It is estimated by experts that data virtualization is poised to grow in terms of its importance with massive generation of data is extremely important for the organizations. Importance of data virtualization is attributed to the need for extracting maximum possible benefits from the available data that is in structured as well as un-structured forms. 

By adopting data virtualization processes businesses are able to abstract data from the applications to position the data in middleware. One more advantage of data virtualization is it provides uniform view of data that is gathered from disparate sources.
 
It offers the data to business intelligence teams exactly in the format required by them. However, data virtualization by putting the data in middleware is a complex process and is found to be a highly challenging task for internal IT departments.
 
Thankfully, some of the big firms including Microsoft, IBM, Red Hat, and Oracle are making impressive progress in this direction to design high performance tools for data virtualization.

Understanding data virtualization

Every organization needs access to market related data that can provide valuable insights about changing consumer preferences and market trends. This can eventually help organizations take important and well informed business decisions for assured business growth. 

However growth of different communication technologies, proliferation of Internet of Things and sheer volume of data that is generated every single moment from diverse sources make it difficult for organizations to derive any meaning from this sort of data. Obviously, one needs to integrate the available data before putting it to any use. Challenges of data management can be listed as follows:

• Thanks to the concept of Big Data, there has been an exponential growth in data generation from different sources
• Organizations have to exhaust their vital resources for managing unstructured and structured data
• Incompatibility of database management systems in different organizations 
• In view of regulations that force organizations to retain data, there has been an unprecedented rise in volumes of data being stored within the organizations
• Companies need to have a unified view of data from diverse sources and equally varied formats to enable them to formulate meaningful decisions

The need to extract high quality data from ever growing volumes of data is increasingly felt by organizations. The only possible solution to this can be found in data integration and data virtualization. 

Since data virtualization effectively separates data from various applications to place it in the middleware we can expect an effective solution to the mammoth problem of data integration. By positioning data in the middleware it is possible to reduce its dependency on Database Management Systems.

Data virtualization tools map data to actual location instead of actually placing the data in the middleware. With development of stronger and more efficient data virtualization tools, we can expect greater capability of providing a more unified view of data. 

A real life application of data virtualization

It was observed that stakeholders from different departments wanted to access data residing in multiple applications and these requests for data integration were handled by the process called as Extract Transform and Load or ETL. The issues were the slow speed of ET process and incompatibility of different applications that hosted data.

In addition to this, it was not possible to add new applications or data sources. This caused lack of unified view of data and there was great strain on the system leading to retarded processes and cost escalations. 

This problem was addressed by introduction of a data virtualization tool that could resolve the issue by storing the data in the cache or middleware instead of actual sources of data. This accelerated data integration and hastened fulfillment of data requests.The data availability was not affected due to unexpected events including server crashes since the data could be accessed from the memory. 

Thanks to the data virtualization tool, it was also possible to add multiple data sources as well as tools for business intelligence. Stakeholders were able to access unified view of data from disparate sources. 

Consequences of growth of data virtualization

It is postulated that with growing importance of data virtualization there will be mitigation of ETL process to a large extent. There are large organizations to support this view who have already implemented data virtualization by abandoning ETL processes. These organizations believe that data virtualization is an ideal solution for handling huge volumes of unstructured data from diverse sources.
 
Although, data virtualization is growing by leaps and bounds with development of more efficient data virtualization tools and unstoppable growth of data generation, the ETW processes will not immediately fade to oblivion. The process or crating maps of data from different applications and placing these in middleware is a technically challenging task for IT personnel. There is an urgent need to acknowledge and address these challenges as early as possible. 

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