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Tuesday, July 16, 2024

What Companies Should Know About Data Virtualization for the Cloud

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What are the challenges and opportunities as businesses navigate the ever-accelerating world of data-driven decision-making? Learn about the key characteristics of effective cloud data virtualization and how companies can optimize their data operations for success in a rapidly evolving landscape.

Today’s Business moves faster than ever and is driven by massive amounts of data to inform, enhance, and accelerate decisions that can make or break a company.

Data virtualization has played a pivotal role in helping organizations integrate data from various sources, centralize security and governance, and deliver real-time insights. However, data virtualization wasn’t traditionally designed for the cloud-native landscape, and its limitations are starting to show.

As cloud-based data grows in size, scale, and complexity, businesses may need tools and processes built specifically for the cloud, which includes data virtualization.

Why traditional data virtualization struggles in the cloud

Data virtualization provides a single access layer for all enterprise data, enabling organizations to integrate and present data in disparate systems.

Critically, data virtualization keeps data in its original location, enabling users to transform data into a unified view without moving or duplicating it. Likewise, it allows users to access and query live data from multiple sources without extensive data preparation, integration efforts, or additional storage.

But the problem is traditional data virtualization solutions were built to handle on-premises systems, which are tightly coupled to databases, making it challenging to integrate, access, and use data from cloud services, APIs, or SaaS applications like Salesforce, Marketo, and NetSuite.

Traditional data virtualization excels in connecting databases but needs help harmonizing and organizing data idiosyncrasies, vocabularies, and varying structures across cloud-based applications. These challenges limit use cases and can increase complexity and cost because organizations must pour time and resources into integration efforts.

That can present a major roadblock, given that managing cloud spending is the top challenge for 82% of organizations, according to a Flexera survey. Consider an e-commerce company struggling to integrate data from various cloud-based shopping platforms and marketplaces into a coherent analytics system or a healthcare provider that can’t incorporate patient data from its legacy on-premises Electronic Health Record (EHR) system to a cloud-based telemedicine platform.

Four aspects of cloud data virtualization

Data virtualization for the cloud allows organizations to simplify, streamline, and safeguard data operations. However, not all cloud-native tools are built similarly, and every organization has unique needs and goals. No matter the tool, there are four characteristics of effective data virtualization for the cloud that companies should ask about when speaking to cloud data virtualization vendors:


Whether it’s live sales data to inform client meetings or financial numbers for the next quarterly budget, teams should be able to access the data needed at any time and from any application. Whatever solution works best, organizations should strive for the greatest depth and breadth of data connectivity, empowering teams to gain valuable information and actionable insights no matter where data lives.


The organization maintains hundreds of data sources across various locations, platforms, and formats. Without managing and simplifying data processes, this complex environment can quickly become a quagmire of data structures, schemas, and terminologies.

Suppose your marketing team analyzes customer data to inform next quarter’s strategy. In that case, they first need to find their data in a consistent format before they can draw meaningful insights and make informed decisions. So, data virtualization tools should provide lightweight, user-friendly interfaces that are easily understood without requiring heavy IT involvement.


Sharing data is often cumbersome—eight in 10 business leaders say they need to prioritize reducing data and information silos, according to Airtable research. The end goal of data virtualization for the cloud should be to make data discoverable and shareable with out-of-the-box connectivity to popular data analytics tools and interfaces like Power BI, Tableau, and Google Analytics.


Sixty percent of corporate data is stored in the cloud, so most data breaches occur there. As organizations increasingly rely on cloud-based technologies, data security is even more crucial. The most effective data tools centralize and standardize data security, making managing and protecting sensitive information easier.

Relying solely on tools’ security defenses is not enough. To avoid possible security risks (such as unauthorized access, data leakage, or breaches), you should also implement robust access controls, encryption, and security monitoring solutions for every aspect of data operations.

When data virtualization is the right option—and when it’s not

While data virtualization may be an effective approach in many scenarios, there are times when other solutions might better serve your needs. Business leaders must apply the right tool for the right job—or they’ll face more challenges than solutions.

Data virtualization works best when users need access to live data and diverse data sources without extensive data preparation, such as when analyzing up-to-date marketing or sales data for business intelligence efforts. However, it’s less effective when users need to move and manipulate data or when dealing with massive volumes of data.

In these cases, organizations might instead consider data integration solutions such as ETL pipelines, which extract data from multiple sources, transform it to fit a specific schema or model and load it into a target system, such as a data warehouse. This method can be particularly useful when dealing with Big Data, historical analysis, and large datasets that would be difficult to query directly without performance and latency issues.

Also Read: Explained: TinyLlama – The Promising Generation of Powerful Smaller Language Models

Business leaders must consider specific use cases, data volume, and query frequency to adopt the right solution to integrate and access data successfully. They should also examine their organization’s unique needs and goals. In many cases, combining data integration and virtualization can provide the most comprehensive, efficient, and versatile approach to enabling access to data across the organization.

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