Transcript Ch05

CHAPTER 5
Data and Knowledge Management
Announcements
• Today
• Chapter 5 – Data and Knowledge Mgmt
• Friday
• Access Tutorial
• Questions/comments
Data and Knowledge Management:
Application to other majors
• Accounting
• Marketing
• Finance
• Operations Management
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
Industrial Revolution of Data => “Big Data”
Measuring amount of data
Bit (binary digit)
Byte (eight bits)
Letter “A” = 01000001
Annual Flood of New Data!
In the zettabyte range
1 zettabyte
=
1,000,000,000,000,000
,000,000 bytes
“Big
Data” is getting bigger
Solutions to manage it
© Fanatic Studio/Age Fotostock America, Inc.
Difficulties in Managing Data
Amount of Data
increasing
Data is subject to
data rot
(degrade overtime)
Multiple sources of
data
Information
systems that do not
communicate with
each other
Spread throughout
organizations
Source: Media Bakery
Solution: Governance
Data Governance => Set of Rules
Implementation Strategy:
Master Data Management
Goal: Create a
“single version of the truth”
Master Data Management:
Types of Data
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 The Database Approach
Database management system (DBMS)
Minimize the following problems:
1. Data redundancy
2. Data isolation
3. Data inconsistency
Maximize the following:
1. Data security
2. Data integrity
3. Data independence
Database Management Systems
How is data organized in a DB:
Data Hierarchy
Field
Is a grouping of
Record
Is a grouping of
File (or table)
Is a grouping of
Database
Data Hierarchy (continued)
Example:
 Field
 Record
Data Hierarchy (continued)
Example:
 Field
 Record
Designing the Database:
Data Model

Entity (e.g. Student)
 Instance (e.g. John Doe)



Attribute (e.g. Student name)
Primary key (e.g. Student ID)
Secondary keys (e.g. Major)
Entity
Primary
Secondary
Instance
Student
Student
Student ID (pk)
Student Name
Student Address
Student Major
850000000 (pk)
John Doe
123 Anywhere St.
MIS
Data Model Technique:
Entity-Relationship Modeling
Database designers plan the database design
in a process called entity-relationship (ER)
modeling/diagrams.
ER diagrams consists of entities, attributes and
relationships.
Entity-relationship diagram model
Database Management Systems
Database management system (DBMS)
Focus of this course: Relational database model


Related Tables (PK Important)
Data dictionary
 How do you request data?

Structured Query Language (SQL) - keywords

Query by Example (QBE) – forms/templates
Microsoft Access
Student Database Example
Relational DB Effectiveness:
Normalization
Normalization (most streamlined DB)



Minimum redundancy
Maximum data integrity
Best processing performance
Normalized data occurs when attributes in the
table depend only on the primary key.
Non-Normalized Database
Normalizing the Database (part A)
Normalizing the Database (part B)
Normalization Produces Order
Beyond Databases:
Data Warehousing
Data warehouses and Data Marts




Organized by business dimension or subject
Multidimensional
Historical
Use online analytical processing
Data Warehouse Framework & Views
Source
Systems
Data
Integration
Data
Storage
Users
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.
Knowledge Management (KM)

Knowledge


aka. Intellectual Capital
Knowledge management systems (KMSs)


Goal: Systematize, enhance and expedite
Examples
© Peter Eggermann/Age Fotostock America, Inc.
Challenges to Capturing Knowledge
Explicit Knowledge
(above the waterline)
Policies, Procedures, Goals
Tacit Knowledge
(below the waterline)
Experience, Insights, Know-how
© Ina Penning/Age Fotostock America, Inc.
Knowledge Management System Cycle
DB Exercise
Create the entities you would need for a
student registration system. Draw out how
the tables may look and how they are
related.
Next Class…
• Access Tutorial (bring casebook)
• We will be continuing through the first
Access Tutorial (Sales Rep)
• Bring your flash drive
• You will need to save your files either to a
flash drive or your Timmy account