Amit Sharma, CEO, CData.
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Business today moves faster than ever—and it’s 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 insights in real time. But 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 struggle to keep up without 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 that resides 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 can struggle to harmonize and organize data idiosyncrasies, vocabularies and varying structures across cloud-based applications. These challenges limit use cases and can also 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 integrate 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 the same, and every organization has its own 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:
Accessible
Whether it’s live sales data to inform client meetings or financial numbers for the next quarterly budget, teams should be able to access 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.
Understandable
The average organization maintains hundreds of data sources spread across various locations, platforms and formats. If you don’t manage and simplify data processes, this complex environment can quickly become a quagmire of data structures, schemas and terminologies.
If your marketing team is analyzing customer data to inform next quarter’s strategy, 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.
Shareable
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.
Secure
Sixty percent of corporate data is stored in the cloud, so that's where the majority of data breaches occur. 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 it easier to manage and protect sensitive information.
But it’s not enough to solely rely on the security defenses of tools themselves. 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 many times when other solutions might better serve your needs. Business leaders need to 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. For example, when analyzing up-to-date marketing or sales data for business intelligence efforts. But 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.
To successfully adopt the right solution to integrate and access data, business leaders must consider specific use cases, data volume and query frequency. They should also examine their organization’s unique needs and goals. In many cases, combining both data integration and data virtualization can provide the most comprehensive, efficient and versatile approach to enable access to data across the organization.
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