Enterprises are investing heavily in data infrastructure, but many are struggling to translate that spend into measurable business impact. That is according to Fivetran’s Enterprise Data Infrastructure Benchmark Report 2026, published March 2026, which revealed that large organizations now spend an average of $29.3 million annually on data programs. Of that, $2.2 million is dedicated solely to maintaining data pipelines, an operational cost that continues to scale with complexity rather than value.
The findings are based on a survey of more than 500 senior data and technology leaders at organizations with over 5,000 employees, spanning industries and regions. The results point to a consistent pattern: despite significant investment in analytics, AI, and real-time decision-making capabilities, foundational data infrastructure remains a bottleneck.
A primary constraint is the disproportionate amount of engineering effort required to keep pipelines running. Data teams report that 53% of their time is spent on maintenance rather than innovation. On average, enterprises manage 328 pipelines, supported by teams of 35 to 60 engineers. This operational overhead reflects fragmented and often manual processes that become increasingly difficult to manage as environments scale.
Pipeline reliability is a key contributor to both cost and performance challenges. Organizations relying on legacy or custom-built data integration approaches report 30-47% higher failure rates compared to those using modern, managed solutions. These failures translate into approximately 60 hours of downtime per month. With the estimated business impact of downtime at $49,600 per hour—rising to $75,200 in larger enterprises—this equates to nearly $3 million in potential value loss per month, or more than $36 million annually.
The downstream impact on analytics and AI initiatives is significant. Nearly 30% of organizations report delays of a month or more in delivering data-driven projects due to pipeline maintenance or downtime issues. These delays undermine the timeliness of insights and reduce the effectiveness of AI systems that depend on consistent, high-quality data inputs.
Budget allocation patterns further illustrate the disconnect between investment and outcomes. Enterprises dedicate approximately 14% of their data budgets, or $4.2 million on average, to data integration. However, only 27% of organizations report that their data investments exceed ROI expectations, while 73% say initiatives fall short. At the same time, many organizations incur more than $500,000 per month in cloud ingest and compute costs, adding to the total cost of ownership without necessarily improving performance or reliability.
A significant factor in these outcomes is data maturity. The report finds that 62% of enterprises still operate at low levels of data maturity, characterized by fragile pipelines, tight coupling between systems, and reliance on manual intervention. These conditions not only increase the likelihood of failure but also slow down recovery times. For organizations using legacy or DIY approaches, incidents typically take between 13 and 16 hours to resolve.
In contrast, organizations that adopt fully managed data integration models show measurable improvements. Managed ELT approaches reduce per-pipeline costs—from approximately $1,900 with legacy ETL to $1,600—and shorten recovery times to around 11 hours. More importantly, they improve reliability and reduce the engineering burden associated with maintenance, enabling teams to reallocate time toward higher-value activities.
The report highlights a widening cost gap between legacy and modern approaches. While legacy and custom-built systems may appear cost-effective upfront, hidden costs—particularly those associated with downtime and maintenance—often exceed the price of the tools themselves. As a result, total cost of ownership is significantly higher in environments that rely on fragmented or manually managed pipelines.
For enterprise leaders, the implications are operational rather than theoretical. Data infrastructure decisions directly affect the speed, reliability, and cost of analytics and AI initiatives. As organizations scale their use of AI, the tolerance for pipeline instability decreases, making reliability and automation foundational requirements rather than optimization targets.
The findings suggest that improving data infrastructure is less about increasing spend and more about reallocating it. Reducing maintenance overhead, improving pipeline resilience, and adopting managed integration approaches can have a disproportionate impact on both cost efficiency and delivery timelines. In environments where AI outcomes depend on consistent data availability, these changes are increasingly tied to competitive performance.