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CHAPTER 5 Data and Knowledge Management CHAPTER OUTLINE 5.1 Managing Data 5.2 Big Data 5.3 The Database Approach 5.4 Database Management Systems 5.5 Data Warehouses and Data Marts 5.5 Knowledge Management LEARNING OBJECTIVES 1. Discuss ways that common challenges in managing data can be addressed using data governance. 2. Define Big Data, and discuss its basic characteristics. 3. Explain how to interpret the relationships depicted in an entity-relationship diagram. 4. Discuss the advantages and disadvantages of relational databases. Learning Objectives (continued) 5. Explain the elements necessary to successfully implement and maintain data warehouses. 6. Describe the benefits and challenges of implementing knowledge management systems in organizations. Chapter Opening Case Big Data! The data deluge is here Chapter Opening Case (continued) Big Data and HR Chapter Opening Case (continued) Big Data and product development Chapter Opening Case (continued) Big Data and operations Chapter Opening Case (continued) Big Data and marketing 5.1 Managing Data The Difficulties of Managing Data Data Governance Difficulties in Managing Data The Data Deluge Data Governance See video Data Governance (continued) Master Data Management John Stevens registers for Introduction to Management Information Systems (ISMN 3140) from 10 AM until 11 AM on Mondays and Wednesdays in Room 41 Smith Hall, taught by Professor Rainer. Transaction Data John Stevens Intro to Management Information Systems ISMN 3140 10 AM until 11 AM Mondays and Wednesdays Room 41 Smith Hall Professor Rainer Master Data Student Course Course No. Time Weekday Location Instructor 5.2 Big Data video Annual Flood of Data from….. Credit card swipes RFID tags Digital video surveillance E-mails Blogs Digital video Radiology scans Online TV Annual Flood of New Data! In the zettabyte range A zettabyte is 1000 exabytes 5.3 The Database Approach Database management system (DBMS) minimize the following problems: Data redundancy Data isolation Data inconsistency Database Approach (continued) DBMSs maximize the following issues: Data security Data integrity Data independence Database Management Systems Data Hierarchy Bit Byte Field Record File (or table) Database Hierarchy of Data for a Computer-Based File Data Hierarchy (continued) Bit (binary digit) Byte (eight bits) Data Hierarchy (continued) Example of Field and Record Data Hierarchy (continued) Example of Field and Record Designing the Database Data model Entity Attribute Primary key Secondary keys Entity-Relationship Modeling Database designers plan the database design in a process called entity-relationship (ER) modeling. ER diagrams consists of entities, attributes and relationships. Entity classes Instance Identifiers Entity-Relationship Diagram Model 5.4 Database Management Systems Database management system (DBMS) Relational database model Structured Query Language (SQL) Query by Example (QBE) Student Database Example Normalization Normalization Minimum redundancy Maximum data integrity Best processing performance Normalized data is when attributes in the table depend only on the primary key. Non-Normalized Relation Normalizing the Database (part A) Normalizing the Database (part B) Normalization Produces Order 5.5 Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject. Multidimensional. Historical. Use online analytical processing. A Data Cube Data Warehouse Framework & Views Relational Databases Multidimensional Database Equivalence Between Relational and Multidimensional Databases Equivalence Between Relational and Multidimensional Databases Equivalence Between Relational and Multidimensional Databases Benefits of Data Warehousing End users can access data quickly and easily via Web browsers because they are located in one place. End users can conduct extensive analysis with data in ways that may not have been possible before. End users have a consolidated view of organizational data. Data Marts 5.6 Knowledge Management Knowledge management (KM) Knowledge Intellectual capital (or intellectual assets) Knowledge Management (continued) Explicit Knowledge (above the waterline) Tacit Knowledge (below the waterline) Knowledge Management (continued) Knowledge management systems (KMSs) Best practices Knowledge Management System Cycle Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge Knowledge Management System Cycle