What is Data Virtualization? | e-WEEK

The vast amount of data that businesses manage comes in many forms, including structured and unstructured. To be effective, businesses need to be able to view all of their data in one place, in real time. They must also be able to translate the data into actionable insights. Data virtualization is the solution.

Data virtualization is a method of data management which refers to the compilation of all business data in one place, regardless of source. This is completed via a virtual data layer that aggregates all data across disparate systems.

Also see: Understanding Database Virtualization: 5 Key Points

How does data virtualization work?

The data virtualization process is quite simple. Data is accessible in its original form and source. Unlike typical “extract, transform, and load” (ETL) processes, virtualization does not require data to first be moved to a data warehouse or data lake.

Data is aggregated into a single location, called the virtual data layer. Using this layer, businesses can develop simple, holistic, and customizable views (also known as dashboards) to access and make sense of data.

Using these tools, users can also pull real-time reports, manipulate data, and perform advanced data processes such as predictive maintenance. Data is easily accessible via dashboards from anywhere.

The virtualization process is most often achieved using data virtualization software. Many platforms exist, including those from industry leaders such as IBM, Oracle, and TIBCO.

The Benefits of Data Virtualization

The benefits of data virtualization for growing businesses are numerous. Data virtualization enables IT teams to access data in real time, regardless of its source, to improve decision making, increase productivity and reduce overall costs.

Combines structured and unstructured data

Data held in various sources such as relational and non-relational databases will be both structured and unstructured. While structured data is usually numbers and other values, unstructured data is more complex and comes in the form of video files, IoT sensor data, and other examples.

Analyzing unstructured data can be a challenge. However, it is an incredible source of ideas.

The best way forward is to combine structured and unstructured data, a simplified process through data virtualization processes and software.

Eliminates data replication

Businesses need real-time access to up-to-date data at all times. Unfortunately, traditional processes such as ETL require data to be replicated whenever updated data is requested. Data replication is expensive because it requires ever-increasing levels of storage. It also leads to duplicate and incorrect data which can skew data sets.

Data virtualization does not require replication. Instead, the data is persisted in its original source while visualized in a virtual layer. This means that virtualization can produce higher quality data faster and at lower cost.

Improves decision making

Although data is essential to the decision-making process, not all data will be enough. The data used must be accurate, up-to-date and logical. It should also be displayed in a way that all stakeholders can understand, whether it’s a data scientist or a senior executive.

Data virtualization allows stakeholders to access the specific data they need when they need it. Because the data is not simply a replica of any given moment, all data is accurate to the minute. This translates to complete enterprise visibility that allows stakeholders to pivot quickly with confidence.

When integrated with data visualization tools, virtualization software allows users to see real-time data in an easy-to-understand form. For example, data can be displayed in tables or graphs.

See also: Best Data Visualization Tools in 2022

Improves productivity

Data virtualization improves productivity in several ways. First, virtualization improves access to data. Users do not need to access multiple applications or serversall data is collected in a single resource. Users can walk in, enter the data they need, and get back to work.

Data virtualization also simplifies the data analysis process. Virtualization software offers an easy-to-use interface that even those unfamiliar with data analysis can use. Stakeholders can quickly access the data they need to make fast business decisions. Additionally, these self-service features will reduce the workload on IT and data teams.

The increased productivity resulting from data virtualization allows businesses to scale quickly. Processes such as product development can also become more efficient. As a result, companies gain a competitive advantage within their industries.

Reduces infrastructure costs

Data virtualization requires fewer resources than traditional data integration methods. For example, by eliminating replication, companies can reduce the amount of data storage they have to pay for. Virtualization also reduces the number of data sources to manage, saving teams time and effort.

Simplifies data security and governance

Data security and governance policies ensure that data is protected and used appropriately. Unfortunately, as businesses grow, data and its sources become increasingly complex. Security and governance quickly become unmanageable.

Data virtualization simplifies data governance by providing a single source of truth. All data sources are integrated into one, allowing IT teams to enforce centralized data security and governance policies. Additionally, data virtualization platforms include features such as access control and the ability to integrate with other data security tools.

How is data virtualization used today?

The increase in data volume and the need for real-time access is driving strong growth in the data virtualization market. Recent data shows that the global market is expected to reach $22.2 billion by 2031, growing at a CAGR of 21.7%.

Many businesses across all industries are using data virtualization to improve data access, reduce costs, and increase productivity.

  • In retail, data virtualization allows companies to view everything from inventory levels to customer behavior in stores in one centralized location.
  • In manufacturing, data virtualization allows users to quickly identify areas for production improvement, resulting in improved manufacturing efficiency.
  • In healthcare, data virtualization enables stakeholders to access real-time patient data across all sources to deliver the highest quality care.
  • In telecommunications, data virtualization allows companies to use accessible data to quickly resolve support tickets and improve customer experience.

The challenges of data virtualization

While incredibly beneficial across industries, data virtualization comes with its own set of challenges. For example, like any other digital transformation project, an initial investment is necessary. Companies need to invest in data virtualization software, staff training, and other components.

Besides the investment, other challenges exist:

  • Virtualization is complex: Modernizing a data infrastructure is not easy. And data virtualization isn’t as simple as buying and implementing software. In many cases, data virtualization tools must be implemented in conjunction with other tools such as ETL. The virtualization process can quickly become complex.
  • Specific expertise is required: Data virtualization and its future management require specific expertise that some companies may not currently have access to. Companies may need to hire professionals specifically for virtualization or seek third-party support, which can increase costs.
  • Data recovery depends on source availability: Because data is collected directly from the source in real time, data retrieval requires the source to be operational when a query is made. If the source (such as a database or software) experiences downtime, data recovery will not occur.

Data Virtualization: Related Terms

The topic of data virtualization encompasses a wide range of terms, some of which are used interchangeably. To gain a solid understanding of virtualization and data management, it is important to be clear about these terms. Here are some common data-specific terms businesses should be aware of:

  • Data management: Data management is the process of collecting, storing and managing data. Data virtualization is a process included in the practice of data management.
  • Data consolidation: Data consolidation is the process of combining data from various sources into one. For example, all data can be consolidated into a data warehouse or a lake. While data consolidation is similar to virtualization, consolidation lacks the virtual data layer that allows real-time data access.
  • Data storage : A data warehouse is a large collection of data gathered from various sources. Data inside the warehouse is structured and processed, ready for analysis.
  • Data Lake: A data lake is a simple repository of data. Unlike the data warehouse, the data inside the data lake is unstructured and raw.

See also: Best Data Analysis Tools and Software for 2022

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