CHAPTER 5 Data and Knowledge Management. CHAPTER OUTLINE 5.1 Managing Data 5.2 The Database Approach 5.3 Database Management Systems 5.4 Data Warehouses.

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  • CHAPTER 5 Data and Knowledge Management
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  • CHAPTER OUTLINE 5.1 Managing Data 5.2 The Database Approach 5.3 Database Management Systems 5.4 Data Warehouses and Data Marts 5.5 Knowledge Management
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  • LEARNING OBJECTIVES 1. Identify three common challenges in managing data, and describe one way organizations can address each challenge using data governance. 2. Name six problems that can be minimized by using the database approach. 3. Demonstrate how to interpret relationships depicted in an entity-relationship diagram. 4. Discuss at least one main advantage and one main disadvantage of relational databases.
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  • Learning Objectives (continued) 5. Identify the six basic characteristics of data warehouses, and explain the advantages of data warehouses and marts to organizations. 6. Demonstrate the use of a multidimensional model to store and analyze data. 7. List two main advantages of using knowledge management, and describe the steps in the knowledge management system cycle.
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  • Annual Flood of Data from.. Credit card swipes E-mails Digital video Online TV RFID tags Blogs Digital video surveillance Radiology scans Source: Media Bakery
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  • Annual Flood of New Data! In the zettabyte range A zettabyte is 1000 exabytes Fanatic Studio/Age Fotostock America, Inc.
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  • 5.1 Managing Data The Difficulties of Managing Data Data Governance
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  • Difficulties in Managing Data Source: Media Bakery
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  • Data Governance See videovideo Data Governance Master Data Management Master Data
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  • 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 Master Data John StevensStudent Intro to Management Information SystemsCourse ISMN 3140Course No. 10 AM until 11 AMTime Mondays and WednesdaysWeekday Room 41 Smith HallLocation Professor RainerInstructor
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  • 5.2 The Database Approach Database management system (DBMS) minimize the following problems: Data redundancy Data isolation Data inconsistency
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  • Database Approach (continued) DBMSs maximize the following issues: Data security Data integrity Data independence
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  • Database Management Systems
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  • Data Hierarchy Bit Byte Field Record File (or table) Database
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  • Hierarchy of Data for a Computer-Based File
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  • Data Hierarchy (continued) Bit (binary digit) Byte (eight bits)
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  • Data Hierarchy (continued) Example of Field and Record
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  • Data Hierarchy (continued) Example of Field and Record
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  • Designing the Database Data model Entity Attribute Primary key Secondary keys
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  • 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
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  • Relationships Between Entities
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  • Entity-relationship diagram model
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  • 5.3 Database Management Systems Database management system (DBMS) Relational database model Structured Query Language (SQL) Query by Example (QBE)
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  • Student Database Example
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  • Normalization Minimum redundancy Maximum data integrity Best processing performance Normalized data occurs when attributes in the table depend only on the primary key.
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  • Non-Normalized Relation
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  • Normalizing the Database (part A)
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  • Normalizing the Database (part B)
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  • Normalization Produces Order
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  • 5.4 Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing
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  • Data Warehouse Framework & Views
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  • Relational Databases
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  • Multidimensional Database
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  • Equivalence Between Relational and Multidimensional Databases
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  • 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.
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  • 5.5 Knowledge Management Knowledge management (KM) Knowledge Intellectual capital (or intellectual assets) Peter Eggermann/Age Fotostock America, Inc.
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  • Knowledge Management (continued) Tacit Knowledge (below the waterline) Explicit Knowledge (above the waterline) Ina Penning/Age Fotostock America, Inc.
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  • Knowledge Management (continued) Knowledge management systems (KMSs) Best practices Peter Eggermann/Age Fotostock America, Inc.
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  • Knowledge Management System Cycle Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge
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  • Knowledge Management System Cycle
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  • Chapter Closing Case The Problem The Solution The Results

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