Unified databases: A powerhouse behind generative AI success
Unified databases support generative AI (GenAI) by integrating all data types into a single platform, streamlining infrastructure and improving scalability, speed and trust.
- Stephen Catanzano,
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Enterprise Strategy Group
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As generative AI shifts from buzzword to business driver, organizations face a crucial challenge: building AI applications that can access the full spectrum of their data --structured, semi-structured and unstructured -- while remaining scalable, secure and fast. Many are turning to unified, or converged, databases to address this.
While some teams have focused on working with multiple databases tailored to a specific data type or use case, this strategy might not always be the best and might have some real trade-offs. Managing multiple systems can introduce complexities in governance, data integration, security policies and operational overhead complexity. When AI models rely on incomplete or inconsistent data, the value of insight and the trust in outputs can quickly erode.
Converged databases: One platform, many models
Converged databases are gaining serious traction for good reason. These platforms are designed to support all data types and workloads within a unified environment. Modern unified databases combine structured data in SQL, semi-structured formats like JSON, unstructured text and complex data like vectors and graphs, streamlining the data foundation that generative AI depends on.
Research from the Enterprise Strategy Group shows that 84% of organizations are implementing or actively evaluating new databases to support generative AI initiatives. The same study shows that 71% expect to deploy 11 or more generative AI applications within two years, most of which rely heavily on enterprise data. The need for scalable, AI-ready data infrastructure is more urgent than ever.
Oracle as a real-world example
One example of this converged approach in action is Oracle Database 23ai. While it's not the only option, it illustrates how unified platforms can support GenAI use cases by combining traditional relational capabilities with modern features:
- Duality views that allow developers to work with both JSON and relational data in a single model.
- AI vector search and vectorization for fast semantic search and retrieval-augmented generation (RAG).
- Built-in support for documents, graphs and time-series -- all within the same environment.
This versatility allows developers to focus on building applications rather than managing data pipelines. For example, a biotech company used Oracle's converged platform to analyze genomic data sets, reducing diagnostic time from five days to less than four hours. A media organization reported that its vector search performance improved seven times, streamlining creative workflows.
Balancing simplicity and power
Using multiple best-of-breed databases is still viable, especially for workloads with unique requirements. However, this approach can increase complexity in maintaining consistent security policies, ensuring data quality across systems and dealing with varied query languages and APIs. Unified platforms help reduce this burden by consolidating operations without compromising on capability.
Strategic takeaways
Generative AI isn't just another workload; it's a new way of working with data, which means rethinking the data infrastructure that powers it. Whether through a converged database or other modern unified platform, organizations that centralize their data in a smart, scalable way will be better positioned to capitalize on AI's full potential.
Stephen Catanzano is a senior analyst at Enterprise Strategy Group, now part of Omdia, where he covers data management and analytics.
Enterprise Strategy Group is part of Omdia. Its analysts have business relationships with technology vendors.
