Data warehousing, technology assessment and management

1
Data warehousing, technology assessment and
management

• Journal Source Industrial Management Data
  Systems Volume 100 Number 3 2000 pp. 125-135
• Author Catherine Ma, David C. Chou, David C. Yen
• Student ???
• Advisor ???

2
Outline

• Introduction
• Implications of data warehouse
• Data warehouse evolution
• Technological analyses
• Objectives and management
• Future developments
• Conclusion

3
Introduction

• DW concept springs from
• Business requirement for a company wide view of
  info.
• Info system department to manage company in a
  better way.
• DW become the decision support trend.

4
Implications of data warehouse

• Mattison explained DW as a database that
• Is organized to serve as a neutral data storage
  area.
• Is used by data-mining and other applications.
• Meets a specific set of business requirement.
• Uses data that meets a predefined set od business
  criteria.

5
Implications of data warehouse

• Brown presented three basic technical
  characteristics of every data warehouse
• Data extraction.
• Connectivity.
• High performance RDBMS.

6
Data warehouse evolution

• Devlin indicated four major evolution stages
• Prehistoric times
• The middle ages
• The data revolution
• The era of information-based management

Information-basedManagement
Middle ages
Data revolution
Prehistoric times
7
Data warehouse current status

• Data warehouse has three primary uses
• Allows data coming from different transaction
  systems to be consolidated into the warehouse and
  used in reporting.
• Supports a type of query and reporting called
  dimensional analysis.
• Enables an exciting new technology called data
  mining.

8
Technological analyses

• Online analytical processing (OLAP)
• Data mining
• Data visualization

9
Online analytical processing

• A category of software technology that enables
  analysts, managers, and executives to gain
  insight into data through fast, consistent,
  interactive access to a wide variety of possible
  views of information that has been transformed
  from raw data to reflect the read dimensionality
  of the enterprise as understood by user. (Inmon,
  1992)

10
OLAP characteristics

• Fast on-line access
• Strong analysis and deriving capability
• Strong query organizing capability
• Aggregate capability

11
OLAP Pros and Cons
12
Data Mining

• Data mining, from business perspective, can be
  defined as the process of scanning a large data
  set to glean information. (Reeves, 1995)
• The vast assortment of data-mining products can
  be included following types
• Query managers and report writers
• Spreadsheets
• Multidimensional databases
• Statistical-analysis tools
• Artificial intelligence and advanced analysis
  tools
• Graphical-display tools

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FOCUS Query

• DEFINE FILE ACTAIR
• GRD/A3 GRADE
• NAME/A18 EDIT (bba, '999999999999999999')
• SSAN/A9 bacx
• DAS/A6 aabx
• OFC/A8 bchx
• DYPH/A10 bcg
• STRD/A6 waz
• ODSD/A6 waj
• CPAS/A32 CLTXT_BCA
• UNIT/A24 EDIT (CPAS, '99999999999999999999999
  9')
• DAFS/A7 jbc
• DAFS1/A1 EDIT (DAFS, 99999)
• CAFS/A7 nas
• CAFS1/A5 EDIT (CAFS, 99999)
• PAFS/A7 nab
• END

• TABLE FILE ACTAIR
• PRINT GRD AS '' IN 0 NAME AS '' IN 5 SSAN AS ''
  IN 26 OFC AS '' IN 38
• DYPH AS '' IN 48 DAS AS '' IN 60 OVER
• STRD AS '' IN 0 ODSD AS '' IN 8 CAFS AS '' IN 16
  DAFS AS '' IN 25
• PAFS AS '' IN 34
• HEADING
• "lt21 3S0X1 PERSONNEL IN GRADES E-6 AND E-7"
• "lt29 PRODUCED YMD"
• " "
• "lt2 GRD lt7 NAME lt28 SSAN lt40 OFC-SYM lt50 DY-PH
  lt62 DAS"
• "lt2 STRD lt10 ODSD lt18 CAFSC lt27 DAFSC lt36 PAFSC"
• " "
• BY UNIT NOPRINT SUBHEAD
• "lt0 UNIT ltUNIT"
• " "
• BY NAME NOPRINT SKIP-LINE
• WHERE (CAFS1 EQ '3S01') OR (DAFS1 EQ 3S01)
• WHERE (gaax EQ 36 OR 37) AND (atc NE '6')
• END

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Data mining characteristics

• Data pattern determination
• Formatting capability
• Content analysis capability
• Synthesis capability

15
Data mining Pros and Cons
16
Data visualization

• Data visualization is the use of graphics to show
  the reams of data for analysis and decision
  making.
• Spreadsheets and OLAP tools are examples of
  products that moderate data visualization in
  business.
• Two primary types of tools are used to develop
  advanced data visualization applications
  specialized programming languages, and GUI
  exploration and development tools.

17
Data visualization characteristics

• A combined class of tools.
• Particularly valuable for viewing data queried
  from data warehouse.
• Complex technique to allow multidimensional
  display.

18
Data Visualization Pros and Cons
19
Objectives and management

• Objectives issues
• Supports strategic decision making.
• Supports integrated business value chain.
• Speeds up response time to business queries.
• Data quality.
• Management Issues
• Data management ISO 11179
• Process management
• Security management

20
ISO 11179

• Part 1 Framework for the specification and
  standardization of data elements
• Part 2 Classification for Data Elements
• Part 3 Basic Attributes of Data Elements
• Part 3 (2002) Metadata registries (MDR) --
  Registry metamodel and basic attributes
• Part 4 Rules and Guidelines for the Formulation
  of Data Definitions
• Part 5 Naming and Identification Principles for
  Data Elements
• Part 6 Registration of Data Elements

21
Future developments

• Single information source
• Distributed information availability
• Automated information delivery
• Information quality and ownership

22
Conclusion

• Brown (1995) suggested the ten steps necessary to
  build a successful data warehouse
• 1 Clearly define the set of business issues that
  need to be solved by the data warehouse.
• 2 Identify the operational, financial, management
  and other criteria by which a successful
  implementation will be measured and evaluated.
• 3 Determine what data are required in the data
  warehouse.
• 4 Design the data warehouse by using a data model
  appropriate for decision support.
• 5 Estimate the data and index sizes of the
  initial data warehouse and project the
  warehouse's volume for the next year.

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Conclusion

• 6 Determine the frequency with which new data
  will be added to the warehouse and estimate the
  volumes of the data.
• 7 Pilot test the data warehouse with a subset of
  actual data.
• 8 Have end users implement and work with the
  pilot system for a period of time.
• 9 Understand vendors' future product strategies
  specifically related to data warehousing and
  evaluate against success criteria.
• 10 Invest in education and training for
  client-server technology, networking, and UNIX.