What is Data Virtualization?

Modern businesses rely on data virtualization to get up-to-date information and improve agility. This essential tool enables businesses to respond quickly to changing market conditions or regulatory requirements. Read on to learn about data virtualization, its key benefits, common use cases, and business challenges.

Definition

Data virtualization is a modern data management approach that allows users to view all enterprise data from multiple sources in a single data-access layer. It comprises data from various sources, such as data warehouses, data lakes, XML documents, cloud data, and more, in a centralized, logical layer. 

Data virtualization enables real-time access to data without physical replication. It abstracts data assets separate from the underlying source systems, allowing users to interact with data as if it were in a single location, regardless of where it is stored. Delivering a unified view of distributed data eliminates the need for complex data movement and reduces latency.

The potential benefits gleaned from this technology are reduced data integration costs, increased efficiency, and improved decision-making. Compared to traditional data integration methods like ETL, data virtualization eliminates time-consuming tasks by using metadata and advanced query optimization to retrieve data directly from its source.

How Data Virtualization Works

At its core, data virtualization connects to a wide variety of data sources, such as cloud applications, big data platforms, etc., and utilizes native connectors, APIs, and drives to extract and deliver data. This data is not replicated or moved; it stays in its original storage location.

Data accessed from disparate sources is integrated into a single virtual data layer. This centralized layer allows users to view and interact with data as if it were consolidated into a single database. This single data-access layer supports real-time queries and allows users to avoid the delay caused by physical replication.  

The underlying technology uses abstraction layers and metadata to standardize data representation in a meaningful way to the business. Logical views are built on top of metadata to simplify complex data and align with business and user needs. Data virtualization allows for real-time query processing and optimization by breaking down queries and distributing them to the appropriate sources. Results are then aggregated and presented in real time. 

The virtual layer also enforces data security and access controls to prevent unauthorized users from viewing or interacting with sensitive information. Additionally, data virtualization can integrate with governance frameworks to remain compliant with HIPAA, GDPR, and other regulations. Data virtualization streamlines accessing and managing enterprise data to enable insights and better decision-making. 

Key Benefits of Data Virtualization

This technology offers a swift, agile, and efficient approach to data management for businesses. The benefits of data virtualization range from instant insights to enhanced decision-making.

Real-Time Data Access

Real-time data access is a key benefit for financial services, healthcare, and retail businesses. Data virtualization allows users to access the most up-to-date data to help make timely decisions. 

• Financial Services: Traders and financial analysts are empowered to monitor and act on market fluctuations nearly instantaneously.  
• Healthcare: Healthcare providers can make informed decisions to support patient care with access to the latest patient records. 
• Retail: Marketing teams can optimize promotions and inventory by analyzing real-time customer behavior. 

Cost Savings

Data virtualization also reduces costs by eliminating the need for physical data replication and storage. With this advanced technology, teams won’t need to maintain redundant storage systems or data warehouses. It can also help organizations save money on labor expenses for traditional ETL processes. Businesses save time and money with this streamlined data architecture.

Increased Agility

In today’s rapidly changing business environments, teams need flexible solutions to meet evolving data needs. Data virtualization enables businesses to integrate new data sources without costly system overhauls, enabling teams to respond with agility to shifting business requirements. 

Enhanced Decision-Making

Data virtualization gives organizations a comprehensive and holistic view of their operations, allowing leaders to make better decisions. Decision-makers can improve accuracy and insight quality by using this unified data approach. Data virtualization helps foster data-driven company cultures where teams collaborate freely. 

Common Use Cases for Data Virtualization

Data virtualization allows businesses to streamline data access and integration processes. It has become a popular data management approach due to its many benefits. Its unified viewing capabilities support a wide range of use cases, including: 

Business Intelligence and Analytics

One primary use of data virtualization is improving BI and analytics. Organizations often store transactional, operational, and historical data in separate systems. Data virtualization pulls data from these multiple sources to provide a unified data layer. BI tools can leverage this single, consistent layer to generate dynamic dashboards and reports, enabling business leaders to make effective decisions for their organizations. 

Data Integration for Mergers and Acquisitions

During a merger or acquisition, a business may be tasked with integrating data from many sources. In these cases, data virtualization can significantly streamline the data integration process. With data virtualization, teams don’t have to worry about replicating and moving data into a centralized system. It allows the data to stay in place while providing a unified view for teams. 

IoT and Real-Time Applications

Data virtualization also enables real-time data access for smart devices and predictive analytics. The rise of the Internet of Things (IoT) has increased the demand for access to real-time data. Smart devices can generate large amounts of data used to make smart decisions. Data virtualization allows for real-time integration of IoT data streams and other data sources. 

Regulatory Compliance

Organizations must remain compliant with data privacy laws to avoid severe non-compliance penalties. Data virtualization offers secure access to data for audits and reporting to authorized parties. A single data-access layer enables centralized data governance, ensuring organizations remain compliant with regulations. Authorized users can access necessary data without needing to replicate it. 

Challenges of Data Virtualization

Data virtualization poses a few considerations for businesses, including performance limitations, security concerns, and data quality. 

Performance Limitations

One drawback of data virtualization is the potential for latency issues when handling large or complex queries. Since data virtualization retrieves data from multiple sources in real time, it can lead to slow response times. Latency issues can make data virtualization less suitable for real-time applications as it may struggle to meet users’ needs when working with extensive databases.

Security Concerns

Another concern with data virtualization is the security of sensitive information. If not managed properly by an organization, data virtualization can pose several security risks. For one, if unauthorized access is granted, sensitive information can be leaked from underlying systems, leading to non-compliance issues and penalties. As a result, an organization should implement robust access controls and encryption protocols to mitigate security risks. Additionally, businesses should ensure their security measures are aligned with the overall data governance framework.  

Governance and Quality

Data virtualization relies on strong governance practices and high-quality data sources to deliver accurate and consistent insights. A business struggling to control decentralized data may take governance and compliance risks. Inconsistent governance practices can lead to unauthorized access and compliance risks in the centralized layer. Data quality issues may also become problematic if the underlying sources are outdated, incomplete, or inconsistent. Poor data quality can undermine the reliability and accuracy of the data virtualization layer. 

To address these challenges, organizations should invest in data quality management, such as cleansing and profiling source data. They should also clearly define roles and responsibilities for users. 

Tools and Technologies Supporting Data Virtualization

Several popular tools support data virtualization. Denodo is a commonly used data management tool that provides this technology, which is focused on agile data management and delivering logical data. 

IBM Data Virtualization is another platform businesses may use to streamline data access. It is a universal query engine that allows users to access data from disparate sources. 

The TIBCO®️ Platform also supports data virtualization for businesses interested in a unified view. This platform offers 200+ pre-built connectors to business suites, databases, and legacy technologies. 

Best Practices for Implementing Data Virtualization

Implementing data virtualization requires an organization to thoughtfully plan how it can be used to meet organizational goals. These best practices help organizations maximize the impact of a data virtualization platform. 

• Start with clear objectives and specific use cases. Organizations should identify how the technology will solve particular problems they face and establish measurable goals, such as reducing query latency or improving time-to-insight. Organizations can build confidence in the platform by starting with specific use cases and setting goals. 

• Invest in robust tools that integrate seamlessly with existing systems. Businesses should also choose the right data virtualization platform to integrate with existing tools. A platform that supports diverse data formats, legacy systems, and emerging technologies helps future-proof the investment. 

• Establish strong data governance policies to maintain quality and compliance. Organizational leaders should define clear policies to ensure the virtualized data is secure, compliant, and held to quality standards. Businesses can consider using governance features such as audit tracking and automated policy enforcement. 

• Train teams to maximize the benefits of data virtualization solutions. The success of implementing data virtualization lies in the team using it. Training programs ensure that employees are equipped and understand the capabilities of this technology. Training should focus on designing queries and leveraging real-time data access for better insights. 

Data Virtualization and the Semantic Layer

A semantic layer can enhance data virtualization for businesses. It bridges the gap between raw data and business users, transforming complex raw data into business-friendly terms. When combined with a data virtualization platform, a semantic layer helps deliver actionable insights from disparate sources.

But semantic layer technology doesn’t stop there — it abstracts data further to translate technical metadata into more business-friendly language. Using both data virtualization and a semantic layer improves data accessibility for non-technical users, provides consistency across reports, and promotes collaboration.

Use cases for a semantic layer and data virtualization include:

• Business Intelligence and Reporting. Organizations can create unified views of enterprise data for reporting purposes. The semantic layer ensures even non-technical users can access and interpret the data. 
• Regulatory Compliance. Data virtualization gives users real-time access to disparate data. The semantic layer ensures consistency among terminology, streamlining compliance reporting for audits. 
• Predictive Analytics. Businesses can enhance predictive analytics with real-time and historical data from data virtualization and simplified data preparation from the semantic layer. 

Using AtScale in Combination with Data Virtualization

The transformative potential of data virtualization in modern organizations is truly remarkable. It improves decision-making, streamlines access to diverse data sources, and drives innovation. AtScale’s universal semantic layer platform can enhance data virtualization and promote data democratization. 

Contact us today to learn how AtScale enables real-time access to actionable insights across an organization. 

Additional Resources: 

• 5 Common Data Challenges & How a Semantic Layer Can Solve Them
• Building a Common Data Language: How a Semantic Layer Drives Collaboration and Trust
• The Semantic Layer and its Role in the Modern Data Stack