Thursday, April 9, 2009
08:30 AM - 09:30 AM
Do you have a need to integrate data from multiple different data sources on time and on budget? How do you measure the status of a large scale data migration effort against a project schedule? What are the right “Metrics” to measure that the project is making progress toward a Production release date? Are you concerned about duplicate data in the legacy systems and which records are accurate? These are just some of the challenges that face IT professionals and project managers who work on systems integration initiatives on a daily basis.
Systems integration, which usually includes data migration, are made even more difficult in the U.S. Federal Government. As data is integrated from multiple agencies, and even multiple organizations within the same agency to create common systems for the Government, politics and users spread across the country enhance the challenge further. This session will address:
- The challenges faced by in the U.S. Government to implement these programs on time and on budget.
- How to implement a Measures and Metrics program to measure the success of a data migration effort.
- The creation of meaningful data migration metrics to measure the quality of the data upon migration.
- Discuss lessons learned that can be applied to Government, and the private industry for a successful migration effort.
Mr. McGinn, an Associate in the Data Management practice of Booz Allen Hamilton, Inc., is responsible for the delivery of enterprise wide information strategy and architecture, business intelligence and data migration solutions. He has designed and built data architectures, data migration strategies, and data warehouses for clients in the commercial and government sector.
James specializes in helping organizations design, develop, and implement information systems infrastructures that are optimized to enable those organizations to meet industry challenges. He has worked as a project manager, developer, and analyst to numerous companies and government agencies to devise and build technical architectures for migrating legacy databases, data profiling, data quality, data management, and data warehouse solutions to help those organizations leverage their information and gain or solidify a competitive advantage.