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 and Data Marts 5.5 Knowledge Management
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.
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.
Annual Flood of Data from Credit card swipes E-mails Digital video Online TV RFID tags Blogs Digital video surveillance Radiology scans
Annual Flood of New Data! In the zettabyte range A zettabyte is 1,000 exabytes 10 21 or 2 70 10 18 or 2 60 Just under 64-bit
5.1 Managing Data The Difficulties of Managing Data Data Governance
Difficulties in Managing Data Growing exponentially Scattered Multiple sources Timeliness Data Security Data Quality Data Integrity Data Consistency Federal regulations Source: Media Bakery
Data Governance Data Governance Master Data Management Master Data See video
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 Stevens Student Intro to Management Information Systems Course ISMN 3140 Course No. 10 AM until 11 AM Time Mondays and Wednesdays Weekday Room 41 Smith Hall Location Professor Rainer Instructor
5.2 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
Relationships Between Entities
Entity-relationship diagram model
5.3 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 (most of the time) Normalized data occurs 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
5.4 Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing
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.
5.5 Knowledge Management Knowledge management (KM) Knowledge Peter Eggermann/Age Fotostock America, Inc. Intellectual capital (or intellectual assets)
Knowledge Management (continued) Explicit Knowledge (above the waterline) Tacit Knowledge (below the waterline) Ina Penning/Age Fotostock America, Inc.
Knowledge Management (continued) Knowledge management systems (KMSs) Peter Eggermann/Age Fotostock America, Inc. Best practices
Knowledge Management System Cycle 1. Create knowledge 2. Capture knowledge 3. Refine knowledge 4. Store knowledge 5. Manage knowledge 6. Disseminate knowledge
Knowledge Management System Cycle