Data and Knowledge

coilcruelManagement

Nov 6, 2013 (3 years and 9 months ago)

61 views

10
-
1

Data and Knowledge
Management

10
-
2

Data Management:

A Critical Success Factor


The difficulties and the process


Data sources and collection


Data quality


Multimedia and object
-
oriented databases


Document management

10
-
3

The Difficulties and the Process:

The Difficulties


Data amount increases exponentially


Data: multiple sources


Small portion of data useful for specific
decisions


Increased need for external data

10
-
4

The Difficulties and the Process:

The Difficulties


Differing legal requirements among
countries


Selection of data management tool
-

large
number


Data security, quality, and integrity

10
-
5

The Difficulties and the Process:


Data Life Cycle Process and

Knowledge Discovery


Data Collection


Stored in databases


Processed


Stored in data warehouse


Transformation
-

ready for analysis


Data mining tools
-

knowledge


Presentation

10
-
6

Data Sources and Collection


Internal data


Personal data


External data


Internet and commercial database services


Methods for collecting raw data

10
-
7

Data Quality (DQ)


Intrinsic DQ:



Accuracy, objectivity, believability, and
reputation



Accessibility DQ:



Accessibility and access security



10
-
8

Data Quality (DQ)


Contextual DQ:




Relevancy, value added, timeliness,
completeness



Representation DQ:



Interpretability, ease of understanding, concise
representation, and consistent representation

10
-
9

10
-
10

Multimedia and Object
-
Oriented
Databases


Object
-
Oriented database (multimedia
database)


Document management

10
-
11

Data Warehousing,

Mining, and Analysis


Transaction versus analytical processing


Data warehouse and data marts


Knowledge discovery, analysis, and mining

10
-
12

Transaction Versus Analytical
Processing

Good Data Delivery System


Easy data access by end users


Quicker decision making


Accurate and effective decision making


Flexible decision making

10
-
13

Transaction Versus Analytical
Processing

Solution


Business representation of data for end
users


Client
-
server environment
-

end users query
and reporting capability


Server
-
based repository (data warehouse)

10
-
14

The Data Warehouse and Marts


The purpose of a data warehouse is to
establish a data repository that makes
operational data accessible in a form readily
acceptable for analytical processing
activities . . .


A data mart is … dedicated to a functional
or regional area.

10
-
15

Characteristics of Data
Warehousing


Organization


Consistency


Time variant


Nonvolatile


Relational

10
-
16

The Data Warehouse and Marts


Benefits


Cost


Architecture


Putting the data warehouse on the internet


Suitability

10
-
17

Knowledge Discovery, Analysis,
and Mining


Foundations of knowledge discovery in
databases (KDD)


Tools and techniques of KDD


Online analytical processing (OLAP)


Data mining

10
-
18

The Foundations of Knowledge
Discovery in Databases (KDD)


Massive data collection


Powerful multiprocessor computers


Data mining algorithms

10
-
19

10
-
20

OLAP Queries


Access very large amounts of data


Analyze the relationships between many
types of business elements


Involve aggregated data


Compare aggregated data over hierarchical
time periods

10
-
21

OLAP Queries


Present data in different perspectives


Involve complex calculations between data
elements


Able to respond quickly to user requests

10
-
22

Data Mining


Automated prediction of trends


Automated discovery of previously
unknown patterns

10
-
23

Data Mining

Characteristics and Objectives


Data often buried deep within large
databases


Data may be consolidated in data
warehouse or kept in internet and intranet
servers


Usually client
-
server architecture

10
-
24

Data Mining

Characteristics and Objectives


Data mining tools extract information
buried in corporate files or archived public
records


The “miner” is often an end user


“Striking it rich” usually involves finding
unexpected, valuable results


Parallel processing

10
-
25

Data Mining

Characteristics and Objectives


Data mining yields five types of
information


Data miners can use one or several tools

10
-
26

Data Mining Yields Five Types
of Information


Association


Sequences


Classifications


Clusters


Forecasting

10
-
27

Data Mining Techniques


Case
-
based reasoning


Neural computing


Intelligent agents


Others: decision trees, genetic algorithms,
nearest neighbor method, and rule reduction

10
-
28

Data Visualization Technologies


Data visualization


Multidimensionality


Geographical information systems (GIS)



10
-
29

Data Visualization


Data visualization refers to presentation of
data by technologies digital images,
geographical information systems, graphical
user interfaces, multidimensional tables and
graphs, virtual reality, three
-
dimensional
presentations and animation.

10
-
30

Multidimensionality


Major advantage
-

data can be organized the
way managers prefer to see the data


There factors: dimensions, measures, and
time

10
-
31

Examples


Dimensions


Products, salespeople, market segments,
business units, geographical locations


Measures


Money, sales volume, head count, inventory,
profit, actual versus forecasted


Time


Daily, weekly, monthly, quarterly, yearly

10
-
32

Geographical Information
Systems (GIS)


A GIS is a computer
-
based system for
capturing, storing, checking, integrating,
manipulating, and displaying data using
digitized maps.

10
-
33

Geographical Information
Systems (GIS)


Software


Data


Emerging GIS applications

10
-
34

Emerging GIS Applications


Integration of GIS and GPS


Reengineer aviation and shipping industries


Intelligent GIS (integration of GIS and ES)


User interface


Multimedia, 3D graphics, animated and
interactive maps


Web applications

10
-
35

Marketing Databases in Action


The Marketing Transaction Database
(MTD)


Implementation Examples

10
-
36

The Marketing Transaction
Database (MTD)

… a new kind of database, oriented toward
targeting and personalizing marketing
messages in real time.

10
-
37

10
-
38

Knowledge Management


Knowledge management or managing
knowledge databases



A
knowledge base

is a database that
contains infromation or organizational
know how.

10
-
39

Knowledge Management


Knowledge bases and organizational
learning


Implementing knowledge management
systems

10
-
40

Arthur Andersen’s

Learning Organization Knowledge Base


Global best practices hotline


These data combined with ongoing research
identify areas to be developed


Research analysis team with content experts
to develop best practices


Qualitative and quantitative information and
tools are released on CD
-
ROM for
corporate wide access

10
-
41

Arthur Andersen’s

Knowledge Base


Best company profiles


Relevant Arthur Andersen engagement
experience


Top 10 case studies and articles


World
-
class performance measures


Diagnostic tools

10
-
42

Arthur Andersen’s

Knowledge Base


Customizable presentations


Process definitions and directory of internal
experts


Best control practice


Tax implementations

10
-
43

Managerial Issues


Cost
-
benefit analysis


Where to store data physically


Disaster recovery


Internal or external


Data security and ethics


Data purging

10
-
44

Managerial Issues


The legacy data problem


Data delivery


Privacy

10
-
45

Copyright


1999 John Wiley & Sons, Incorporated. All rights
reserved. Reproduction or translation of this work beyond that
permitted in Section 117 of the 1976 United States Copyright Act
without the express written permission of the copyright owner in
unlawful. Request for further information should be addressed to
the Permissions Department, John Wiley & Son, Inc. Adopters of
the textbook are granted permission to make back
-
up copies for
his/her own use only, to make copies for distribution to student of
the course the textbook is used in, and to modify this material to
best suit their instructional needs. Under no circumstances can
copies be made for resale. The publisher assumes no
responsibility for errors, omissions, or damages, caused by the use
of these programs or from the use of the information contained
herein.