The rise of AI is forcing companies to review the very foundations of their data architecture.
Infrastructures designed for reporting are no longer sufficient to supply automated systems, requiring reliable, fresh and governed data. To scale up without exploding costs or complexity, openness becomes an architectural principle.
For years, companies have built their data architectures around a simple hypothesis: data was primarily used to help humans make decisions, via dashboards and reports. Those days are over. Data now powers operational workflows, machine learning systems and, increasingly, AI agents who need reliable information, available at scale.
These new uses reveal the limits of traditional architectures. Companies can no longer choose between reliability and cost control. They must build an infrastructure capable of guaranteeing both. This is precisely the role of an open data infrastructure.
The historic compromise no longer holds
Traditional architectures have long forced organizations to choose between two imperfect options. The data warehouse provided a structured, reliable and optimized environment for analytics. The data lake better met the needs for massive storage and unstructured data: cheaper, more scalable, but more difficult to govern and less reliable for demanding analytical use cases.
This compromise fragmented the systems. Teams extracted, loaded and transformed data in an initial environment for BI and reporting, then reproduced the same operations elsewhere for large-scale storage, data science or application support. Over time, this organization has had multiple data copies, redundant pipelines, and governance exceptions.
The cost is not limited to infrastructure. It is also measured in engineering time, maintenance, supervision and safety. Each duplication, each passage between systems, each exception adds complexity. As the architecture grows, it becomes more expensive to operate and more difficult to make reliable.
Dependence on a single supplier further reinforces this fragility. When storage, computing, and data access are closely intertwined in a single platform, initial adoption seems simple, but scaling up becomes costly. Companies can then pay high service levels for workloads that don’t justify them, while losing the freedom to optimize their tools, performance and costs.
Openness changes the equation
An open data infrastructure offers a more readable trajectory. It combines the economical 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, bring essential capabilities to lake-based architectures: relational structure, schema control and ACID-style transactional reliability.
These capabilities make the data stored in the lake more usable for production analytics and AI workloads, while maintaining the data lake’s ability to process unstructured data. The company can thus rely on a single base, rather than maintaining several parallel environments.
Open infrastructure is also based on a determining principle: the decoupling of storage and calculation. Organizations can store data once, in standard, cost-effective object storage, then choose the calculation engine best suited to each use: BI, data science, operational applications or AI systems.
This flexibility improves both reliability and cost control. Reliability improves because teams can organize their operations around a single architecture, with consistent structure, governance, and semantics. Cost control improves because storage remains economical and the calculation can be chosen according to the level of performance and price actually necessary.
Interoperability constitutes the other pillar of this approach. Open formats reduce dependence on a single vendor. A foundation built on open standards can serve many downstream tools without requiring teams to duplicate data or pipelines. Data circulates less, but it is better used.
AI makes this transformation urgent
AI systems amplify both the scale and consequences of decisions. They create more automated actions downstream, making data quality and availability even more critical. Fragile and expensive architecture not only blocks innovation; it can also amplify the negative effects of poorly controlled AI.
Cost management therefore goes beyond simply reducing the cloud bill. It also involves reducing the engineering and administrative burden required to keep the system running. The better architecture isn’t just cheaper to store or query. It is simpler to operate, simpler to govern and simpler to adapt.
Open data infrastructure is therefore not just one technical development among others. It marks the transition from a logic of stacking to a logic of cost control, governance and future uses.
As AI becomes more autonomous, data becomes less of a dormant asset and more of an operational fuel. If this fuel is fragmented, expensive 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: that of being able to innovate without rebuilding their architecture for each new use case.
The question is therefore no longer about choosing between reliability and cost control. It is to build an infrastructure capable of guaranteeing both. For enterprise AI, it is now the foundation that will make the difference.
