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IS500: 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?.

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IS500: Information Systems Instructor: Dr. Boris Jukic

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  1. IS500: Information SystemsInstructor: Dr. Boris Jukic Decision Support Systems

  2. Systems and Technologies that Support Organizational Decision Making • Decision-enabling, problem-solving, and opportunity-seizing systems

  3. 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

  4. 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

  5. EXECUTIVE INFORMATION SYSTEMS • Digital dashboard – integrates information from multiple components and present it in a unified display

  6. 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?

  7. Artificial intelligence (AI) • The three most common categories of AI include: • Expert systems – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems • Neural Networks – attempts to emulate the way the human brain works • Intelligent agents – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users Common example: shopping bot

  8. Data Mining • Common forms of data-mining analysis capabilities include • Cluster analysis • Association detection • Statistical analysis

  9. 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

  10. 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

  11. 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

  12. 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)

  13. Need for Data Warehousing • Integrated, company-wide view of high-quality information. • Separation of operational and analytical systems and data.

  14. OPERATIONAL vs. ANALYTICAL DATA Operational DataAnalytical 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

  15. OPERATIONAL vs. ANALYTICAL DATA Hardware Utilization(Frequency of Access) Operational Data Warehouse

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