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Open Data Infrastructure (ODI) is an architectural approach—not a single product—that uses open standards, modular components and separate storage and compute to help data systems work together. It may give organizations more control over data and make it easier for analytics and AI systems to access consistent information. But ODI is not a guarantee of better AI outcomes: the case for it in a September 2026 TechRadar Pro opinion article rests on the author’s argument, not a published implementation comparison.
What Open Data Infrastructure means
In a TechRadar Pro Perspectives article published 14 September 2026, Anjan Kundavaram, Fivetran’s Chief Product Officer, describes ODI as an architectural approach in which tools and platforms use shared, open standards to access, move and use data. In his words, “ODI is 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.” Read the TechRadar Pro article.
The idea is a collection of architectural choices rather than a branded platform or a particular vendor’s product. In the article, its key elements are open standards, modular components, and separation of data storage from the compute resources that process it. The author argues these features let parts of a data stack interoperate and change independently.
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Why data foundations can affect AI outcomes
AI systems depend on the data made available to them. If data is fragmented, stale, difficult to access or unreliable, an organization can spend heavily on AI without making that investment useful in practice. The article argues that shared, dependable data access can help analytics tools and AI agents work from more consistent information.
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That is a plausible architectural rationale, not evidence that ODI alone improves model quality, business impact or return on investment. Results also depend on the data itself, governance, deployment choices and the business processes around an AI system. The TechRadar article does not report a controlled comparison of organizations using ODI against those using other architectures.
What the article’s figures do—and do not—show
Kundavaram uses several statistics to illustrate the gap between AI spending and data readiness. They should be read as figures reported in his opinion article, not as independently verified findings: the article provides incomplete attributions and does not identify the underlying study titles, sample sizes or field dates for most figures. The underlying sources could not be established from the article’s citations.
- More than 90 percent of CIOs globally are said to be increasing AI funding; the article attributes this to Gartner but gives no year or report details.
- Average annual enterprise spending on data programs is stated as $29.3 million, without a named source or year.
- Organizations with successful AI initiatives are said to invest up to four times more in data and analytics foundations; the source and year are not stated.
- The article says 73 percent of organizations report data initiatives falling short of expectations and nearly 62 percent report low data maturity; neither figure is tied to an identifiable study.
- Large organizations are said to experience more than 60 hours per month of downtime from data-pipeline failures, with an estimated business impact of £50,000 per hour. The article does not name the study or define the organizations included.
- More than half of data-engineering capacity is said to go to pipeline maintenance, and modern, managed, open data foundations are said to be associated with nearly twice the likelihood of exceeding ROI targets. The article does not supply enough study information to assess either claim independently.
- A ratio of 82 non-human entities to each human in modern enterprises is attributed only to unnamed “studies.” The article does not define the entities or cite the studies.
These claims may communicate the author’s concern, but the missing provenance means readers cannot determine how the measures were defined, which organizations they represent or how comparable they are. They are not a sound basis for predicting a particular organization’s costs or AI returns.
What ODI is intended to improve
Interoperability
Using shared standards is intended to make it easier for different tools and platforms to work with the same data. That can reduce dependence on custom connections, but interoperability depends on the standards and implementations actually supported by the systems an organization uses.
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Portability and control
Modular components and separation of storage from compute can give teams more flexibility to change one part of an architecture without replacing everything. The author presents this as a way to maintain control and reduce lock-in. It does not remove switching costs by itself: proprietary formats, application dependencies, governance rules and the effort of moving data can still make an exit difficult.
Consistent access for analytics and AI
A common way to access governed, current data may help people, analytics software and AI agents draw on more consistent information. That benefit depends on reliable pipelines, clear access controls and appropriate data quality. An open architecture cannot make inaccurate source data accurate or resolve conflicting definitions without additional work.
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How to assess the approach for your organization
The TechRadar article does not compare products or prescribe a migration plan. Treat ODI as a set of design principles to evaluate against your current needs, rather than as a guaranteed solution or a mandate to replace an existing platform.
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- Standards and interoperability: Identify which open standards matter for your workloads and confirm that the tools you rely on implement them in compatible ways.
- Storage and compute: Check whether storage and processing can be changed or scaled independently, and identify dependencies that could prevent that separation in practice.
- Portability and exit costs: Map proprietary formats, data-egress charges, application dependencies and the work required to move data and workloads elsewhere.
- Governance and access control: Determine how permissions, auditing, retention and policy enforcement apply across components, not just within one platform.
- Freshness and reliability: Define the required data-update frequency and service expectations; measure pipeline failures and recovery against those requirements.
- Total operating cost: Include infrastructure, integration, maintenance, governance and the people needed to operate the system. Compare those costs with the flexibility ODI is expected to provide.
These checks turn broad promises such as “less lock-in” or “better AI readiness” into questions that can be tested in a specific environment. Set a baseline first, then judge any proposed change against the organization’s own reliability, portability and cost requirements.
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How to read the argument
The article’s central point is that AI investment cannot compensate for weak or fragmented data foundations. Its proposed response—open standards, modularity and separation of storage and compute—is an architectural case made by Kundavaram, whose role as Fivetran’s Chief Product Officer is relevant context. TechRadar notes that Perspectives views belong to their authors and are not necessarily those of TechRadar Pro or Future plc. The piece offers a useful framework for evaluating data architecture, but it is not independent evidence that ODI produces a particular financial or technical outcome. TechRadar Pro Perspectives listing.
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