Analytic data integration continues to be an expanding practice that’s usually applied to data warehousing and business intelligence. However, operational data integration (usually applied to the consolidation, collocation, migration, upgrade, or synchronization of operational databases) is growing even faster. This growth comes at a cost. Many corporations have staffed operational data integration by borrowing data integration specialists from data warehouse teams, which puts important BI work in peril. Others have gone to the other extreme, by building new teams and infrastructure that are redundant with analytic efforts. And the best practices of operational data integration are still coalescing, so confusion abounds.

You will learn:

• Business and technology drivers for operational data integration
• How requirements for tools, techniques, and teams vary between analytic and operational data integration
• Specialized technology requirements for operational data integration, including service oriented architecture, software as a service, data exchange standards, exception processing, and cross-functional collaboration
• Staffing, funding, and organizational approaches that accommodate both analytic and operational data integration