Unifying Enterprise AI and Hybrid, Multicloud Architecture, Using Data Virtualization

Unifying Enterprise AI and Hybrid, Multicloud Architecture, Using Data Virtualization

After over a year of the COVID-19 pandemic, perhaps we can now finally see the light at the end of the tunnel, though it may still seem a bit far off. Already, the economy is starting to recoup some of its losses. However, even as we contemplate a return to normal, we should not go back to “business as usual,” because this would mean forgoing some of the innovations that were developed specifically to cope with a population that suddenly began to work, study, socialize, and seek medical advice almost exclusively online.

Two Emerging Technologies

Consider enterprise artificial intelligence (AI), which has smoothed the path for many businesses along their journeys toward digital transformation. Many large tech companies have recently been touting their capabilities in AI and machine learning (ML), which is understandable, given the strength of these capabilities, but organizations have a difficult time integrating disparate AI/ML models with their expanding data science teams. Enterprise AI overcomes these challenges through shared workspaces and pre-built algorithms, which minimize duplication while facilitating collaboration and accelerating time-to-market.

Next, consider hybrid multicloud architecture, which has been playing an increasingly prominent role in today’s data infrastructure. However, with a mix of on-premises sources and multiple cloud platforms, it becomes challenging to both access and manage data across the entire enterprise.

The Role of Data Virtualization

What we need is a third technology, data virtualization. Data virtualization establishes a unified, real-time data-access layer across all enterprise sources, be they on-premises, in the cloud, or in multiple clouds, enabling a logical data fabric. Supported by data virtualization, teams can work more efficiently and collaborate more effectively. Because it provides views of data rather than replicating it, it saves access and storage costs, and because it provides a unified data-access layer, it enables stakeholders to implement data governance controls from a single point across the entire enterprise. This “single source of truth” is one of the most valuable characteristics of unifying enterprise data through data virtualization.

Beyond “Business as Usual”

Enterprise AI, multicloud architecture, and data virtualization is not a conceptual framework, but a proven combination that is being leveraged by many modern organizations in their digital transformations, expanding what is possible beyond “business as usual.” Data virtualization forms the “glue” that holds the other two critical pieces together. Data virtualization underscores the fact that digital transformation is not just about technology, but about using technology in the most intelligent way, ultimately to bring enhanced value to internal and external customers.

To learn more about unifying enterprise AI and multicloud architecture with data virtualization, please contact me at nicholas@metora.co or visit www.metora.co

Nicholas Kühne

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