IS500: Information Systems Instructor: Dr. Boris Jukic Decision Support Systems.
Published byModified over 5 years ago
Presentation on theme: "IS500: Information Systems Instructor: Dr. Boris Jukic Decision Support Systems."— Presentation transcript:
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 Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization Most EISs offering 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
EXECUTIVE INFORMATION SYSTEMS Digital dashboard – integrates information from multiple components and present it in a unified display
Artificial intelligence (AI) 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 Cluster analysis Association detection Statistical analysis
Cluster Analysis 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 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 DataAnalytical Data Data Differences Typical Time-Horizon: Days/MonthsTypical Time-Horizon: Years DetailedSummarized (and/or Detailed) CurrentValues over time (Snapshots) Technical Differences Can be UpdatedRead (and Append) Only Control of Update: Major IssueControl of Update: No Issue Small Amounts used in a ProcessLarge Amounts used in a Process Non-RedundantRedundancy not an Issue High frequency of AccessLow/Modest frequency of Access Purpose Differences For “Clerical Community”For “Managerial Community” Supports Day-to-Day OperationsSupports Managerial Needs Application OrientedSubject Oriented OPERATIONAL vs. ANALYTICAL DATA
Hardware Utilization (Frequency of Access) Operational Data Warehouse