Learning Objectives
                    •   Describe the issues in management of data.
                    •   Understand the concepts and use of DBMS.
                    •   Learn about data warehousing and data marts.
                    •   Explain business intelligence/business analytics.
                    •   Examine how decision making can be improved 
                        through data manipulation and analytics.
                    •   Understand the interaction betwixt the Web and 
                        database technologies.
                    •   Explain how database technologies are used in 
                        business analytics.
                    •   Understand the impact of the Web on business 
                        intelligence and analytics.
                    © 2005  Prentice Hall, Decision Support Systems and Intelligent Systems, 7th                   5-2
                    Edition, Turban, Aronson, and Liang
                                     Data, Information, 
                                                  Knowledge
                    • Data
                          – Items that are the most elementary descriptions 
                              of things, events, activities, and transactions
                          – May be internal or external
                    • Information
                          – Organized data that has meaning and value
                    • Knowledge
                          – Processed data or information that conveys 
                              understanding or learning applicable to a 
                              problem or activity
                    © 2005  Prentice Hall, Decision Support Systems and Intelligent Systems, 7th                   5-3
                    Edition, Turban, Aronson, and Liang
                                                            Data 
                    • Raw data collected manually or by 
                        instruments
                    • Quality is critical
                          – Quality determines usefulness
                                • Contextual data quality
                                • Intrinsic data quality
                                • Accessibility data quality
                                • Representation data quality
                          – Often neglected or casually handled
                          – Problems exposed when data is summarized
                    © 2005  Prentice Hall, Decision Support Systems and Intelligent Systems, 7th                   5-4
                    Edition, Turban, Aronson, and Liang
                    © 2005  Prentice Hall, Decision Support Systems and Intelligent Systems, 7th                   5-5
                    Edition, Turban, Aronson, and Liang
                                                             Data
                    • Cleanse data
                          – When populating warehouse
                          – Data quality action plan
                          – Best practices for data quality
                          – Measure results
                    • Data integrity issues
                          – Uniformity
                          – Version
                          – Completeness check
                          – Conformity check
                    © 2005  Prentice Hall, Decision Support Systems and Intelligent Systems, 7th                   5-6
                    Edition, Turban, Aronson, and Liang