Key components of an effective data virtualization architecture

Is your organization prepared to implement a data virtualization architecture? A successful implementation often requires coordination between several technical pieces for capturing data, organizing it and ensuring data quality and governance. The ultimate goal of a data virtualization architecture, which can provide integrated views of data from different source systems, is to enable users and applications to access data without having to understand the intricacies of the underlying data technology. Sometimes that's easier said than done, but luckily there are a set of specific components that can help ensure effective implementation and management. These range from a good abstraction tier to help hide some of the underlying complexity, a metadata management layer to help orchestrate important data virtualization processes and data quality adeptness to help identify problems and clean data. It's also important to work out some of the governance and security issues around the underlying data and how it's shared.