AI Enforces Open Data: The End of Silos

The Rise of AI and the Need to Rethink Data Infrastructures

The rise of artificial intelligence (AI) compels companies to rethink the very foundations of their data architecture. Infrastructures that were once designed for reporting are no longer sufficient to support automated systems, which are increasingly demanding in terms of reliable, fresh, and well-governed data. To evolve without exploding costs or complexity, openness becomes a fundamental principle of architecture.

For years, companies built their data architectures around a simple assumption: data primarily served to help humans make decisions through dashboards and reports. That era is now over. Data today fuels operational workflows, machine learning systems, and increasingly, AI agents that require reliable information available at scale.

These new uses highlight the limitations of traditional architectures. Companies can no longer afford to choose between reliability and cost control. They must build an infrastructure capable of ensuring both. This is precisely the role of an open data infrastructure.

The Historical Trade-off of Traditional Architectures

Traditional architectures have long constrained organizations to choose between two imperfect options. The data warehouse provided a structured, reliable, and optimized environment for analytics. In contrast, the data lake better met the needs for massive storage and unstructured data: it was cheaper, more scalable, but harder to govern and less reliable for demanding analytical use cases.

This trade-off has fragmented systems. Teams extracted, loaded, and transformed data in one environment for business intelligence (BI) and reporting, then replicated the same operations elsewhere for large-scale storage, data science, or application support. Over time, this organization multiplied data copies, redundant pipelines, and governance exceptions.

The cost is not limited to infrastructure. It is also measured in engineering time, maintenance, oversight, and security. Each duplication, each transition between systems, each exception adds complexity. As the architecture grows, it becomes more expensive to operate and harder to ensure reliability.

Dependence on a single vendor further reinforces this fragility. When storage, computation, and data access are tightly intertwined within a single platform, initial adoption seems simple, but scaling becomes costly. Companies may then pay for high service levels for workloads that do not justify them, while losing the freedom to optimize their tools, performance, and costs.

Openness as a Solution to Current Challenges

An open data infrastructure offers a clearer trajectory. It combines the economic scalability of the data lake with the structure and reliability historically associated with the data warehouse. Open table formats, such as Apache Iceberg or Delta Lake, provide lake-based architectures with essential capabilities: relational structure, schema control, and ACID-type transactional reliability.

These capabilities make data stored in the lake more usable for production analytics and AI-related workloads, while retaining the data lake's ability to handle unstructured data. The company can thus rely on a single foundation, rather than maintaining multiple parallel environments.

The open infrastructure also rests on a crucial principle: the decoupling of storage and computation. Organizations can store data once, in a standard and cost-effective object storage, and then choose the most suitable computation engine for each use: BI, data science, operational applications, or AI systems.

This flexibility improves both reliability and cost control. Reliability increases because teams can organize their operations around a single architecture, with consistent structure, governance, and semantics. Cost control improves because storage remains economical and computation can be selected based on the actual performance and price needed.

Interoperability is another pillar of this approach. Open formats reduce dependence on a single vendor. A foundation based on open standards can serve many downstream tools without requiring teams to duplicate data or pipelines. Data flows less, but it is used more effectively.

The Urgency of Transformation in the Age of AI

AI systems amplify both the scale and consequences of decisions. They create more automated actions downstream, making the quality and availability of data even more critical. A fragile and costly architecture not only stifles innovation; it can also amplify the negative effects of poorly managed AI.

Cost management thus goes beyond simply reducing the cloud bill. It also involves reducing the engineering and administrative burden required to keep the system running. The best architecture is not only cheaper to store or query. It is easier to operate, easier to govern, and easier to adapt.

The open data infrastructure is therefore not just a technical evolution among others. It marks the shift from a stacking logic to a logic of cost control, governance, and future use cases.

As AI gains autonomy, data becomes less a dormant asset and more an operational fuel. If this fuel is fragmented, costly to mobilize, or trapped in closed environments, the promises of AI will remain limited to a few experiments. Companies capable of structuring an open, interoperable, and governed foundation will have a decisive advantage: the ability to innovate without rebuilding their architecture for each new use case.

The question is no longer about choosing between reliability and cost control. It is about building an infrastructure capable of ensuring both. For enterprise AI, this is now the foundation that will make the difference.