Download 5e PP ch5 - Harbert College of Business

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