Curriculum Design and Instruction To Teach
Building A Data Warehouse For Decision
Support: Training, Support, and Rollout:
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:
@ Training, Support, and Rollout:
A. Success criteria:
B. Training:
C. Support:
D. Internal marketing of the data warehouse:
E. Data Warehouse Marketing Ideas:
1. Target specific groups:
2. Get clear and visible management support:
3. Provide visible opportunites:
4. Be proactive:
5. Create a publication:
F. Planning a Rollout: Deployment:
1. Phased Rollout Approach:
2. Logistics of a Rollout:
G. Summary:
* STATE OF THE ART CURRICULUM DESIGN:
* NEW:
* ILLUSTRATIONS:
* DIAGRAMS-CHARTS:
* COLOR PHOTOS:
* BIBLIOGRAPHICAL REFERENCES & INDEX:
* PAPERBACK:
* TRANSPARENT FRONT PAGE:
* BLACK-WHITE-RED OR BLUE BACK PAGE COVER:
* BINDED WIRE-0: BLACK-WHITE-RED OR BLUE:
* 100 WHITE PAGES: 8x11"
* ALLOW 10 TO 14 DAYS TO RECEIVE ITEM:
