Data Virtualization vs Data Visualization: 3 Key Differences & Benefits

The role of data analysis is pivotal for businesses that want to remain competitive in their markets. Businesses work hard to keep pace with and harness growing amounts of data to gain competitive advantages, make informed decisions, and enhance operational efficiency. Amid this growing reliance on data, the proliferation of data technologies has led to some confusion between terms that sound similar—in particular, the terms "data virtualization" and "data visualization." This article clarifies the crucial differences between these technologies to help business leaders and data professionals understand the unique roles and applications of these tools in the realm of data management and analysis. 

What is data virtualization? 

Data virtualization technology enables users to access and manipulate data from multiple sources without the need to know where that data is stored or to move it to a central location. Data virtualization tools like CData Virtuality provide a unified view of data in real-time—that is, data from multiple sources is presented as if it were from a single, cohesive source. This unified view enables faster and more flexible data integration and analysis. 

What is data visualization? 

In contrast with data virtualization, data visualization is a process in which data is presented in graphical or visual formats (for example, charts, graphs, maps, and infographics). Tools like Microsoft Power BI and Tableau transform raw data into visual elements, making it easier to spot relationships, compare metrics, and communicate findings effectively. Such visualization helps you to understand complex data patterns, trends, and insights more easily by providing visual context that can make the information more accessible and interpretable.  

What are 3 key differences between data virtualization and data visualization? 

As you can see, data virtualization and data visualization cater to distinct aspects of data handling. Understanding the key differences between these two concepts can help you use them more effectively to meet your data-strategy goals. This section explores three key areas where these technologies differ: Focus, benefits, and use cases. 

Focus 

Data virtualization focuses on integration while data visualization focuses on presentation. Data virtualization centers on data integration and management, allowing users to query data across various platforms as if it were from a single source. Data visualization centers on the presentation of the data, emphasizing clarity, aesthetics, and usability in graphical formats (charts, graphs, and infographics). 

Benefits 

Data virtualization and data visualization offer different yet complementary benefits that enhance data accessibility, improve analytical capabilities, and streamline reporting processes. The synergy of these technologies provides seamless access to diverse data sources while transforming complex data into clear, actionable insights. 

Data virtualization offers the following important benefits, among others:  

• Data accessibility: Unified data access is the key to data virtualization's power. Data virtualization enables you to access data from multiple sources—such as databases, cloud services, and APIs—through a single interface, which eliminates the need to navigate different systems. Data virtualization tools also provide a virtual layer, which avoids the need for data duplication. This feature reduces storage costs and ensures consistency across datasets. In addition, such tools often include built-in security features, ensuring that users have access only to the data they need while maintaining compliance and governance.
• Data discovery: In addition to all its data-accessibility features, data virtualization provides enhanced query capabilities, facilitates experimentation, and reduces data silos to aid you in data discovery. Data virtualization allows you to run complex queries across various datasets, making it easier to uncover hidden insights and relationships. You can also experiment by testing different queries and scenarios without affecting the underlying data. In addition, by integrating data from diverse sources, data virtualization breaks down silos, which enables you to see the bigger picture and discover insights that might not be apparent in isolated datasets.
• Improved data quality: By consolidating data from multiple sources, data virtualization ensures consistency and accuracy. This unified view (a single source of truth) also reduces confusion and helps users rely on a consistent dataset for analysis. Data virtualization also can incorporate data-validation rules during the querying process, which ensures that only high-quality data is accessed and used. In addition, centralized access provides better oversight of data quality issues, simplifying the identification and rectification of errors. 
• Optimized resources: By using a virtual layer for data access, data virtualization eliminates the need to replicate data across multiple systems. It also allows organizations to manage their resources more without all the overhead that is associated with maintaining multiple physical copies of data.  

This technology also allows for dynamic allocation of resources, which enables organizations to adapt quickly to changing data needs, without significant reconfiguration. Data virtualization also optimizes human resources by making data more accessible so teams can work more collaboratively and efficiently. In addition, it enables organizations to scale their data environments effortlessly, without requiring major hardware investments.  

Data visualization offers many advantages, as well, that help raise your understanding of data to a higher level. Here are just a few of those advantages: 

• Data accessibility and understanding: Data visualization enhances data accessibility by breaking down complex datasets into easily digestible formats. This feature makes it easier for you to grasp key insights without becoming overwhelmed by data fatigue. Many visualization tools also offer real-time data updates, allowing you to access the most current information and make timely decisions.
• Data exploration: Interactive elements like filtering, zooming, and drill-down capabilities enable you to explore data actively, fostering a more engaging and dynamic experience. Filtering, zooming, and drilling down into specific data points provides the deeper access that you need to find relevant information.
• Data insights and decision making: Visual tools allow for rapid assessment of data, reducing the time it takes to analyze and interpret information compared to raw data. By presenting data visually, complex information becomes easier to comprehend, allowing decision-makers to grasp key insights quickly. Visual representations also effectively communicate findings to stakeholders, fostering a shared understanding and facilitating collaborative decision making.
• User experience: Data visualization tools enhance the overall user experience in several ways. Well-designed dashboards and visualizations provide a user-friendly interface that guides you through the data, making navigation intuitive. As a result, you can understand and interact with complex datasets without needing extensive technical knowledge. Visually appealing designs enhance the overall experience, making data exploration enjoyable and encouraging users to interact with the information. In addition, effective visualizations can convey a narrative, guiding you through the data in a way that makes insights more memorable and impactful. 

Use cases 

Having explored the distinct focuses and benefits of data virtualization and data visualization, it is essential to understand how these technologies are applied in real-world scenarios. The following sections delve into common use cases for each, illustrating how they can empower your organization to enhance decision-making and optimize performance. 

Some common use cases for data virtualization include:

• Data analytics: Analysts in areas as diverse as BI, healthcare, and customer experience use data virtualization to query and analyze data from various sources (for example, CRM, ERP, and social media) in real-time, enabling comprehensive insights without moving data. Organizations from many other areas (for example, finance, retail, and supply chain) also use live data feeds to perform predictive modeling, improving accuracy and responsiveness to market changes.
• Reporting: Data virtualization enables analysts, data scientists, and IT managers to design and use dashboards that contain combined views of key metrics and performance indicators that their organizations can use for reporting purposes. Then, users can generate real-time reports and interact with those reports by drilling down into specific data points for deeper analysis.
• Business intelligence: Data virtualization enables marketing, telecommunications, and government organizations to obtain comprehensive analyses of key performance indicators. The data intelligence derived from these analyses allow such organizations to make informed strategic decisions that align with their overall business objectives. 

Some common use cases for data visualization include:

• Sales dashboards: Sales organizations like retail stores and e-commerce companies employ data visualization for performance tracking, sales pipeline management, and geographical sales analysis. By using this data, such businesses can quickly identify areas that exceed expectations or areas that require attention. They also use visual dashboards to track the status of leads and opportunities through various stages of the sales funnel and to identify geographical trends by displaying sales data by region or territory.
• Marketing reports: C-suite executives use marketing performance dashboards to make strategic decisions based on campaign, demand generation, SEO, advertising, and social media insights. Equipped with this information, executives can quickly assess the effectiveness and optimization of their marketing strategies. 
• Customer insights: Brand managers, sales teams, and customer experience managers use visual tools to monitor brand perception, customer behavior, and customer pain points. Visual data delivers important sentiment analysis and feedback trends from surveys and reviews, which provides these teams with actionable insights to enhance product and service offerings. 

CData Virtuality: Data virtualization built for the enterprise

CData Virtuality offers comprehensive data streaming support, enabling seamless virtualized connectivity to over 200 enterprise data sources, including popular streaming services like Amazon Kinesis, Apache Kafka, and Google Cloud Dataflow. CData Virtuality handles the complexities of data connectivity and integration so you don’t have to, ensuring your data streams are always accessible and actionable.