The vast amount of data that companies manage comes in many forms, including structured and unstructured. To be effective, enterprises must be able to view all of their data in one place, in real time. They must also be able to translate the data to glean actionable insights from it. Data virtualization is the solution.
Data virtualization is a method of data management that refers to the compiling of all enterprise data in one place, regardless of its source. This is completed via a virtual data layer that aggregates all data across disparate systems.
How Does Data Virtualization Work?
The process of data virtualization is quite simple. Data is accessed in its original form and source. Unlike typical “extract, transform, and load” (ETL) processes, virtualization doesn’t require data to be moved to a data warehouse or data lake first.
Data is aggregated in a single location, known as a virtual data layer. Using this layer, enterprises can develop simple, holistic, and customizable views (also known as dashboards) for accessing and making 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 commonly completed 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 enterprises are many. Data virtualization enables IT teams to gain access to real-time data regardless of source to improve decision-making, increase productivity, and reduce overall costs.
Combines Structured and Unstructured Data
The data held in various sources such as relational and non-relational databases will be both structured and unstructured. While structured data is typically 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’s an incredible source of insights.
The best way forward is to combine structured and unstructured data, a process that is made simple through data virtualization processes and software.
Eliminates Data Replication
Enterprises require 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 as it requires ever-growing levels of storage. It also results in duplicate and incorrect data that can skew datasets.
Data virtualization doesn’t require replication. Instead, data is kept in its original source yet viewed in a virtual layer. This means virtualization can result in higher-quality data faster and at a lower cost.
While data is critical to the decision-making process, not just any data will do. The data used must be accurate, up-to-date, and logical. It must also be displayed in a way that all stakeholders can understand, whether a user is a data scientist or a C-level executive.
Data virtualization enables stakeholders to access the specific data they need when they need it. Because data isn’t just a replication from any given time, all data is accurate to the minute. This results in full business visibility that enables 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 charts or graphs.
See also: Best Data Visualization Tools in 2022
Data virtualization enhances productivity in many ways. First, virtualization improves data access. There’s no need for users to access multiple applications or servers—all data is collected into a single resource. Users can get in, grab 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 quick business decisions. Plus, these self-service capabilities will reduce the workload for IT and data teams.
The enhanced productivity resulting from data virtualization enables enterprises to move quickly. Processes such as product development can also become more efficient. As a result, enterprises gain a competitive advantage within their industries.
Reduces Infrastructure Costs
Data virtualization requires fewer resources than traditional data integration methods. For example, due to the elimination of replication, enterprises can reduce the amount of data storage they must pay for. Virtualization also results in fewer data sources to manage, which saves teams time and effort.
Simplifies Data Security and Governance
Data security and governance strategies ensure data is protected and used appropriately. Unfortunately, as enterprises grow, data and its sources become increasingly complex. Security and governance quickly become impossible to manage.
Data virtualization simplifies data governance by delivering a single source of truth. All data sources are integrated into one, enabling IT teams to enforce centralized data security and governance policies. In addition, 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 are resulting in serious growth within 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 enterprises across industries are utilizing data virtualization to improve data access, reduce costs, and boost productivity.
- In retail, data virtualization enables enterprises to view everything from inventory levels to customer behavior across stores in one centralized location.
- In manufacturing, data virtualization enables users to quickly pinpoint areas of improvement in production, resulting in improved manufacturing yield.
- In healthcare, data virtualization enables stakeholders to access real-time patient data across sources to provide the highest quality care.
- In telecommunications, data virtualization enables enterprises to use accessible data to quickly resolve support tickets and improve the customer experience.
The Challenges of Data Virtualization
While it’s incredibly beneficial across industries, data virtualization does come with its own set of challenges. For example, just like any other digital transformation project, an upfront investment is required. Enterprises must invest in data virtualization software, staff training, and other components.
In addition to the investment, other challenges exist:
- Virtualization is complex: Modernizing a data infrastructure isn’t easy. And data virtualization isn’t as simple as purchasing 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 get complex.
- Specific expertise is required: The virtualization of the data and its future management requires specific expertise that some enterprises may not have current access to. Companies may need to hire professionals specifically for virtualization or look for third-party assistance, which can drive up costs.
- Data retrieval depends on source uptime: As data is gathered directly from the source in real time, data retrieval requires the source to be up and running when a query is made. If the source (such as a database or software) is experiencing downtime, data retrieval won’t occur.
Data Virtualization: Related Terms
The topic of data virtualization includes a wide range of terms, some of which are used interchangeably. To gain a solid understanding of data virtualization and management, it’s important to be clear on these terms. Some common, data-specific terms enterprises must be familiar with include:
- Data management: Data management is the process of collecting, storing, and managing data. Data virtualization is a process included within the practice of data management.
- Data consolidation: Data consolidation is the process of combining data from various sources into one. For example, all data may be consolidated into a data warehouse or lake. While data consolidation is similar to virtualization, consolidation is missing the virtual data layer that enables access to real-time data.
- Data warehouse: A data warehouse is a large set of data gathered from various sources. Data inside the warehouse is structured and processed, ready for analytics.
- Data lake: A data lake is a simple repository of data. Unlike the data warehouse, data inside the data lake is unstructured and raw.