DATA WAREHOUSE ENVIRONMENTS

The advance of technologies, such as client/server, data warehouse, and data distribution are reshaping the roles of both application development and information management. Information management is taking on increasing importance to enable competitive advantage business process re-engineering and value-adding to the information resource through the data warehouse.

In this seminar you learn how to implement effective information management processes to manage data in client/server, distributed, and data warehouse environments as an enterprise resource. Management, technical, and organizational issues required to implement information resource management (IRM) effectively in the distributed and decentralized computing environment are addressed. Potential pitfalls facing IRM in the client/server environment are identified along with guidance on how to avoid them.

The key to successful exploitation of any new technology is understanding the underlying paradigm and re-thinking the rules and regulations of that paradigm. This seminar illustrates guidelines and techniques for modeling client/server applications and data.

The term "data warehouse" is an unfortunate term. This seminar focuses on the fact that the data warehouse is a "distribution center" used to "informate" knowledge workers.

• Describe the client/server and data warehouse paradigm and their impact on information management
• Describe IRM objectives for distributed data
• Describe distributed IRM functions and responsibilities in a decentralized I/S organization
• Develop information policy to enable management of distributed data and decentralized development
• Develop effective data standards and procedures for client/server and data warehouse
• Describe an architectural approach for client/server applications and data
• Describe techniques for implementing integrity and security for client/server and distributed data
• Describe techniques for managing "redundancy" and distributed data in the client/server and data warehouse environments
• List major inhibitors to IRM and describe steps to avoid them
• Describe steps to transition to a distributed IRM function

 

6. Modeling Client/Server Data and Processes

A framework for modeling client/server applications and data

Distributed data and process modeling

Data and event models

7. Data Integrity Design for Client/Server

• Client/server architecture alternatives
• Using triggers and stored procedures effectively
• Business rules: a behavioral model
• Integrity policies: client versus server implementation
• Encapsulated development

8. Designing and Managing the Data Warehouse

• Data warehouse components
• The warehouse data model
• Mapping operational data to the warehouse model
• Modeling transform rules
• Strategic use of the warehouse data model

9. Distributed Database Management: Managing Redundant Data

• Migration/coexistence guidelines and strategies
• Redundancy/replication management

10. The Distributed Data Resource Management Organization

• I/S organization for distributed data and data warehouse
• Distributed IRM organization
• Information stewardship and accountability for distributed data
• Re-equipping the I/S professional for tomorrow’s information technologies

11. Assuring Success of Distributed Data Applications

• Developing a distributed IRM vision
• Acquiring management commitment
• Quantifying data value
• Maximizing information politics
• Managing technology-induced change effectively