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Managing Information Systems

Managing Information Systems. Enhancing Management Decision Making Part 1 Dr. Stephania Loizidou Himona ACSC345. Objectives. To understand types of decision-support systems To understand the components of a decision-support system. Decision-support Systems.

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Managing Information Systems

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  1. Managing Information Systems Enhancing Management Decision Making Part 1 Dr. Stephania Loizidou Himona ACSC345

  2. Objectives • To understand types of decision-support systems • To understand the components of a decision-support system Dr. S. Loizidou - ACSC345

  3. Decision-support Systems • What is a decision-support system (DSS)? Dr. S. Loizidou - ACSC345

  4. MIS or DSS? • Management Information Systems: • Routine reports (periodic) • Assist control of an organisation • Decision-support Systems: • Non-routine • Support flexibility and rapid response • Semi-structured or unstructured data Dr. S. Loizidou - ACSC345

  5. Types of DSS • Model-driven • Uses a model to perform ‘what if’ analysis • Typically standalone • In-house or departmental • Strong theory or model Dr. S. Loizidou - ACSC345

  6. Types of DSS • Data-driven • Analyse large amounts of data • Data from TPS into data warehouses • Use • On-line Analytical Processing (OLAP) • Data mining Dr. S. Loizidou - ACSC345

  7. Data-driven Examples • Contrast • How many widgets were shipped in December? • With • Compare the sales of widgets to the sales plan by quarter and sales region for the last two years? Dr. S. Loizidou - ACSC345

  8. DSS Components External Data DSS Database TPS DSS Software System: Models OLAP Tools Data Mining Tools User Interface User Dr. S. Loizidou - ACSC345

  9. DSS Models • Abstract representation that illustrates the components or relationships of the problem • Physical: model of an airplane • Mathematical: profit = revenue - costs • Verbal: description of a procedure Dr. S. Loizidou - ACSC345

  10. DSS Models • Statistical (typical) • Optimisation • Forecasting • Sensitivity analysis • “What if” • Repeatedly modify parameters of model to determine outcome Dr. S. Loizidou - ACSC345

  11. OLAP(On-line Analytical Processing) • Dynamic multi-dimensional analysis of enterprise data • Just-in-time information • Wide variety of views of information • Transformation of raw data: • Reflects the ‘real’ dimensionality of enterprise Dr. S. Loizidou - ACSC345

  12. OLAP • Data: • Loading – bulk and operational, internal and external • Aggregation • Processing: • Application of business models and statistics • Querying: • Complex • Drill-down through hierarchies • Ad-hoc Dr. S. Loizidou - ACSC345

  13. Data Mining • Provides a way of finding hidden insight not obtained by traditional techniques. • Uses: • Statistical analysis • Neural networks • Fuzzy logic • Genetic Algorithms • Rule-based systems Dr. S. Loizidou - ACSC345

  14. Data Mining • Associations • Occurrences linked to a single event • Example • Supermarket purchases • When crisps are bought, 85% of the time a can of Coca-cola is bought Dr. S. Loizidou - ACSC345

  15. Data Mining • Sequences • Events linked over time • Example • House purchase • Within two weeks, 65% of the time a refrigerator is bought • Within one month, 45% of the time an oven is bought Dr. S. Loizidou - ACSC345

  16. Data Mining • Classification • Recognise pre-defined patterns to group similar items • Example • Telephone operators • Recognise those attributes of customers who are likely to leave Dr. S. Loizidou - ACSC345

  17. Data Mining • Clustering • Recognise patterns to cluster similar items without pre-defined groups • Example • Bank customer details • Partitioning data into groups by demographics or investments Dr. S. Loizidou - ACSC345

  18. Data Mining • Forecasting • Use existing data to forecast future values • Example • Past performance to predict sales figures Dr. S. Loizidou - ACSC345

  19. DSS Examples • Supply Chain Management • Who, what, when and where? • Purchasing, manufacture and distribution • Customer Relationship Management • Pricing • Customer retention • New revenue streams Dr. S. Loizidou - ACSC345

  20. DSS Examples • Business Scenarios • Sensitivity analysis of business parameters • Cost / benefit analysis • Geographic Information Systems (GIS) • Display information geographically • Demographics, customers, crime Dr. S. Loizidou - ACSC345

  21. Example Questions Analysis • Who are our most frequent customers? • Do they live close to our shops? • How can we re-segment those customers? • How can we better reach those segments? • Use statistical analysis to find top 25% most frequent customers. • Establish correlation between location and sales • Verify new customer segments • Query database on customer information per segment • Customer data warehouse • Legacy data • Website transactions • Call centre data • External data Dr. S. Loizidou - ACSC345

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