuals or small businesses
– Requires little or no formal training
– Does not have all the capabilities of larger
DBMSs
– Examples: Microsoft Access, FileMaker
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Types of DBMSs
(Cont.)
Server / Enterprise
– Designed for managing larger and complex databases
by large organizations
– Typically operate in a client/server setup
– Either centralized or distributed
• Centralized – all data on one server
– Easy to maintain
– Prone to run slowly when many simultaneous users
– No access if the one server goes down
• Distributed – each location has part of the database
– Very complex database administration
– Usually faster than centralized
– If one server crashes, others can still continue to operate.
– Examples: Oracle Enterprise, DB2, Microsoft SQL
Server
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Types of DBMSs (Cont.)
Handheld
– Designed to run on handheld devices
– Less complex and have less capabilities than
Desktop or Server DBMSs
– Example: Oracle Database Lite, IBM’s DB2
Everywhere.
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DBMS Functions
Create database structure (tables,
relationships, schema, etc.)
Transform data into information (reports, ..)
Provide user with different logical views of
actual database content
Provide security: password authentication, access control
– DBMSs control who can add, view, change, or
delete data in the database
Physical view
ID Name Amt
01 John 23.00
02 Linda 3.00
03 Paul 53.00
Logical views
ID
02
Name
Paul
Name
Linda
Amt
53.00
ID Name Amt
01 John 23.00
02 Linda 3.00
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DBMS Functions (cont.)
Allowing multi-user access with control
– Control concurrency of access to data
– Prevent one user from accessing data that
has not been completely updated
• When selling tickets online, Ticketmaster allows
you to hold a ticket for only 2 minutes to make your
purchase decision, then the ticket is released to
sell to someone else – that is concurrency control
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Database Models
Database model = a representation of the
relationship between structures (e.g. tables) in a
database
Common database models
– Flat file model
– Relational model (the most common, today)
– Object-oriented database model
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Flat File Database model
Stores data in basic table structures
 No relationship between tables
 Used on PDAs for address book

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Relational Database Model
 Multiple two-dimensional tables related by common fields
 Uses controlled redundancy to create fields that provide
linkage relationships between tables in the database
– These fields are called foreign keys – the secret to a
relational database
– A foreign key is a field, or group of fields, in one table
that is the primary key of another table
 Handles One-to-Many and One-to-One
relationships
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Object-Oriented Database model
Needed for multimedia applications that
manage images, voice, videos, graphics,
etc.
Used in conjunction with Object-oriented
programming languages
Slower compared to relational DBMS for
processing large volume of transactions
Hybrid object-relational Databases are
emerging
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Data Warehouse
A logical collection of information gathered
from many different operational databases
Supports business analysis activities and
decision-making tasks
The primary purpose of a data warehouse
is to aggregate information throughout an
organization into a single repository for
decision-making purposes
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Data Warehouse Fundamentals
 Many organizations need internal, external, current, and
historical data
 Data Warehouse are designed to, typically, store and
manage data from operational transaction systems,
Web site transactions, external sources, etc.
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Multidimensional Analysis
 Data mining – the process of analyzing data to extract
information not offered by the raw data alone
 Data-mining tools use a variety of techniques (fuzzylogic, neural networks, intelligent agents) in order to
 find patterns and relationships in large volumes of data
 and infer rules that predict future behavior and guide decision
making
 Other analytical tools: query tools, statistical tools, etc.
used to
 Analyze data, determine relationships, and test hypotheses
about the data
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Data Warehouse Fundamentals
 Extraction, transformation, and loading (ETL) – a process that extracts
information from internal and external databases, transforms the information
using a common set of enterprise definitions, and loads the information into
a data warehouse.
Information Cleansing or Scrubbing
Organizations must maintain high-quality
data in the data warehouse
Information cleansing or scrubbing
– a process that weeds out and fixes or
discards inconsistent, incorrect, or incomplete
information
– first, occurs during ETL. Then, when the data
is in the Data Warehouse using Information
cleansing or scrubbing tools.
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Data Mart
Subset of data warehouses that is highly
focused and isolated for a specific population of
users
Example: Marketing data mart, Sales data mart,
etc.
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Database vs. Data Warehouse
Databases contain information in a series
of two-dimensional tables
In a Data Warehouse and data mart,
information is multidimensional, it contains
layers of columns and rows
Total annual sales
of TV in U.S.A.
Date
2Qtr 3Qtr
4Qtr
sum
U.S.A
Canada
Mexico
sum
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Country
TV
PC
VCR
sum
1Qtr
Managing IS Development
projects
IS Development
What is a project?
• Individual or collaborative
enterprise that is carefully
planned, designed and
executed to achieve a
particular goal
–This definition highlights some,
but not all, key elements of a
project
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Characteristics of a project
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IS Development project
• A project which main goal is
to plan, design,
develop/acquire, and
implement an information
system
• IS Development is a process
that needs to be well
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IS project managers’ skills
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Traditional Systems
Development Life Cycle (SDLC)
•
Planning
Seven phases
1)
2)
3)
4)
Planning
Systems Analysis
Systems Design
Development
5) Testing
6) Implementation
7) Maintenance
Analysis
Design
Development
Testing
•
•
Typically one phase needs to be
completed before beginning the
next
Implementation
Maintenance
Problem in later phase may require
return to previous phase
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Planning
Feasibility Analyses
System Development Schedule
• Feasibility analyses
– Technical Analysis
• Do the technologies exist to develop the system?
– Economic Analysis
• Can the organization afford the system?
• Will it provide an adequate Benefit?
– Operational Analysis (i.e. assessing the human factors that could
make the project fail)
• Resistance to change
• Organizational politics
• System development schedule
– Is the proposed development time line realistic?
– Is the programming team available during Programming step?
• Planning performed by Project Manager using
– Search and investigation (e.g. for technical analysis step)
– Total Cost of Ownership analysis
– Project Management software
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Systems Analysis
Analyze current system
Define new system requirements
• Systems analyst works with company to fully
understand the problem, and to detail the
requirements of the proposed system
• Step 1: Analyze current system
– Objectives:
• Understand what things are done and how (business processes)
• Identify any problems associated w/ current business processes
– Techniques used:
• Talking to employees (potential users)
• Conducting interviews
• Observing employees at work
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Systems Analysis
Analyze current system
Define new system requirements
• Step 2: Define new system’s requirements
– Main Objective:
• Specify What need to be done (not how to do it)
– To be defined:
•
•
•
•
Input requirements (nature of data, source, etc.)
Processing requirements
Output requirements (Types of reports, content, etc.)
Storage requirements
– Tools and techniques used:
• Data flow diagrams (DFD)
– Start with high level process
– Add more levels with increased levels of detail
• Computer-Aided Software Engineering (CASE) tools
– Software that eases the systems development process
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Data Flow Diagrams (DFD)
Process
. Symbolized by a rectangle or a curved rectangle.
. Action performed by people of organizational
units in order to transform input into output OR
Action performed by people in the organizational
units to help the units achieve their objectives
Data flow
. Symbolized by an arrow.
. Shows data being passed from or to a process
External Entity
Symbolized by a square, an external entity is
something (person, group, department, etc.)
outside the system that interacts with the system
by providing input or receiving information.
Data storage
Used to store data in the system.
Represents a file, a database, etc.
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Systems Design
Logical system design
Physical system design
• Using the requirements from the Systems Analysis phase to
design the new or modified system.
– Logical systems design
• Details the system’s functionality (what it should do?)
• Uses Structure charts to create top-down representation of system’s
modules
• Uses System flowcharts to show relationships between modules
– Physical systems design
• Specifies all of the actual components (hardware, network, databases)
used to implement the logical design
– The design must be frozen at end of this phase to prevent the system
from growing indefinitely in terms of its scope and features
• Scope creep (continuous growth in a project's scope)
• Features creep continuous growth in a project's features)
• Performed by system designer or (system analyst in some
case)
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Implementation
• Implementation strategies
– Direct cutover: Quick change to new system
– Parallel conversion: Old and new systems
used in parallel for a while.
– Pilot testing: New system installed at only one
location or one department
– Staged conversion: Only one part of the
system installed, then another part is
installed.
• User training
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SDLC: Recap
Steps
Key actors
Tools/Techniques
1. Planning
Project Manager
TCO, Project Management software
2. System Analysis
System Analyst, Users.
Interviews, observing users at work,
DFD
3. System Design
System analyst (or system designer)
System Flowchart, Structure chart
4. Development
Programmers, database developers,
network engineers
Program Flowchart, Pseudo code,
programming languages
5. Testing
Development team, Users
Modules’ testing, units’ testing,
System’s testing (verification,
validation)
6. Implementation
Development team, Users
Direct cutover, parallel conversion,
pilot testing, staged conversion
7. Maintenance
internal IS staff, external consultant
Problems with Traditional SDLC
• SDLC is time consuming
• SDLC is not flexible (sequential process)
• SDLC gets users’ inputs ONLY during
Systems analysis.
• Design is frozen at end of System Design
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Prototyping
• A SDM that addresses:
– Time consuming issue associated with SDLC
– SDLC’s inability to take care of new requirements
• A SDM in which the Development team uses
limited set of users requirements to quickly
build a working model of the proposed system
– a prototype.
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Prototyping
Actors
Identify basic
requirements
Operational
prototype
YES
Development team, Users
Develop a
prototype
System analyst, programmer
Use the
prototype
Users
Is User
satisfied?
NO
Develop final system
(improved prototype)
Revise the
prototype
System analyst, programmer
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Joint Application Development (JAD)
• A SDM that addresses:
– The limited scale of users involvement problem of Prototyping
– Potential implementation problem due to limited users involvement
• A SDM that brings together the Development
team and a significant number of users to
define system requirements and develop a
prototype.
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Joint Application Development (JAD)
Identify a valid
sample of users
Objectives
Set a JAD team
(Users, IS professionals, scribe)
Run the 1st JAD session
(JAD team + Facilitator)
Identify agreed upon systems requirements
Develop system prototype
(based on agreed requirements)
Run the 2nd JAD session
(JAD team + Facilitator)
Test the system and identify agreed changes
Improve system prototype
(based on JAD session results)
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