Transcript IS605/606: Information Systems Instructor: Dr. Boris Jukic

IS500: Information Systems
Instructor: Dr. Boris Jukic
Decision Support Systems
Systems and Technologies that Support
Organizational Decision Making

Decision-enabling, problem-solving, and opportunity-seizing
systems
Why are Decision Support Systems
back in Vogue?

The amount of information people must
understand to make decisions, solve problems,
and find opportunities is growing exponentially
Executive information Systems
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Executive information system (EIS)
– a specialized DSS that supports senior
level executives within the organization
Most EISs offering the following
capabilities:

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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
EXECUTIVE INFORMATION
SYSTEMS

Digital dashboard – integrates information
from multiple components and present it in a
unified display
Artificial intelligence (AI)

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Intelligent systems – various commercial
applications of artificial intelligence
Artificial intelligence (AI) – simulates human
intelligence such as the ability to reason and
learn and typically can:

Learn or understand from experience

Make sense of ambiguous or contradictory
information

Use reasoning to solve problems and make
decisions
AI Fell out of favor in the early 90’s

Back in Fashion?
Artificial intelligence (AI)

The three most common categories of AI
include:
1.
Expert systems – computerized advisory
programs that imitate the reasoning
processes of experts in solving difficult
problems
2.
Neural Networks – attempts to emulate
the way the human brain works
3.
Intelligent agents – special-purposed
knowledge-based information system that
accomplishes specific tasks on behalf of its
users
Common example: shopping bot
Data Mining

Common forms of data-mining
analysis capabilities include

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Cluster analysis
Association detection
Statistical analysis
Cluster Analysis

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Cluster analysis – a technique used to
divide an information set into mutually
exclusive groups such that the members
of each group are as close together as
possible to one another and the
different groups are as far apart as
possible
CRM systems depend on cluster analysis
to segment customer information and
identify behavioral traits
Association Detection
Association detection – reveals the
degree to which variables are related
and the nature and frequency of these
relationships in the information


Market basket analysis – analyzes such
items as Web sites and checkout scanner
information to detect customers’ buying
behavior and predict future behavior by
identifying affinities among customers’
choices of products and services

Beer-Diapers example
Statistical Analysis

Statistical analysis – performs such
functions as information correlations,
distributions, calculations, and variance
analysis

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Forecasts – predictions made on the basis
of time-series information
Time-series information – time-stamped
information collected at a particular
frequency
Data Warehouse: Definition

Data Warehouse: An enterprise-wide
structured repository of subject-oriented,
time-variant, historical data used for
information retrieval and decision support.
The data warehouse stores atomic and
summary data.
(Bill Inmon, paraphrased by Oracle Data
Warehouse Method)
Need for Data Warehousing


Integrated, company-wide view of
high-quality information.
Separation of operational and
analytical systems and data.
OPERATIONAL vs. ANALYTICAL DATA
Operational Data
Analytical Data
Data Differences
Typical Time-Horizon: Days/Months
Typical Time-Horizon: Years
Detailed
Summarized (and/or Detailed)
Current
Values over time (Snapshots)
Technical Differences
Can be Updated
Read (and Append) Only
Control of Update: Major Issue
Control of Update: No Issue
Small Amounts used in a Process
Large Amounts used in a Process
Non-Redundant
Redundancy not an Issue
High frequency of Access
Low/Modest frequency of Access
Purpose Differences
For “Clerical Community”
For “Managerial Community”
Supports Day-to-Day Operations
Supports Managerial Needs
Application Oriented
Subject Oriented
OPERATIONAL vs. ANALYTICAL DATA
Hardware Utilization
(Frequency of Access)
Operational
120
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100
100
80
80
60
60
40
40
20
20
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Data Warehouse
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