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					CHAPTER 4


Data and Knowledge Management
CHAPTER OUTLINE

 4.1 Managing Data
 4.2 The Database Approach
 4.3 Database Management Systems
 4.4 Data Warehousing
 4.5 Data Governance
 4.6 Knowledge Management
LEARNING OBJECTIVES
 Recognize the importance of data, issues
  involved in managing data and their lifecycle.
 Describe the sources of data and explain how
  data are collected.
 Explain the advantages of the database
  approach.
Learning Objectives (continued)
 Explain the operation of data warehousing
  and its role in decision support.
 Explain data governance and how it helps to
  produce high-quality data.
 Define knowledge, and describe different
  types of knowledge.
Chapter Opening Case
Examples of Data Sources



  Credit card      RFID tags
  swipes                            Digital video
                                    surveillance




                                  Blogs


E-mails
                Radiology scans
 4.1 Managing Data
Difficulties in Managing Data
  Amount of data increases
   exponentially.
  Data are scattered and collected
   by many individuals using
   various methods and devices.
  Data come from many sources.
  Data security, quality and
   integrity are critical.
 Difficulties in Managing Data (continued)
 An ever-increasing amount of data needs to be
   considered in making organizational decisions.



The Data Deluge
Data Life Cycle   (Figure 4.1)
Data, Information, Knowledge, Wisdom
4.2 The Database Approach
 Database management system (DBMS)
  provides all users with access to all the data.
 DBMSs 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
4.3 Database Management Systems

 Database management system (DBMS)
 Relational database model
  Structured Query Language (SQL)
   Query by Example (QBE)
Student Database Example
Normalization
 Normalization is a method for analyzing and
  reducing a relational database to its most
  streamlined form for:
     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
  Turnitin (IT’s About Business 4.1)

A Turnitin
originality
report
4.4 Data Warehousing
Data warehouse
    Data warehouses are organized by business
     dimension or subject.
    Data warehouses are multidimensional.



A Data Cube
Data Warehousing (continued)
 Data warehouses are historical.
 Data warehouses 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.
     Data Marts

A data mart is a small data warehouse,
designed for the end-user needs in a
strategic business unit (SBU) or a
department.
4.5 Data Governance

  Data governance
  Master data management
  Master data
Data Governance (continued)
Data Governance (continued)
4.6 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

				
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posted:10/18/2012
language:English
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