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CHAPTER 4
Data, Information, andKnowledge Management
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Examples of Data Sources
E-mails
Credit cardswipes
RFID tagsDigital videosurveillance
Radiology scans
Blogs
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Managing Data
Difficulties in Managing Data Amount of data increases
exponentially
Data are scattered and collectedby many individuals usingvarious methods and devices
Data come from many sources Data security, quality, and
integrity are critical
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Difficulties in Managing Data(continued)
An ever-increasing amount of data needs to be consideredin making organizational decisions.
The Data Deluge
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Data Life Cycle
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Hierarchy of Data for a
Computer-Based File
Database
File (or table)
RecordField
Byte
Bit
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Data Hierarchy (continued)
Bit (binary digit)
Byte (eight bits)
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Data Hierarchy (continued)
Examples of Fields
Walker
TA 347
John
Associate Professor
FOIS
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Data Hierarchy (continued)
Example of a Record
LName FName Room Title Department
Sproule Susan TA364 Assistant Professor FOIS
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Data Hierarchy (continued)
Example of a File (or Table)
LName FName Room Title Department
Walker John TA347 Associate Professor FOIS
Herath Tejaswini TA366 Assistant Professor FOIS
DeSimone Valerie TA350 Administrative Assistant FOIS
Cyr Donald TA313 Acting Dean BUSADMIN
Wright Barry TA240 Associate Professor OBHREE
EMPLOYEE
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Data Hierarchy (continued)
Example of a Database
EMPLOYEE
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Designing the Database
Data model Entity
Attribute
Primary key
Foreign key
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Entity-Relationship Modeling
Database designers plan the databasedesign in a process called entity-relationship (ER) modeling.
ER diagramsconsists of entities, attributesand relationships. Entity classes
Instance Identifiers
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Data Modeling
It is the data that is modeled, not theinformation
14
BirthdateSubtract
from
todays
date
Age (in
years)
DATA PROCESS INFORMATION
SYSTEM
+ =
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Relational Data Model
Jay acts as an agent for a group of painters.He arranges for his clients paintings to beloaned to art galleries
What are the entities that Jay needs to keepdata on?
Describe the relationships between theseentities?
15
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Relational Data Model
16
Artist Painting Gallerycreates displays1 1M M
1:M 1:M
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Relational Data Model
17
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Many-to-many Relationships
18
Many-to-many relationships are broken down
to two one-to-many relationships
EnrollmentStudent Course
Student CoursetakesM M
MM1 1has for
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Many to Many relationships
Students, Courses, Professors
Customer, Order and Products
19
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Converting Entity Relationship
Diagrams to Relational Tables
Create one table for each entity
List all attributes that need to be recorded
Make sure that each table has a primarykey
Introduce a foreign key into the many
side to represent a 1 to many relationship
20
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Converting Entity Relationship
Diagrams to Relational Tables
Enrolment Example
STUDENT
SIDNameMajorAddress
STUDENT COURSEENROLL1M1 M
ENROLLMENT
SIDCourseIDSection COURSE
CourseIDRoomProfessorID
PROFESSOR
1
M
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Enrollment example (cont.)
22
SID Name Major Address
100 Jones Accounting 123 Main St.
150 Parks History 234 James St.
200 Baker Math 34 King St.
.. .. .. ..
SID CourseID Section
100 BD445 1
150 BA200 2
200 CS250 1
.. .. ....
CourseID Room ProfessorID
BA200 SC100 5678
BD445 SC213 5789
CS250 EA304 5345.. .. ..
STUDENT
Relation :
ENROLLMENT
Relation :
COURSE
Relation :
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Data Modeling
Walkerss Air Charters (Draw an ERD) Customers request charter trips
Each charter trip requires an airplane
There is a pilot and a copilot on each charter trip The company owns different models of airplanes
Pilots are employees of the company
All employee pilots can either pilot or copilot on any
trip
23
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CUSTOMER
MODEL
AIRCRAFT
CHARTER
PILOT
EMPLOYEE
is an
pilots
requests requires
has a
copilots
1
M
1
1 1
11
1
M
M M
M
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Database Management Systems Database Management Systems (DBMS)Interfaces
with the database, and provides all users withintegrated access to the data.
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DBMS - Examples
Microsoft Access
Microsoft SQL Server
Oracle IBM DB2
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Database Management Systems
DBMS minimize the following problems: Data redundancy
Data isolation
Data inconsistency Data security
Data integrity
Data independence
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Normalization
Normalization is a method for analyzing andreducing a relational database to its moststreamlined form for: Minimum redundancy
Maximum data integrity
Best processing performance
Normalized data is when attributes in thetable depend only on the primary key.
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Non-Normalized Relation
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Normalizing the Database (part A)
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Normalizing the Database (part B)
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Normalization Produces Order
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Database Basics
Primary types of databases Operational production or transaction databases
RWED
Analytical Multi-dimensional (OLAP), data warehouse, data mart
Read-only
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Data Life Cycle
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Information needs
Operational Queries What is the promised date of purchase
order X? Which of our customers sales orders
are late? What is the demand for item Y next
month?
Analytical Queries What was the revenue from new
markets comparing to the averagerevenue from the rest of our markets inthe last quarter?
How successful were our leather
products that cost less than 5$ amongour regular customers last month?
How much did we sell during holidayscomparing to regular days in theEuropean market last year?
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Data Warehouse
Organized by business dimension or subject.
Consistent
Historical
Nonvolatile Multidimensional.
A Data Cube
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Data Transformation (ETL)
Data is extracted Can be from different databases, in different formats must
be cleaned Data is transformed
Derive calculated value (e.g., sale_amount = qty *unit_price) Joining together data from multiple sources
Data is loaded Timing and scope of additions or overwrites
Integrity checks Audit trails
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Data Warehouse Framework &
Views
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Relational Databases
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Multidimensional Database
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Equivalence Between Relational and
Multidimensional Databases
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Equivalence Between Relational and
Multidimensional Databases
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Equivalence Between Relational and
Multidimensional Databases
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Benefits of Data Warehousing
End users can access data quickly and easilyvia Web browsers because they are locatedin one place
End users can conduct extensive analysiswith data in ways that may not have beenpossible before
End users have a consolidated view oforganizational data
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Data Marts
Adata martis a small data warehousedesigned for the end-user needs in astrategic business unit (SBU) or adepartment.
O li A l ti l P i
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Online Analytical Processing
(OLAP)
Typical OLAP screenshot
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Data Visualization
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Data Visualization
50 great examples of data visualization Liveplasma
TED Sphere
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Data Visualization
Scemaball plots the relationships between tables in a database. The yellow
Lines represent foreign keys that are linking tables.
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Data Visualization
Hans Rosling shows the best stats youve ever s
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Data Mining
EU Resist See handout
Video
Artificial intelligence in data-mining Neural networks
Machine learning (case-based learning)
What issues do you see surrounding the collectionand use of this data?
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Data Mining
Diamonds in the Data Mine Describe Harrahs competition strategy. How is it
different from its competitors?
How does the company maintain customer loyalty?What are the specific technologies, systems,methods and tactics used to enhance customerservice.
Describe the problems encountered initially andthe ideas that worked later, to make the customerreward system a success.
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Knowledge Management
Knowledge management (KM)
Intellectual capital (or intellectual assets)
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Knowledge Management(continued)
Tacit Knowledge
(below the waterline)
Explicit Knowledge(above the waterline)
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Knowledge Management System
Cycle
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Knowledge Management(continued)
Knowledge management systems (KMSs) Repositories
Expert systems
Blogs
Wikis
Social networks
Best practices
M i l I i t d ith
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Managerial Issues associated with
Data Resource Management
Cost-benefit issues/justification Legacy data problems
Where to store the data physically Internal or external
Disaster recovery
Data security
Privacy, legal and ethical issues
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Copyright 2008 John Wiley & Sons Canada, Ltd. All rightsreserved. Reproduction or translation of this work beyond thatpermitted by Access Copyright (the Canadian copyright licensingagency) is unlawful. Requests for further information should be
addressed to the Permissions Department, John Wiley & SonsCanada, Ltd. The purchaser may make back-up copies for his orher own use only and not for distribution or resale. The author andthe publisher assume no responsibility for errors, omissions, ordamages caused by the use of these files or programs or from the
use of the information contained herein.
Copyright
Adapted and supplemented by Susan Sproule & John Walker
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