Data Virtualization Techniques for Business: A Short Guide 

Data virtualization provides organizations with instant business decisions and the power to bring value to customers faster. Such precious data lives all over the place: in the cloud, on social media platforms, on operation systems, on websites, etc. Not to mention the additional sources that are constantly being added. 

With the data virtualization example enterprises can speed up access to the data and better understand the insights. In this guide, you’ll learn more about, how this approach works, its use cases, benefits, and more. 

What Is Data Virtualization? 

In the Data Management Book of Knowledge, it’s mentioned that data virtualization enables distributed databases to be accessed and viewed as one database. This is a technique that consolidates data from multiple sources into a comprehensive, logical view without moving it 

physically. In other words, data remains in native sources while users can access and analyze it through a virtual space. 

How Data Virtualization Works 

Briefly, data virtualization works as a middleware that is nothing more than a virtual data access layer that is built based on many data sources. This data virtualization layer presents the layer as a single virtual view, regardless of its type and model. 

Data access is instant, on-demand, and in real-time. As a result, more than half of businesses worldwide use virtualization technologies such as network function virtualization, SD-WAN, or desktop virtualization or have it on their future agenda. 

Data virtualizations architecture consists of three building blocks: 

Connection layer (a fixture of connectors to data sources in real-time). This layer is responsible for accessing data that is scattered across multiple source systems ● Abstraction layer (a service that presents, manages, and creates a logical view of data). This is the cornerstone of the whole process, a so-called bridge between all data sources and all business users 

Consumption layer (a range of tools and applications that request abstract data). This layer provides a single point of access to data kept in the underlying sources

The most common data virtualization use cases 

Data virtualization has many uses, since its a layer of data access between data sources and consumers. This technique is used by many companies and entrepreneurs, 10% of the IT software budget is allocated to virtualization. Some of the most common use cases include: 

Data integration 

This is the most likely use case to encounter. Most if not every company has data from many different data sources, which means they need to bridge data between old sources and new digital systems like social media. Data virtualization makes the connections and then you can search for needed data within the catalog. 

Logical data warehouses 

The logical data warehouse has similar functions to a traditional data warehouse. But does have some exceptions, no data is stored in an actual warehouse. But you can access whatever data you need including the components of a traditional data warehouse, all on a single platform. 

Big Data and predictive analytics 

The nature of data virtualization works great in this area because big data and predictive analytics are built on diverse data sources. Big data comes from many sources, like cell phones, social media, email, and many highly diverse methodologies. 

Operational uses 

A great inconvenience that call centers and customer services experienced for many years is siloed data. For example, a bank would need a different call center for credit cards and another one for home loans. With data virtualization, everyone from a call center can see the entire span of data stores from a single point of access. 

Abstraction and decoupling 

This is the opposite side of the features discussed above. Possibly there are data sources you want to isolate, either they come from a questionable source, have unique privacy rules, or have other complex regulations. Data virtualization lets you isolate a specific data source from particular users which should not have access to that data. 

Main advantages of data virtualization 

You might already know about the benefits of the data virtualization technique, but do you know what incredible advantages it can generate for your business? In the next section find out more about the benefits and advantages.

Faster and more accurate analytics 

Data virtualization enables remote access to any data type, model, or source. Correspondingly to real-time access you can run your processes ultra-quickly. Data virtualization doesn’t require any knowledge, you don’t need any coding or technical skills. This allows you to focus on the task at hand: analytics and decision-making processes. 

Enhanced simplicity and flexibility 

The systemization that data virtualization provides translates into higher simplicity and flexibility for entrepreneurs that are seeking access to modify data at the touch of a button. Interfaces that data virtualization uses are generally friendly and intuitive, supplying easy usability to non-technical teams. 


Any business or entrepreneur out there is constantly seeking ways to minimize their spending yet boost productivity. This is the exact environment that data virtualization can help you achieve. 

With data virtualization techniques businesses don’t ever need to worry about purchasing extra tools or adding any physical components to their infrastructure. This, in turn, leads to saving money. 

Summing up 

In conclusion, data virtualization is the perfect service available for businesses that don’t have the capacity for data storage, want to save money, and are seeking quick access to data sources. Therefore, with the valuable features and business advantages, it’s likely that companies will continue to use data virtualization techniques.