What is Data Virtualization?

Committing to a data-driven project can sometimes feel like opening Pandora’s box. To avoid getting lost along the way, you need to have a global, secure, and agnostic view of all your data. Among the avenues to explore, there is data virtualization. 

Companies that embark on the path of a data-driven strategy have a compelling need to rely on real-time data. All processes and thought patterns are oriented around data to make faster and more relevant decisions to bring more value to their customers. However, there is a challenge: the more data you use, the more likely it is to be scattered across different platforms, and therefore, the more you need to reconcile your data sources. The risk? Degrading the effectiveness of your data-driven project. To avoid exposure to this risk, there is a path to explore.

This path involves deploying an advanced data integration methodology, called data virtualization.

What is Data Virtualization?

Data is central to your company’s strategy, making it essential to collect data from different sources. However, this creates the risk of having scattered information. When data is disparate, it’s difficult to get an overview that allows for quick and informed decision-making. If some of your data remains in the shadows, if it doesn’t fit into the scope of your analyses, there is a risk of making mistakes.

The objective of data virtualization is to fight against this scattering by bringing together all your data, regardless of their origin, without moving or copying them. The principle is to create a single virtual zone where all your data assets are available regardless of origin or format. Data virtualization provides a global, unified, organized, and encapsulated view of all your data, whether it comes from the same or different data sources, without having to manipulate or move it. A single “virtual” data layer is created to deliver unified data services.

What are the Benefits of Data Virtualization?

Data virtualization supports multiple applications and users while providing:

• Faster access to data, limiting the latency between data collection and exploitation.
• Significantly reduced time to market for data.
• A decrease in data redundancy.
• Agile decision-making.

The first essential benefit of this approach is to have a global and exhaustive vision of all your data. But data virtualization also has another advantage: you don’t have to move, copy or manipulate data, which remains stored in its original platforms. Because the source data is preserved, it cannot be altered or degraded and therefore retains its original quality. And because it is not manipulated, you save considerable time in making decisions.

How Does Data Virtualization Work?

A data virtualization solution works as a single platform that provides access to all data in a virtual environment. The data can be accessed directly, without being moved from its original source. This not only simplifies data access to the maximum extent but also minimizes the risk of degrading or damaging the data. A data virtualization solution aggregates all structured and unstructured data sources to offer a virtual visualization, all available via a dashboard. It allows you to visualize metadata while eliminating the complexity of reconciling disparate data sources.

To reduce storage costs, optimize data governance, simplify access to data and, ultimately, develop your data-driven culture, why not start data virtualization?

About Actian Corporation

Actian makes data easy. Our data platform simplifies how people connect, manage, and analyze data across cloud, hybrid, and on-premises environments. With decades of experience in data management and analytics, Actian delivers high-performance solutions that empower businesses to make data-driven decisions. Actian is recognized by leading analysts and has received industry awards for performance and innovation. Our teams share proven use cases at conferences (e.g., Strata Data) and contribute to open-source projects. On the Actian blog, we cover topics ranging from real-time data ingestion, data analytics, data governance, data management, data quality, data intelligence to AI-driven analytics.