Standards-compliant Data Services
Problem – Creating Standards-compliant Data Services Is Difficult
Internal and industry-wide XML data standards establish a common, agreed-upon consumption format for all data consumers, regardless of source format or location. In recent years, these standards have become increasingly important as data is shared within the myriad departments and groups of a single large enterprise, as well as beyond the firewall to partners, suppliers, customers and others. However, developing data services that adhere to standards is not a trivial exercise.
Solution – Use Data Virtualization to Enable Standards Compliance
Composite data virtualization enables rapid development of XML standards-compliant data services. With Composite, each service can be developed, deployed and modified as an independent, standalone component, providing greater flexibility and reusability. In addition, Composite data services loosely-couple the data sources and consumers, and therefore reduce the impact of changes at either source or consumer level. Third, Composite data services can be developed and changed easily and rapidly using modern Eclipse-based development tools, thereby providing the ease of use and agility required in today’s fast-paced business world. Finally, Composite can leverage other data services to split the work, for example across sourcing services, federation services, and standards transformation services to provide even greater flexibility and reuse.
Standards Compliant Data Services
Selected Examples
- Virtualizing Refinery Data Enterprise-wide – Using the process manufacturing standard called MIMOSA along with Composite data virtualization, the energy company ensures similar data is delivered the same way across all its refineries. As a result, operations analysts studying pump failures to optimize preventative maintenance have easy access to data from all pumps in all of the refineries, thereby significantly improving the quality of analysis. Similarly, process engineers may access pump data from multiple refineries, and analyze it with a different tool set for refining process optimization. This approach has led to better business decision making that has increased refinery yields, lowered equipment maintenance costs, and produced better compliance with a myriad of regulations.
- Sharing Intelligence Data across Government Agency Boundaries– To better control threats, intelligence analysts need to use information both within and across the multiple multiple government agencies. For example, using Composite and several standards, including the Intelligence Community Data Layer (ICDL), the National Information Exchange Model (NIEM,) and the Maritime Information Exchange Model (MIEM), agencies such as the Drug Enforcement Administration and the Immigration and Naturalization Service can easily share passenger, crew and manifest data from a U.S. Coast Guard port arrivals data warehouse. As a result, these agencies ensure America’s security in a cost-effective way.
