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  1. Decision support tools Lecture 2: OLAP & Data Mining

  2. Module structure • Management Decision-Making • OLAP & Data Mining • Group Support Systems • Executive Support Systems • Model-based Decision Support Systems • Intelligent Systems • Expert Systems • Managing Decision Support Tools

  3. The decision-making process

  4. What happens in decision-making? • Trigger for decision-making • To solve a problem • To take advantage of an opportunity • Stages of decision-making (Simon, 1977) • Intelligence: define the problem or opportunity • Design: develop and evaluate alternative solutions • Choice: select the ‘best’ course of action

  5. Decision making vs. Problem solving

  6. Data needed for decision-making • Intelligence stage • Becoming aware of the problem • Defining the problem details and scope • Design stage • Identifying alternative solutions • Evaluating feasibility of each solution • Choice stage • Deciding on the ‘best’ solution Internal or external data? Actual or estimated values?

  7. Previous class test question You are considering exporting fruit from South Africa to Namibia. During which stage of decision-making would you estimate the cost impact of the different shipping options that are available to you? • Design • Implementation • Intelligence • Choice • Monitoring

  8. Selecting the ‘best’ solution • Optimising • Explores all possible solutions • Identifies the best outcome based on numbers • Satisficing • Explores a limited number of possible solutions • Stops if the best outcome meets requirements • Heuristics • Relies on approaches that have worked in the past

  9. But there’s a potential problem Business decisions are generally made by people – and we all know that people aren’t perfect

  10. The answer is ‘Business Intelligence’ • Business Intelligence is • A broad category of applications and technologies for gathering, storing, analysing, and providing access to data, to help enterprise users make better business decisions. • BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. Definition from

  11. Knowledge Discovery

  12. Multi-dimensional data analysis • OLAP and Data Mining • Explores the relationship between multiple variables • Usually at least three variables involved • Relies on large data sets • Usually has a time component • Graphical display aids understanding • Identifies patterns occurring in data • Can provide a basis for developing mathematical models

  13. On-Line Analytical Processing (OLAP) • Queries are (explicitly) initiated by users • Reveals relationships between data items • Detects trends • Clarifies problem definition • Easy to use • Visual interface • Flexible • Drill-down • NOT done on operational database

  14. Example of OLAP output

  15. “Slice and dice” views Different people may have different interests in the same dataset

  16. OLAP vs standard DBMS queries • Quick to compose, run and modify • No programming skills needed • Visual output is more user friendly • Key measures are already calculated • OLAP structure can be used to build models (e.g. financial) • Can provide input to other applications (e.g. performance management)

  17. Equivalent Excel functions • Pivot tables • Pivot charts • Statistical analysis • Correlation • Multiple regression • Analysis of variance • Cluster analysis • We’ll be using Excel as a prac example • But business applications for OLAP and data mining go way beyond Excel!

  18. 18 months & R10 million • The business value of Supply Chain Intelligence enabled by a Global Procurement Intelligence solution would be derived through: • The ability to identify, measure, manage and report procurement spend, price and consumption variances, trends and cost pressures across the group as well as by providing contract and vendor spend visibility; • The ability to drive group cost optimisation through best practice sourcing strategies; • Risk identification and mitigation capability; • Visibility and standardisation of consolidated spend and related policies; and • Data consolidation across the group, aligned to budgets and expenditure forecasts.

  19. Goldfields Annual Report 2008 (pg 18) “In South Africa the Project Beyond Integrated Strategic Sourcing and Procurement Initiative set out in F2005 to achieve cumulative contracted benefits of around R200 to R300 million after three years at the end of F2007. . . . After four years, continued cumulative benefits delivery to date has delivered contracted total cost benefits of around R319 million, adding R31 million during F2008. Additional cost avoidance benefits of around R34 million were also delivered during F2008, which brings the cumulative four year annualised benefits delivery (including cost avoidance) to around R445 million.”

  20. Moving on to Data Mining • Queries are NOT(explicitly) initiated by users • Automatedprocess to extract information from large data sets • Only as effective as the data it uses • Relies on advanced logical, statistical and mathematical techniques • Infers rules and relationships that allow the prediction of future results • But can’t explain underlying reasons

  21. How is data mining being used in SA? • CRM and direct marketing • Customer and product planning • Fraud detection • Credit scoring Benefits include: • Customer attraction and retention • Product design • Bad debt reduction • Bank robbery prediction • Product cross-selling

  22. Comparing OLAP and data mining

  23. Back to Simon’s model • Intelligence • Design • Choice • Where does OLAP fit in? • Where does data mining fit in?

  24. That’s all for today Tomorrow is a self-study day. Instead of coming to a lecture, read the Theory Case Study (Lexmark International) under DST Prac 1, answer the questions and submit your work on RUconnected by 2pm on Thursday 20 March. This handin will be marked as the ‘theory’ part of DST Prac 1.

  25. Herbert Alexander Simon (June 15, 1916 – February 9, 2001) was an American political scientist, economist, sociologist, psychologist and professor, whose research ranged across the fields of cognitive psychology, cognitive science, computer science, public administration, economics, management, philosophy of science, sociology, and political science. With almost a thousand very highly-cited publications, he was one of the most influential social scientists of the twentieth century. Simon was among the founding fathers of several of today's important scientific domains, including artificial intelligence, information processing, decision-making, problem-solving, attention economics, organization theory, complex systems, and computer simulation of scientific discovery. He also received many top-level honours later in life. These include: becoming a fellow of the American Academy of Arts and Sciences in 1959; election to the National Academy of Sciences in 1967;[ the ACM's Turing Award for making "basic contributions to artificial intelligence, the psychology of human cognition, and list processing" (1975); the Nobel Memorial Prize in Economics "for his pioneering research into the decision-making process within economic organizations" (1978); the National Medal of Science (1986); and the APA's Award for Outstanding Lifetime Contributions to Psychology (1993). Simon received an honorary Doctor of Political Science degree from University of Pavia in 1988 and an honorary Doctor of Laws (LL.D.) degree from Harvard University in 1990.