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Data virtualization is a series of data management approaches that allow applications to manipulate data without any awareness of the data’s technical details, including formatting or location. By creating a single representation of data from many sources, data virtualization makes it possible to work with that data without having to move or duplicate it.
Rather than replicating data, data virtualization makes it accessible through a middle layer dashboard or tool that masks the technical complexities but allows metadata through. This helps businesses that rely on real-time remote access to data maintain the integrity of that data at a fraction of the cost. New data virtualization tools have made this technology available to more businesses—here’s what you need to know about how it can benefit your enterprise.
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Featured Partners: Data Visualization Software
Four Key Benefits of Data Virtualization
In addition to boosting productivity and cost efficiency, data virtualization can improve your business’s agility, give you greater scalability, and provide better access to data from disparate sources and locations.
Increased Business Agility
Being able to store, manage, and access data means businesses can more reliably use it to guide decisions on market changes and customer demands. Data can give you real-time insights into product performance and consumer behavior along with confidence that the data represents the entirety of your organization rather than a small or outdated part.
Similarly, switching to a cloud-based data structure reduces the reliance on physical data movement and the need for extensive extract, transform, load (ETL) processes. This also lightens the IT load businesses usually have to carry when they need to make a drastic change on short notice to keep up with industry developments.
Better Scalability and Flexibility
Data virtualization can increase your company’s scalability and elasticity by creating a single-access layer. This approach allows you to scale resources up or down based on real-time needs, ensuring optimal performance and cost efficiency. Immediate resource allocation and enhanced capacity for data-handling also let businesses scale operations based on demand more easily without uprooting the entire infrastructure.
Dynamic resource allocation gives added elasticity by keeping data readily accessible on the cloud. Elasticity ensures that systems can evolve and grow along with business, supporting future technological development without the need for data migration.
The ability to quickly integrate new data sources as they become relevant lets businesses stay ahead in fast-paced market conditions without extensive IT overhead or administrative delays. This not only supports current operational needs but also positions businesses for future growth and innovation in the increasingly data-driven global marketplace.
Real-Time Global Data Access
Data virtualization helps eliminate data silos that can hinder effective collaboration across large enterprises. Centralizing data storage to improve accessibility allows for real-time queries that aren’t resource-exhaustive and return more reliably up-to-date results.
Specialized data virtualization tools also typically offer real-time data management and aggregation capabilities that are both format- and origin-agnostic. This simplifies data access and reduces the need for complex data integration processes when handling large amounts of data from various sources and in diverse formats.
Cloud environments, in particular, benefit from data virtualization technology when their resources are being accessed from multiple geographical locations, like in remote work scenarios. It’s important to ensure that all parties access the same data regardless of their location, with their changes visible in real-time to avoid task redundancy. This guarantees that all employees are working with current data.
Cost Reductions
Data virtualization offers significant cost benefits by streamlining data management processes and allowing multiple virtual machines to share the same physical server. At the same time, remote workers and small offices can rely on the company’s centralized computing power without needing to install and manage their own hardware in-house, eliminating the need for more hardware maintenance and administration.
The reduction in expenses isn’t limited to hardware—operations and processes also benefit from a virtual approach to data management and storage. By providing real-time or near-real-time access to data across disparate systems, data virtualization minimizes the need for data warehousing infrastructure and the labor-intensive processes typically required to prepare data for analysis. It also cuts down on the costs and time involved in complex ETL processes necessary for traditional data management.
5 Critical Capabilities of Data Virtualization Software
Data virtualization software acts as middleware by presenting a unified, logical view of the data from multiple sources, ensuring seamless integration, consistent data management, and security. While the functionalities and features can differ among vendor solutions, several capabilities are essential to data virtualization.
Smart Query Acceleration
The highlight of data virtualization is its ability to facilitate data access. Smart query acceleration combines multi-source query optimization with parallel processing and artificial intelligence (AI) to speed up search requests. The following are key components of smart queries:
- Multi-Source Query Optimizer: Optimizes queries across various sources by tailoring execution plans to the data’s location and characteristics, minimizing retrieval times and resource use.
- Next-Generation MPP Engine: Employs massively parallel processing (MPP) to distribute tasks across multiple resources, handling large data volumes and complex queries efficiently, ensuring high throughput and fast responses.
- AI-Powered Query Acceleration: Uses AI to dynamically optimize query paths and strategies, learning from patterns to predict and enhance data access routes, reducing latency and boosting efficiency.
Smart query acceleration enhances the performance of data virtualization tools to make them more agile and capable of meeting the demands of modern data-driven enterprises.
Advanced Semantics
Data virtualization tools can handle progressively larger amounts of data thanks primarily to advanced semantics, which lets the software sort data into catalogs that simplify and speed up the data discovery process. More commonly, many solutions use AI-driven recommendations to improve the accuracy of data queries by analyzing usage patterns and user interactions to determine the most desired search outcome.
Semantic-powered queries allow teams to collaborate more seamlessly by adapting to the specific requirements of different users and scenarios. This adaptability is beneficial for businesses like social media and financial firms that deal with rapidly changing datasets.
Universal Connectivity and Data Services
Universal connectivity ensures seamless, reliable integration of multiple data sources via standard interfaces, which include the following:
- Structured Query Language (SQL): Enables complex queries and data manipulation across traditional relational databases.
- JavaScript Object Notation (JSON): Facilitates data interchange between servers and web applications, making it ideal for web integration.
- Representational State Transfer (REST): Supports web services that are easy to integrate with mobile and web applications.
- GraphQL: Allows clients to request only the data they need, making it highly efficient for complex systems with many user types and data structures.
Universal connectivity also works the other way around, allowing businesses to publish data easily via the same interfaces and sharing between different departments and external partners. This capability not only promotes transparency but also accelerates the decision-making process, as data is readily available and easily consumable.
Flexible Data Integration
Having data collection and accessibility on their own layers simplifies integration without the need to rely on complex ETL processes across various platforms and systems. By merging traditional data warehousing with virtual machines, data virtualization tools can support a wider range of use cases for real-data collection, management, and processing.
Standardized frameworks also help businesses document data integration processes through data catalogs and metadata management. This guarantees data validity, accuracy, and completeness even after it’s been transferred and modified to work on a new system.
Unified Security and Governance
Having all your data in the same location lets you implement unified security and governance practices, ensuring consistent results for all data, regardless of its origin or access method. This centralized framework makes compliance with data protection legislation easier through comprehensive audit logs and constant monitoring of access activity.
Data virtualization removes the burden of data security and governance from smaller branches and offices with limited resources, and centralization reduces the potential avenues of attacks thanks to advanced encryption and secure transfer protocols.
5 Key Impacts of Data Virtualization on Businesses
Data virtualization technology can influence how businesses operate and expand and help lower the bar of entry for many startups and small businesses. A centralized, smart approach to data collection and management streamlines internal operations and safeguards data integrity and compliance across the enterprise, which can have a number of impacts across the business.
Profit Growth
Through streamlining data management, cutting down on costs, and enhancing the decision-making process, data virtualization technology can positively impact revenue. It can also help organizations achieve operational efficiency, which can reduce their products and services’ time to market and user satisfaction, contributing to profit growth.
Technology Optimization
Data virtualization can significantly enhance the functionality of other tools and technologies that rely on a consistent flow of data. By streamlining the management and utilization of data across various platforms, data virtualization practices can facilitate tech and resource optimization enterprise-wide in the following ways:
- Technology Overhead Reduction: By abstracting the physical layer, data virtualization reduces the need for traditional data management for hardware and software. This minimizes IT overhead, freeing up staff and resources for more strategic tasks.
- Enhanced Data Processing: By optimizing data retrieval, you can enable faster processing and quicker access to queries and insights. This speed is needed to make timely decisions.
- Simplified IT Management: Centralized data is much more efficient to manage, allowing IT teams to efficiently manage its security, compliance, and operations, ensuring the uniformity of protocols and eliminating data silos.
Risk Reduction
With data being one of the most targeted assets of a business by cybercriminals, data virtualization helps mitigate risks and gain customer and stakeholder trust. By integrating data from multiple sources into a single virtual layer, data virtualization also minimizes inconsistencies and errors and reduces the risk of poor business decisions being based on faulty data.
Time-to-Value Acceleration
Data virtualization significantly reduces time to value (TTV) by streamlining data collection and delivery processes. Businesses leveraging the technology see quicker completion of data-driven projects, resulting in faster return on investment (ROI) cycles such as increased revenue, cost savings, and risk mitigation. Focusing on accelerating TTV lets companies maintain a competitive edge and adapt swiftly to market changes and customer demands.
Staff Productivity
Staff productivity is closely linked to the efficiency of operations and ease-of-access to data. By implementing data virtualization, companies can reduce the time staff spends on some data-related tasks, allowing them to focus on more analytical and decision-making tasks. Increased staff productivity can manifest in the following ways:
- Reduced Data Handling Time: Access to a centralized database eliminates the need for processing multiple queries across various systems, cutting down the time employees spend on collecting and verifying individual data sets.
- Enhanced Collaboration: With access to up-to-date data, teams across different locations can more effectively share insights and collaborate without having to worry about data silos or inconsistent information.
- Streamlined Training and Onboarding: Data virtualization tools are generally user-friendly, with a lower technical knowledge barrier to overcome. New employees can begin contributing to teams more quickly.
Top 3 Recommended Data Virtualization Tools
The market is filled with data virtualization tools, each with different features and strengths to meet the needs of a range of enterprise data infrastructures. Choosing the right one will depend upon your own needs and budget.
Denodo Data Virtualization
The Denodo Data Virtualization platform is ideal for organizations handling big data thanks to its built-in integration with Hadoop distributions and NoSQL data storage. It’s highly flexible and scalable, allowing businesses to continue leveraging their existing data infrastructures without needing to invest in new resources.
Denodo’s platform facilitates self-service business intelligence (BI), data science, and hybrid/multi-cloud data integration, making it a versatile choice for enterprises seeking to enhance their data services and analytics capabilities. It also excels at providing robust security and governance capabilities, which are essential for maintaining data integrity and compliance.
IBM Cloud Pak for Data
IBM Cloud Pak is a comprehensive solution that includes data virtualization and offers various integration options for data management tasks, all on the same platform. It’s available as a fully managed service on IBM Cloud or as a self-hosted solution.
By supporting a wide array of data sources and architectures, IBM Cloud Pak helps businesses build a connected data ecosystem across hybrid cloud environments, facilitating collaboration and productivity. Its capabilities in reducing data replication and simplifying analytics through a modular set of components make it a powerful tool for businesses focused on digital transformation.
TIBCO Data Virtualization
TIBCO Data Virtualization excels in flexibility, with the ability to handle various data sources like databases, big data, and cloud sources. It’s a Java-based architecture that supports extensive customization and scalability for more efficient real-time data delivery across geographical locations.
TIBCO is best known for its wide range of connectivity and data transformation options, supporting multiple scripting and query languages such as SQL, Java, and XQuery. It’s primarily web-based, with a simplified user interface for a self-service model. The platform’s advanced optimization algorithms and fine-grained security measures ensure that data is safe and in compliance with data protection regulations.
Bottom Line: Data Virtualization Benefits
The inclusion of data virtualization technology lets businesses of various industries simplify their complicated data processing operations, enhancing their decision-making capabilities and resource usage. The centralization of the data layer also ensures real-time data accessibility and collaboration between different departments and locations of the business.
Specialized tools made by Denodo, IBM, and TIBCO help streamline the data virtualization process thanks to a user-friendly interface and built-in integration capabilities with other data management and processing tools. These tools embody the future of data management, enabling businesses to take advantage of data virtualization benefits to navigate the complexities of big data in a cloud-centric ecosystem.
Data virtualization is one piece of the data science puzzle that enterprise organizations are increasingly working to solve to make their vast data estates more accessible, reliable, and secure. Read about the top data science tools you can use in 2024 to make sure your organization is keeping pace.
