The Decision Support Life Cycle

Curriculum Design and Instruction To Teach

Building A Data Warehouse For Decision

Support: The Decision Support Life Cycle:

Author: Charles Hayes:



A Data Warehouse is the main repository

of the organization's historical data,

its corporate memory. For example, an

organization would use the information

that's stored in its data warehouse to

find out what day of the week they sold

the most widgets in May 1992, or how

employee sick leave the week before the

winter break differed between California

and New York from 2001-2005. In other words,

the data warehouse contains the raw material

for management's decision support system. The

critical factor leading to the use of a data

warehouse is that a data analyst can perform

complex queries and analysis (such as

data mining) on the information without

slowing down the operational systems.

While operational systems are optimized for

simplicity and speed of modification (online

transaction processing, or OLTP) through heavy

use of database normalization and an entity-

relationship model, the data warehouse is

optimized for reporting and analysis

(on line analytical processing, or OLAP).

Frequently data in data warehouses is

heavily denormalised, summarised and/or

stored in a dimension-based model but this

is not always required to achieve acceptable

query response times.


More formally, Bill Inmon

(one of the earliest and most

influential practitioners)

defined a data warehouse as

follows:

1. Subject-oriented, meaning that the data

in the database is organized so that all

the data elements relating to the same

real-world event or object are linked

together;

2. Time-variant, meaning that the changes

to the data in the database are tracked

and recorded so that reports can be

produced showing changes over time;

3. Non-volatile, meaning that data in the

database is never over-written or deleted,

once committed, the data is static, read-only,

but retained for future reporting;


4. Integrated, meaning that the database contains

data from most or all of an organization's

operational applications, and that this data

is made consistent.









Special Features Include:

Phases For Conducting a Needs Assessment:
Curriculum Design Supplement:
|a|. Subject-Questions-Answers:
Curriculum Design Plan:
Curriculum Design Goals:
Curriculum Design Objectives:
Instructional Goals:
Instructional Objectives:
Instructional Activities:
Instructional Evaluation Techniques:
Lesson Plans:
Standard Vocabulary:
Learning Objectives:
Key Terms:
A Limited Glimpse:


Topics Include:

* Introduction:

@ The Decision Support Life Cycle:

A. Life Cycles for System Development:

B. Issues Affecting The Decision Support

Life Cycle:

C. The Phases of the Decision Support Life

Cycle (DSLC):

1. Phase 1: Planning:

2. Phase 2: Gathering Data Requirements

and Modeling:

Gathering Data Requirements

Data Modeling:

3. Phase 3. Physical Database Design and

Development:

4. Phase 4. Data Mapping and Transformation:

5. Phase 5: Populating the Data Warehouse:

a. Tips from the Trenches:

Availability of Data:

6. Phase 6. Automating Data Management

Procedures:

7. Phase 7: Application Development-Creating

the Starter Set of Reports:

8. Phase 8: Data Validation and Testing:

9. Phase 9: Training:

10. Phase 10: Rollout:

11. Summary:

* STATE OF THE ART CURRICULUM DESIGN:

* NEW:

* ILLUSTRATIONS:

* DIAGRAMS-CHARTS:

* COLOR PHOTOS:

* BIBLIOGRAPHICAL REFERENCES & INDEX:

* PAPERBACK:

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