Discover how a bimodal integration strategy can address the major data management challenges facing your organization today.
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CData Virtuality is an independent semantic layer that bridges the gap in your heterogeneous data landscape through four key pillars: connectivity, modeling, governance, and data product delivery.
CData Virtuality's four key pillars: The difference
Seamlessly connect heterogeneous data — bridging the gap between digital platforms and legacy systems for real-time insight
Accelerate data preparation with no scale limitations — bridging virtual and physical data models
Centralize data governance — providing holistic management of physical and virtual data assets with corresponding business, operational, and technical metadata
Deliver data products across disparate environments — put the right data into the hands of the right users at the right time
CData Virtuality in your modern data architecture
The benefits of CData Virtuality
Integrate heterogeneous data sources with 300+ connectors easily, breaking down data silos and reducing custom coding and maintenance efforts.
Gain real-time insights, greater visibility, and faster, more informed decision-making with a complete, unified view of data from all sources, eliminating data gaps.
Test, iterate, and deploy new data solutions faster through rapid prototyping using a mix of low-code and code-based options, increasing agility and reducing time-to-market for growth.
Accelerate analytics and reporting by adapting quickly to new business needs, ensuring faster, data-driven decisions and a competitive edge.
Consistently apply access controls and policies across systems, enhancing compliance and security.
Reduce risk associated with inconsistent or unsecured data and rely on trustworthy, consistent data.
Deliver data products across disparate environments and enable self-service data access, reducing IT workload by empowering stakeholders to retrieve and analyze data independently.
Take action independently, boost productivity, and provide swift responses to market changes by using a central data access point to quickly analyze data.
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Our key pillars in action
Crédit Agricole reduced time-to-market by 5x and lowered costs by leveraging the wide range of ready-to-use connectors and rapid data modeling.
Read case studyA public healthcare provider improved hospital operations, reduced costs, and accelerated AI initiatives through enhanced connectivity, centralized data governance, and flexible data delivery.
Read case studyPGGM became a data-driven organization by creating a central data access layer with unified data governance, simplifying data product delivery and bridging the gap between data and users.
Read case studyAreeba solved the 80/20 data science dilemma, significantly boosting productivity and efficiency through streamlined data connectivity and data delivery.
Read case studyWith CData Virtuality, you can
Build a central data access layer
that unifies complex data, allowing data citizens to perform self-service analytics with ease and efficiency.
Adopt a data mesh architecture
to boost organizational agility and flexibility, empowering domain teams to independently manage their data products.
Establish a data fabric
to enhance operational efficiency, simplify infrastructure, and cut costs.
Accelerate AI initiatives
with a robust data foundation, delivering high-quality, trusted, real-time data to power your AI models.
Integrate hybrid- and multi-cloud architectures
to lower operational costs and enhance business continuity.