An IDC research study found that, in 2010, the universe of information exceeded one zettabyte, or one trillion gigabytes, for the first time in history, and is expected to grow another nine fold over the next five years.

Just keeping up is a huge challenge.

Further this data resides in various silos developed over many years of technology acquisition and business consolidation.

So add data integration to your "most important things to-do" list.

Data Virtualization to the Rescue
Data virtualization has been purpose-built to address these challenges through an agile, high-value data integration approach that address these volumes, quickly providing business with the timely data it needs, even if the data required spans multiple silos.  Below are twelve good reasons why businesses and government agencies use data virtualization.

Twelve Good Reasons to Use Data Virtualization

1. Data Virtualization Delivers Value Five Ways - Data virtualization delivers value to business functions and IT operations in a number of measurable ways including top-line revenue growth, risk reduction, time savings, technology savings and staff savings.
2. Data Virtualization Discovers Complex Data - Data virtualization platforms include a discovery option that provides the simplest and fastest way to find and explore enterprise data and prepare the models needed by your downstream data integration and application development processes.
3. Data Virtualization Abstracts Source Data - Enterprises and government agencies use data virtualization to resolve the mismatch between how their data is stored (formats, structures, APIs, etc.) and how their data is used in reports, portals and other consuming applications mismatch.
4. Data Virtualization Accesses Diverse Data - With diverse sources across multiple locations, often outside the firewall, enterprises use data virtualization to facilitate source data access using standard approaches including ODBC, JDBC, and ADO.NET for relational, JMS and SOAP for Web services, APIs for packaged applications and legacy systems, Java for procedural interfaces, and adapters for mainframes.
5. Data Virtualization Federates Data Silos - Adding new meaning and value to previously isolated data, data virtualization lets organization seamlessly federate data silos.
6. Data Virtualization Delivers Timely Information with High Performance - High-performance query techniques and a number of advanced caching methods allow data virtualization to ensure up-to-the-minute data whenever needed.
7. Data Virtualization Secures Data - Data virtualization leverages LDAP and Active Directory authentication to enforce user-based data security rules already established in source and consuming systems.
8. Data Virtualization Complements Physical Data Consolidation and ETL - Leading organizations understand that a portfolio of data integration techniques and technologies are required to effectively meet today's wide range of needs.
9. Data Virtualization Meets both Project and Enterprise Level Requirements - Most organizations initially deploy data virtualization to meet project specific integration requirements and then expand adoption across their enterprises.
10. Data Virtualization Accelerates SOA Transition - Data virtualization works in conjunction with other SOA tools such as Enterprise Services Buses (ESBs), Registries, and Application Servers, so you enterprises can leverage prior SOA technology investments.
11. Data Virtualization Is Easy to Build and Use - Data virtualization platforms simplify, accelerate and improve each of the major steps in the typical software development lifecycle process.
12. Data Virtualization Fits Neatly Within an Existing IT Environment - Data virtualization supports key industry standards such as ODBC, JDBC, ADO.NET, SOAP, JMS, SQL, XQuery, Java, and REST as well as open APIs for metadata and administration.   This enables data virtualization to easily leverage existing metadata, data, hardware, and software assets and to run in any environment without restriction.

Conclusion
Data virtualization a proven approach used by enterprises and government agencies to address a range of critical data integration challenges including data complexity, structure, location, completeness, and latency.

Following the established path of storage, server, and applications virtualization, data virtualization saves time, staff and technology costs, so enterprises and agencies can increase revenue and productivity, while reducing risk.

If your enterprise or government agency faces similar challenges, consider data virtualization.  There are twelve good reasons to do so.

• When Should We Use Data Virtualization?
• Book Excerpt: When to Use Web Services
• Cloud Storage: Just In Time For Data Center Consolidation.