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 MiningCharacteristics 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 MiningCharacteristics 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 MiningCharacteristics 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’sLearning 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’sKnowledge 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’sKnowledge 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

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