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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 Dr. S. Loizidou - ACSC345 2 Decision-support Systems What is a decision-support system (DSS)? Dr. S. Loizidou - ACSC345 3 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 4 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 5 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 6 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 7 DSS Components TPS User Interface DSS Database External Data DSS Software System: Models OLAP Tools Data Mining Tools User Dr. S. Loizidou - ACSC345 8 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 9 DSS Models Statistical (typical) Optimisation Forecasting Sensitivity analysis – “What if” – Repeatedly modify parameters of model to determine outcome Dr. S. Loizidou - ACSC345 10 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 11 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 12 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 13 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 14 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 15 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 16 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 17 Data Mining Forecasting – Use existing data to forecast future values Example – Past performance to predict sales figures Dr. S. Loizidou - ACSC345 18 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 19 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 20 Example Questions 1. 2. 3. 4. Who are our most frequent customers? Do they live close to our shops? How can we resegment those customers? How can we better reach those segments? 1. Customer data warehouse •Legacy data •Website transactions •Call centre data •External data 2. 3. 4. Dr. S. Loizidou - ACSC345 Analysis 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 21