Data Services Open the Door To Data Sharing
The business agility and cost savings benefits of data services are significant, especially as common data is used by myriad departments, as well as by partners, suppliers, customers and others.
Data services development and deployment benefit from internal and industry-wide XML data standards that specify common, agreed-upon formats.
However, developing data services that adhere to standards is not a trivial exercise. And providing these services via scalable information architecture adds complexity and scale.
Build your Data Services Layer with Data Virtualization
The Cisco Data Virtualization Platform enables rapid development of XML standards-compliant data services deployed in a layered data virtualization architecture so you can gain the business agility and cost savings you seek.
- Reusable Services – With CIS, each data service can be developed, deployed and modified as an independent, standalone component. This increases flexibility and reuse.
- Flexibility to Change Sources and Consumers – A CIS-based data services layer loosely-couples data sources and consumers. This reduces the impact of changes.
- Rapid Development to Increase Responsiveness – A CIS-based data services layer can be developed and changed easily and rapidly using modern development tools. This improves IT responsiveness.
- Layered Services Simplify Development and Increase Flexibility – Data virtualization best practices for data abstraction recommend segregating sourcing services, transformation services, business services, etc. This simplifies development and provides even greater flexibility and reuse.
- Data Governance Provides Control – Cisco’s data governance provides data security, data quality and 7x24 operations to maximize control throughout the data services layer.
Standards-based Data Services Layer
- Sharing Refinery Data Enterprise-wide – Using the process manufacturing standard called MIMOSA along with Cisco data virtualization, this energy company ensures similar data is delivered consistently across all refineries. For example, operations analysts studying pump failures to optimize preventative maintenance now have easy access to data from all pumps in all of the refineries, thereby significantly improving the quality of analysis. This approach has led to better business decision making that has increased refinery yields, lowered equipment maintenance costs and produced better regulatory compliance.
- Sharing Intelligence Data across Government Agency Boundaries– To better control threats, intelligence analysts need to use information both within and across multiple government agencies. For example, using CIS 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 now easily share passenger, crew and manifest data from a U.S. Coast Guard port arrivals data warehouse. As a result, these agencies better protect America’s security, doing so in a more cost effective way.