Open Data Infrastructure Gains Ground as AI Spending Outpaces Data Readiness, TechRadar Reports

TechRadar cites Gartner for the CIO figure and says additional research shows enterprises now spend an average of $29.3 million per year on data programs. Those programs include data movement, ingestion and preparation tooling, recurring cloud ingest and compute costs, and the internal engineering capacity needed to keep pipelines running. Organizations with successful AI initiatives invest up to four times more in data and analytics foundations, but higher budgets do not automatically produce high-quality data, the article says.

Despite unprecedented investment, most enterprise data initiatives continue to underperform. TechRadar reports that 73 percent of organizations say their data initiatives are falling short of expectations, while nearly 62 percent report low levels of data maturity. In large organizations, downtime caused by data pipeline failures now exceeds 60 hours a month, disrupting productivity and costing an estimated £50,000 per hour in business impact. Data teams spend more than half of their engineering capacity on pipeline maintenance rather than advancing new use cases, according to the article.

TechRadar describes Open Data Infrastructure, or ODI, as an architectural approach that gives organizations greater control over how data is accessed, moved and used by allowing tools and platforms to work together through shared, open standards. Instead of relying on tightly coupled, proprietary systems, ODI is built on a modular, standards-based foundation that separates storage from compute and allows each layer to evolve independently. As data and AI workloads grow, the article says this creates a unified data environment where analytics and AI can scale more efficiently.

ODI is also presented as a direct challenge to vendor lock-in. TechRadar says the industry is seeing data become more restricted, both technically and commercially, with constraints often appearing as hidden costs or dependencies that push companies toward specific walled-garden ecosystems. The problem is amplified when AI entities become an organization's primary data users. Studies cited by the article suggest non-human entities are present in modern enterprises at a ratio of 82 to 1 compared with humans.

For AI agents to work effectively alongside human users, TechRadar says a shared source of truth is essential. Dashboards, operational workflows, machine learning models and AI agents may all draw from the same underlying data, but they often operate in separate environments with different definitions and models. When those definitions drift, the result can be misaligned decisions, unreliable AI outputs and additional engineering overhead. ODI aims to address this by giving every system, human or automated, a consistent view of the business.

The article adds that AI agents generate exponentially more queries than humans, but closed ecosystems often route them through the same expensive compute infrastructure. Agents can optimize for cost only when open architectures allow them to choose cheaper compute engines when appropriate. Organizations using legacy systems pay significantly more per data pipeline, which, multiplied by hundreds of pipelines at enterprise scale, adds up to a significant ongoing expense.

As investment in AI tools continues to ramp up, TechRadar says organizations must think ahead to alleviate strain on both budgets and engineering resources and to ensure AI systems have consistent access to fresh, trustworthy data.