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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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Examples of Fields

Walker

TA 347

John

Associate Professor

FOIS


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Example of a Record

LName FName Room Title Department

Sproule Susan TA364 Assistant Professor FOIS


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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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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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16

Artist Painting Gallerycreates displays1 1M M

1:M 1:M


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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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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
http://www.webdesignerdepot.com/2009/06/50-great-examples-of-data-visualization/http://www.liveplasma.com/http://www.bestiario.org/research/videosphere/http://www.bestiario.org/research/videosphere/http://www.liveplasma.com/http://www.webdesignerdepot.com/2009/06/50-great-examples-of-data-visualization/


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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.
http://www.bestiario.org/research/videosphere/http://mkweb.bcgsc.ca/schemaball/?homehttp://mkweb.bcgsc.ca/schemaball/?home


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Data Visualization

Hans Rosling shows the best stats youve ever s
http://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html


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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?
http://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html


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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.
http://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html


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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
http://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html


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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
http://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.htmlhttp://www.ted.com/talks/lang/eng/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html


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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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