The Data Virtualization Gold Standard

Robert Eve

Subscribe to Robert Eve: eMailAlertsEmail Alerts
Get Robert Eve: homepageHomepage mobileMobile rssRSS facebookFacebook twitterTwitter linkedinLinkedIn


Related Topics: Virtualization Magazine, Desktop Virtualization Journal, Datacenter Automation

Article

What's So Great About Data Virtualization?

Value add from an additional data integration approach

Business Change Guides Data Integration Strategy
Traditional data integration approaches such as consolidation and replication alone are unable to keep pace with today's dynamic pace of business and ever changing information requirements. Seeking greater agility, lower costs, and less risk, organizations are increasing adopting  data virtualization.

Gain Agility and Cut Costs with Data Virtualization
Data virtualization is high-performance query middleware that integrates data from multiple, disparate sources - anywhere across the extended enterprise - in a unified, logically virtualized manner for on-demand consumption by a wide range of business solutions.

Data virtualization has a simple value proposition.  It  provides a more agile, lower cost data integration approach that overcomes data complexity and disparate silos to provide business with the timely data it needs to meet today’s ever-changing business requirements.  Data virtualization delivers the following benefits:

  • Improve Business Performance – Provide the information required to increase revenue and improve productivity;
  • Increase Agility – Beat competitors by responding faster to new and rapidly changing information demands;
  • Reduce Costs – Save staff and infrastructure resources from the start and then compound these savings over time;
  • Decrease Risk – Increase IT project success via rapid development and quick iterations;
  • Ensure Compliance – Meet regulatory compliance data requirements faster, for less

Overcome Difficult Data Integration Challenges
Data Virtualization addresses enterprise-scale data integration requirements while avoiding the higher costs and longer lead-times associated with data consolidation and replication.

These challenges include:

  • Business Need for Timely Insight – Up-to-the-minute data is a key business requirement.  Data virtualization's query optimization algorithms and techniques deliver the timely information required whenever needed, without impacting source system performance.
  • Business Need for Complete Picture – Data frequently needs to be combined with other data to provide business with the full picture. Data virtualization's data federation virtually integrates internal and external data in memory, without the cost and overhead of physical data consolidation.
  • Data Proliferation – Identifying and understanding data assets distributed across a range of fit-for-purpose repositories and locations requires significant manual effort. Data virtualization's data discovery saves time and money by automating entity and relationship identification and accelerating data modeling.
  • Data Complexity – Incredible complexity challenges IT’s ability to leverage existing data for new business uses.  Data virtualization’s powerful data abstraction tools simplify complex data, transforming it from native structures and syntax into easy-to-understand, reusable views and data services with common semantics.
  • Data Availability – With so many technologies, formats and standards, successfully surfacing data consumes significant IT resources. Data virtualization's numerous standards-based data access, caching and delivery options flexibly publish all the information business users require.
  • Limited Control – Data is a critical asset that must be governed.  Data virtualization's data governance centralizes metadata management, ensures data security, improves data quality and runs reliably 7x24 across scalable clustered servers to maximize control.
  • Environment of Non-Stop Change – New business requirements, new applications and new data sources make frequent change inevitable. Data virtualization's loosely-coupled data virtualization layer, rapid development tools, automated impact analysis and extensible architecture provide the information agility required to keep pace.

Mix and Match Data Virtualization with other Data Integration Tools
Organizations can flexibly deploy data virtualization to meet a range of integration needs for BI and analysis at both project and enterprise scale.  Further,data virtualization is often used in combination with data consolidation and data replication, to provide architects with the widest set of data integration techniques to address each new information challenge.

See For Yourself
If your organization has yet to start using data virtualization, do not worry.  Even though it is the fastest growing segment in the data integration market, actual penetration is less than 25% today.  If you start now, you won't be too far behind the leaders.  But don't wait too long to see for yourself  "What is so great about Data Virtualization?"

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.