2. 1  Dr. Chen. I n t r o d u c t i o n t o Decision Support Systems Professor Jason Chen School of Business Gonzaga University Spokane, WA 99258 chen@gonzaga.edu mbus633 Copyright ©, Dr. Chen Decision Support, E-Business, and OLAP Decision Support, e-Business, and OLAP

3. 2  Dr. Chen. Objectives Identify the changes taking place in the form and use of decision support in E-Business enterprises. Identify the role and reporting alternatives of management information systems. Describe how online analytical processing can meet key information needs of managers. Explain the decision support system concept and how it differs from traditional management information systems.

4. 3  Dr. Chen. Objectives (cont.) Explain how executive information systems can support the information needs of executives and managers. Explain organizations are warehousing and mining data. Give examples of several ways expert systems can be used in business decision- making situations.

5. 4  Dr. Chen. Enterprise Information Portals and DSS Enterprise Information Portal Gateway Enterprise Information Portal User Interface Search Agents Search Agents OLAP Data Mining Data Mining Knowledge Management Knowledge Management Database Management Functions Data Mart Other Business Applications Operational Database Analytical Database Knowledge Base DSS What-If Models Sensitivity Models Goal-Seeking Models Optimization Models Internet Intranet Extranet

6. 5  Dr. Chen. Decisions in the E-Business Strategic Management Tactical Management Operational Management Decisions Information Decision Characteristics Unstructured Semi-structured Structured Planning and Control of Overall Organizational Direction by Top Management Planning and Control of Organizational Subunits by Middle Management Planning and Control of Day to Day Operations by Supervisory Management

7. DATA WORKERS KIND OF SYSTEM GROUPS SERVED STRATEGIC LEVEL SENIOR (ESS,EIS,DSS) MANAGERS MANAGEMENT LEVEL MIDDLE (DSS, MIS)MANAGERS OPERATIONAL OPERATIONAL LEVEL (TPS,OAS) MANAGERS KNOWLEDGE LEVEL KNOWLEDGE & (KWS) SALES & MANUFACTURING FINANCE ACCOUNTING HUMAN RESOURCESMARKETING Types of the Information Systems

8. 7  Dr. Chen. Management Information System (DBMS) Reports Periodic Scheduled Reports Periodic Scheduled Reports Exception Reports Demand Reports and Responses Demand Reports and Responses Push Reports Major Management Information Systems (DBMS) Reports

9. 8  Dr. Chen. TaskEnvironment User Software System DBMS MBMS DGMS The Decision Support Systems DBMS: DataBase Management Systems MBMS: ModelBase Management Systems DGMS: DialoGue Management Systems

10. 9  Dr. Chen. Decision Support Systems What If-Analysis Sensitivity Analysis Goal-Seeking Analysis Optimization Analysis Important Decision Support Systems Analytical Models Important Decision Support Systems Analytical Models

11. 10  Dr. Chen. OnLine Analytical Processing (OLAP) OLAP Server OLAP Server Multi- dimensional database Corporate Databases Client PC Web-enabled OLAP Software Data is retrieved from corporate databases and staged in an OLAP multi-dimensional database Operational DB Data Marts Data Warehouse

12. 11  Dr. Chen. Tools used in the User Interface A variety of tools used by OLAP to query and analyze data stored in data warehouse and data marts:  Traditional query and reporting tools (SQL, QBE, QBF)  Spreadsheets  Data Mining tools.  Data Visualization tools. *

13. COMPONENTS OF DATA WAREHOUSE

14. 13  Dr. Chen. Three-layer data warehouse architecture 1. Operational data and systems 2. EDW 3. DM Quality, Integrity, and Historical Data

15. 14  Dr. Chen. Definitions Data Warehouse: An integrated and consistent store of subject-oriented data that is obtained from a variety of sources and formatted into a meaningful context to support decision-making in an organization. Bill Inmon, the acknowledged father of the Data Warehouse, defines it as an integrated, subject- oriented, time-variant, non-volatile database that provides support for decision making.

17. 16  Dr. Chen. OLAP Activities Generating queries Requesting ad hoc reports Conducting statistical and other analyses Developing multimedia applications

18. 17  Dr. Chen. Using SQL for Querying SQL (Structured Query Language) Data language English-like, nonprocedural, very user friendly language, Free format Example: SELECTName, Salary FROMEmployees WHERESalary >2000

19. 18  Dr. Chen. Data Mining Knowledge discovery in databases Knowledge extraction Data archeology Data exploration Data pattern processing Data dredging Information harvesting

20. 19  Dr. Chen. Data Mining Examples A telephone company used a data mining tool to analyze their customer ’ s data warehouse. The data mining tool found about 10,000 supposedly residential customers that were expending over $1,000 monthly in phone bills. After further study, the phone company discovered that they were really small business owners trying to avoid paying business rates *

21. 20  Dr. Chen. Other Data Mining Examples 65% of customers who did not use the credit card in the last six months are 88% likely to cancel their accounts. If age $25,000 then the minimum loan term is 10 years. 82% of customers who bought a new TV 27" or larger are 90% likely to buy an entertainment center within the next 4 weeks.

22. Example of drill-down (b) Drill-down with color added (a) Summary report $75 TM 14- 21

23. 22  Dr. Chen. Multidimensionality 3-D + Spreadsheets (OLAP has this) Data can be organized the way managers like to see them, rather than the way that the system analysts do Different presentations of the same data can be arranged easily and quickly Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry Measures: money, sales volume, head count, inventory profit, actual versus forecast Time: daily, weekly, monthly, quarterly, or yearly

24. 23  Dr. Chen. Slicing a data cube

25. 24  Dr. Chen. Slicing a data cube Regions Salespersons

26. 25  Dr. Chen. Multidimensionality Limitations Extra storage requirements Higher cost Extra system resource and time consumption More complex interfaces and maintenance Multidimensionality is especially popular in executive information and support systems (EIS and ESS)

27. 26  Dr. Chen. Data Visualization and Multidimensionality Data Visualization Technologies Digital images Geographic information systems Graphical user interfaces Multidimensions Tables and graphs Virtual reality Presentations Animation

28. 27  Dr. Chen. Geographic Information Systems (GIS) A computer-based system for capturing, storing, checking, integrating, manipulating, and displaying data using digitized maps Spatially-oriented databases Useful in marketing, sales, voting estimation, planned product distribution Available via the Web Can use with GPS (Global Positioning System)

29. 28  Dr. Chen. Business Intelligence on the Web Can capture and analyze data from Web Tools deployed on Web

30. 29  Dr. Chen. Current data Short database transactions Online update/insert/delete Normalization is promoted High volume transactions Transaction recovery is necessary Low number of concurrent users Various ad hoc queries Current and historical data Long database transaction Batch update/insert/delete De-normalization is promoted Low volume transactions Transaction recovery is not necessary Low number of concurrent users More predefined queries, but are efficient in processing numerous ad hoc queries. Requires numerous indexing (approx. 50% data) OLTP OLAP (On Line Transaction Processing On Line Analytical Processing)

31. 30  Dr. Chen. Enterprise Information Portals and DSS Enterprise Information Portal Gateway Enterprise Information Portal User Interface Search Agents Search Agents OLAP Data Mining Data Mining Knowledge Management Knowledge Management Database Management Functions Data Mart Other Business Applications Operational Database Analytical Database Knowledge Base DSS What-If Models Sensitivity Models Goal-Seeking Models Optimization Models Internet Intranet Extranet

32. 31  Dr. Chen. Artificial Intelligence Applications Cognitive Science Applications Cognitive Science Applications Artificial Intelligence Artificial Intelligence Robotics Applications Robotics Applications Natural Interface Applications Natural Interface Applications Expert Systems Fuzzy Logic Genetic Algorithms Neural Networks Visual Perceptions Locomotion Navigation Tactility Natural Language Speech Recognition Multisensory Interface Virtual Reality

33. 32  Dr. Chen. Intelligent Agents Interface Tutors Interface Tutors Presentation Agents Presentation Agents Network Navigation Agents Network Navigation Agents Role- Playing Agents Role- Playing Agents User Interface Agents Information Management Agents Search Agents Search Agents Information Brokers Information Brokers Information Filters Information Filters N

34. 33  Dr. Chen. Components of Expert Systems The Expert System Knowledge Base User Workstation Expert Advice User Interface Programs User Interface Programs Inference Engine Program Inference Engine Program Expert System Development Workstation Knowledge Engineering Knowledge Acquisition Program Knowledge Acquisition Program Expert and/or Knowledge Engineer

35. 34  Dr. Chen. Expert System Applications Decision Management Diagnostic/Troubleshooting Maintenance/Scheduling Design/Configuration Selection/Classification Major Application Categories of Expert Systems Process Monitoring/Control

36. 35  Dr. Chen. eBusiness Key Concepts eBusiness –The strategy of how to automate old business models with the aid of technology to maximize customer value eCommerce –The process of buying and selling over digital media eCRM (eCustomer Relationship Management) –The process of building, sustaining, and improving eBusiness relationships with existing and potential customers through digital media

37. E-Channel Management Procurement Network Trading Network E-Customer Relationship E-Commerce E-Portal Management E-Services SCM/ERP/Legacy Appls Businesses Businesses & Consumers 1:NM:1M:N Knowledge Management/Business Intelligence Focus on e-Business Applications

38. 37  Dr. Chen. The E-Business Application Architecture

39. 38  Dr. Chen. Data Mining Application Areas Marketing Banking Retailing and sales Manufacturing and production Brokerage and securities trading Insurance Computer hardware and software Government and defense Airlines Health care Broadcasting Law enforcement

40. 39  Dr. Chen. Intelligent Data Mining Use intelligent search to discover information within data warehouses that queries and reports cannot effectively reveal Find patterns in the data and infer rules from them Use patterns and rules to guide decision making and forecasting Five common types of information that can be yielded by data mining: 1) association, 2) sequences, 3) classifications, 4) clusters, and 5) forecasting

41. 40  Dr. Chen. Main Tools Used in Intelligent Data Mining Case-based Reasoning Neural Computing Intelligent Agents Other Tools –Decision trees –Rule induction –Data visualization

42. 41  Dr. Chen. Major Data Mining Characteristics and Objectives Data are often buried deep Client/server architecture Sophisticated new tools--including advanced visualization tools--help to remove the information “ore” End-user miner empowered by data drills and other power query tools with little or no programming skills Often involves finding unexpected results Tools are easily combined with spreadsheets, etc. Parallel processing for data mining

43. 42  Dr. Chen. How does a Company to Survive and/or Prosper? To survive and/or prosper in the turbulent e-Age, an organization should focus on three areas: –Core competencies, –Business models, and –Execution Operations People Strategies

44. 43  Dr. Chen. Summary Decision support systems in business are changing. The growth of corporate intranets, extranets, and other web technologies have increased the demand for a variety of personalized, proactive, web-enabled analytical techniques to support DSS. Information systems must support a variety of management decision-making levels and decisions. These include the three levels of management activity: strategic, tactical, and operational.

45. 44  Dr. Chen. Summary (cont’d) Online analytical processing (OLAP) is used to analyze complex relationships among large amounts of data stored in multidimensional databases. Data mining analyzes large stores of historical data contained in data warehouses. Decision support systems are interactive computer-based information systems that use DSS software and a model base to provide information to support semi- structured and unstructured decision making.

46. 45  Dr. Chen. Summary (cont’d) The major application domains in artificial intelligence include a variety of applications in cognitive sciences, robotics, and natural interfaces. Organizations are warehousing and mining data.

47. 46  Dr. Chen. Homework Complete an OLAP assignment on #2 of Chapter 6 (p.211) of the text Copy the file CardiologyCategorical.xls What you should turn in –A floppy contains the file with the work done (label with your name) –A hardcopy with answers for questions #2 (i.e., a thru e) [use of Word is required]

48. 47  Dr. Chen. Break !

49. 48  Dr. Chen. Virtual Reality An environment and/or technology that provides artificially generated sensory cues sufficient to engender in the user some willing suspension of disbelief Can share data and interact Can analyze data by creating a landscape Useful in marketing, prototyping aircraft designs VR over the Internet through VRML

50. 49  Dr. Chen. AI Application Areas in Business Neural Networks Fuzzy Logic Systems Virtual Reality Expert Systems AI Application Areas in Business AI Application Areas in Business Intelligent Agents Genetic Algorithms

51. 50  Dr. Chen. Data Warehouse DBMS Data Extract Data Cleanup Data Load MRDB MDDB Data Marts Information Delivery System Legacy & External Data Admin Platform Repository Update Process ODS Metadata Applications & Tools Report Query EIS Tools OLAP Tools Data Mining Tools Management Platform Transform Load Data Warehouse and Operational Data Stores. 1 2 3 4 5 6 8 7