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Data and Knowledge Management
CHAPTER 5
5.1 Managing Data5.2 The Database Approach5.3 Database Management Systems
5.4 Data Warehouses and Data Marts
5.5 Knowledge Management
CHAPTER OUTLINE
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
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.
LEARNING OBJECTIVES (CONTINUED)
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
ANNUAL FLOOD OF NEW DATA!
In the zettabyte range
A zettabyte is 1000 exabytes
© Fanatic Studio/Age Fotostock America, Inc.
5.1 MANAGING DATA
The Difficulties of Managing Data
Data Governance
DIFFICULTIES IN MANAGING DATA
Source: Media Bakery
DATA GOVERNANCE
See video
•Data Governance
•Master Data Management
•Master Data
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 DataJohn Stevens StudentIntro to Management Information Systems CourseISMN 3140 Course No.10 AM until 11 AM TimeMondays and Wednesdays WeekdayRoom 41 Smith Hall LocationProfessor Rainer Instructor
Database management system (DBMS) minimize the following problems:
Data redundancyData isolationData inconsistency
5.2 THE DATABASE APPROACH
DBMSs maximize the following issues:
Data securityData integrityData independence
DATABASE APPROACH (CONTINUED)
DATABASE MANAGEMENT SYSTEMS
BitByteFieldRecordFile (or table)Database
DATA HIERARCHY
HIERARCHY OF DATA FOR A COMPUTER-BASED FILE
Bit (binary digit)
Byte (eight bits)
DATA HIERARCHY (CONTINUED)
Example of Field and Record
DATA HIERARCHY (CONTINUED)
Example of Field and Record
DATA HIERARCHY (CONTINUED)
Data modelEntityAttributePrimary keySecondary keys
DESIGNING THE DATABASE
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 MODELING
RELATIONSHIPS BETWEEN ENTITIES
ENTITY-RELATIONSHIP DIAGRAM MODEL
Database management system (DBMS)
Relational database model Structured Query Language (SQL)
Query by Example (QBE)
5.3 DATABASE MANAGEMENT SYSTEMS
STUDENT DATABASE EXAMPLE
Normalization
Minimum redundancy
Maximum data integrityBest processing performance
Normalized data occurs when attributes in the table depend only on the primary key.
NORMALIZATION
NON-NORMALIZED RELATION
NORMALIZING THE DATABASE (PART A)
NORMALIZING THE DATABASE (PART B)
NORMALIZATION PRODUCES ORDER
Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing
5.4 DATA WAREHOUSING
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
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.
BENEFITS OF DATA WAREHOUSING
Knowledge management (KM) KnowledgeIntellectual capital (or intellectual assets)
5.5 KNOWLEDGE MANAGEMENT
© Peter Eggermann/Age Fotostock America, Inc.
KNOWLEDGE MANAGEMENT (CONTINUED)
Tacit Knowledge(below the waterline)
Explicit Knowledge (above the waterline)
© Ina Penning/Age Fotostock America, Inc.
Knowledge management systems (KMSs)
Best practices
KNOWLEDGE MANAGEMENT (CONTINUED)
© Peter Eggermann/Age Fotostock America, Inc.
Create knowledgeCapture knowledgeRefine knowledgeStore knowledgeManage knowledgeDisseminate knowledge
KNOWLEDGE MANAGEMENT SYSTEM CYCLE
KNOWLEDGE MANAGEMENT SYSTEM CYCLE
CHAPTER CLOSING CASE
• The Problem
• The Solution
• The Results
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