Data Warehouse Training with CONNX
Session Overview: Data Warehousing is an information management strategy that emphasizes consolidating data into a repository primarily for data standardization, centralized reporting, and strategic-advantage trend analysis. The technology research and consulting firm Meta Group Inc. estimates that 90% of the Fortune 1000 companies have a data warehouse in place as of January 1999. This comes in spite of other reports that companies spend an average of three to five million dollars per data warehouse project, and of those 75% fail or are never implemented.
For organizations considering to build or revamp their data warehousing strategies, you should realize how CONNX can contribute to the extraction, conversion, standardization, reporting - and success of your data warehouse projects. This session will provide you with an overview of data warehousing strategies such as enterprise data warehousing, data marts, and Business Intelligence, and then present how CONNX can help make your project a success.
Who Should Attend?: CIOs and IT Managers who have been asked by their Business Executives to provide a business intelligence infrastructure that is both responsive and responsible.
Power Users and Business Technologists who will utilize CONNX and the data warehouse to develop meaningful, business-oriented, and timely information to the organization decision-makers.
Methodologies: This session can be held in a boardroom or informal one-on-one forum. In either case, attendee participation or interaction is heartily encouraged. Training materials and instruction will be provided to each attendee for review during and following the session.
Skills You Will Acquire by Completing this Session
Upon completion of this session, you will have learned, practiced, and assimilated the following topics:
- Different principles and strategies involved in Enterprise Data Warehouses, Data Marts, and Business Intelligence Solutions.
- Why data warehousing projects fail.
- Appreciate how CONNX can reduce the risk, simplify the implementation, and be scaled as the size of the data warehouse increases.
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