What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Open Data Infrastructure (ODI) is an architectural approach—not a single product—that uses open standards, modular components and separation of storage from compute to help data systems work together. In a 14 September 2026 TechRadar Pro Perspectives article, Anjan Kundavaram, Fivetran’s Chief Product Officer, argues that stronger data foundations can help organizations turn AI spending into useful outcomes. That is a vendor executive’s opinion, not a measured comparison proving ODI delivers a particular return.
Why AI spending may not translate into results
AI systems depend on data that can be accessed, moved and used consistently. When data is fragmented across systems, difficult to govern or delivered unreliably, organizations may struggle to put AI initiatives into practice. In his article, Kundavaram frames this mismatch as a data-architecture problem: investment in AI alone does not ensure that the information and pipelines supporting it are fit for purpose.
As an Amazon Associate I earn from qualifying purchases.
The article reports a series of figures to illustrate the gap, but several lack the details needed to assess or verify them. It says more than 90% of CIOs globally are increasing AI funding, attributing that figure to Gartner without naming a year. It also reports that 73% of organizations say data initiatives fall short of expectations and nearly 62% report low data maturity, without identifying the organizations behind those figures or the studies’ methods. These are claims reported in the article, not independently established findings.
Other figures in the piece are similarly difficult to interpret without their underlying sources: average annual enterprise spending of $29.3 million on data programs; up to four times greater investment in data and analytics foundations among organizations with successful AI initiatives; more than 60 hours of monthly downtime from pipeline failures in large organizations, with an estimated £50,000 hourly business impact; and more than half of data-engineering capacity spent on pipeline maintenance. The article does not provide the study titles, sample sizes, field dates or links needed to evaluate those estimates. It also says modern enterprises have an 82:1 ratio of non-human entities to people, and that organizations with modern, managed, open data foundations are nearly twice as likely to exceed ROI targets as those on legacy systems, without identifying the underlying studies.
#1 Best Overall
What Open Data Infrastructure means
ODI describes a way to organize data systems around shared, open standards and modular components. Rather than treating a platform as one inseparable stack, the approach aims to let different tools interoperate and let individual layers change without requiring a wholesale replacement of the rest.
A central idea in the article is separating storage from compute. Data can reside in a storage layer while different computing tools access and process it. The architectural goal is to let storage and compute evolve independently, rather than tying the data to one processing engine or the engine to one storage system. ODI is therefore a pattern for evaluating architecture, not a guarantee that any particular product is open, portable or interoperable.
What ODI is intended to improve
Interoperability
Shared standards can make it easier for tools and platforms to exchange or use data. Kundavaram describes ODI as giving organizations greater control over how data is accessed, moved and used by allowing platforms to work together through open standards. The article presents this as the model’s purpose; it does not report a comparative test demonstrating how well specific systems interoperate.
Portability and control
Modular components and separation of storage from compute may give an organization more options to change one part of its architecture while retaining others. That could reduce dependence on a single provider, but open standards alone do not establish that a migration will be easy or inexpensive. Data formats, proprietary features, governance rules and operational dependencies can still make an exit costly.
More consistent access for analytics and AI
The article argues that a shared, reliable data foundation can make information more consistently available to analytics systems and AI agents. This is a plausible architectural objective, not an outcome guaranteed by adopting ODI: data quality, freshness, permissions, pipeline reliability and the design of each AI use case still matter.
How to assess an ODI approach
The article does not compare vendors or products. Organizations considering the approach can use these questions to examine their own architecture and proposals:
- Standards and interoperability: Which open standards and data formats are actually supported? Can the systems exchange data without proprietary connectors or manual workarounds?
- Storage and compute: Can storage and processing components be selected or changed independently? What dependencies would prevent that in practice?
- Portability and exit costs: Can data and metadata be exported in usable formats? What would it take to move workloads, rebuild pipelines and preserve history if a component changed?
- Governance and access control: Can teams apply consistent permissions, lineage and policy enforcement across tools, including for AI workloads?
- Freshness and reliability: How will the organization monitor data delays, pipeline failures and recovery? What service expectations apply to the specific workload?
- Total operating cost: What staffing, integration, compute, storage and migration costs accompany the design? A modular architecture may increase choice, but it also needs to be operated and governed.
How to read the article’s claims
TechRadar Pro published Kundavaram’s article on 14 September 2026 as a Perspectives piece. TechRadar identifies him as Fivetran’s Chief Product Officer, and the article states that its views are the author’s and are not necessarily those of TechRadar Pro or Future plc. That context matters: the piece makes the case for an architectural approach, but it is not an independent evaluation of ODI or a product comparison.
The statistics should be treated as figures the article reports, rather than as a complete evidence base. Because their underlying studies and methods are not identified, readers cannot determine from the article whether the figures use comparable definitions, populations or time periods. They are not enough on their own to forecast the effect ODI would have in a particular organization.
What the case for ODI does—and does not—establish
The article’s useful contribution is its emphasis on architecture as part of AI readiness: systems should make data manageable and accessible, not merely accumulate AI funding. ODI offers a way to think about that goal through open standards, modularity and independent storage and compute layers. The article does not establish that ODI will produce a particular financial return, eliminate lock-in or outperform other architectures. Those outcomes depend on implementation and should be assessed against an organization’s own requirements, costs and evidence.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
