10 Things You Need to Know about Data Virtualization- Denodo

Gartner estimates that by 2020, 35% of enterprises will have adopted some form of data virtualization (DV) as a production option for data integration. But what exactly is data virtualization, and why does it matter?

Here are 10 key points about data virtualization:

1. Lower Maintenance Costs

Traditional integration tools often involve significant expenses for data replication, movement, and storage. DV creates a virtual data layer that eliminates the need for repeated data replication, leading to substantial cost savings.

2. Faster Data Management

With traditional data integration methods, obtaining results can take hours or even days. DV, however, delivers data in real-time, accelerating decision-making processes.

3. Complements Data Warehousing

DV is designed to work alongside traditional data warehousing solutions, enhancing their capabilities and providing additional flexibility.

4. Optimizes Performance

Performance bottlenecks often stem from network latency—the delay before data transfer starts. DV directly connects to the data source, facilitating real-time insights and minimizing latency issues.

5. Empowers Self-Service BI

DV allows business users to access and analyze data independently, reducing their reliance on technical teams and fostering a culture of data-driven decision-making.

6. Enhances Data Governance

By centralizing data access, DV ensures effective security management, robust data governance, and streamlined performance monitoring across the enterprise.

7. More Advanced Than Data Federation

While DV builds on the older concept of data federation, it goes further by offering advanced features such as performance optimization and self-service search and discovery capabilities.

8. Strong Return on Investment (ROI)

Most data virtualization projects deliver a return on investment within six months. DV helps businesses achieve time savings of 50% to 80% compared to traditional data integration techniques.

9. Greater Agility

DV supports prototyping, allowing organizations to test strategies and solutions before full-scale implementation, making it more flexible than conventional methods.

10. Enriches Big Data Fabric

Data virtualization underpins Big Data Fabric by integrating and preparing data for predictive analytics, making real-time information accessible to end users.