New Report Reveals How Data Stack Complexity is Undermining Decision Making and AI Innovation

‍Survey shows 85% cite tool integration as a top challenge, while 63% spend over one day per week on maintenance instead of delivering business value‍

PALO ALTO, California, August 21, 2025 — The Modern Data Company, creator of DataOS^®, the world's first data operating system built to accelerate AI at enterprise scale, released the 2024–2025 Modern Data Report, revealing that complex data architectures force teams to spend valuable time on infrastructure over strategic impact. The research highlights persistent issues with the current approach to data stacks powering business insights and AI. This includes tool sprawl, fragmented governance, lack of automation, and poor integration–all of which prevent organizations from effectively unlocking the value of their data.

Based on a global survey of more than 230 senior data professionals, with an average of over 15 years of experience, the report exposes a widespread inability to efficiently operationalize enterprise data due to complex, fragmented, maintenance heavy architectures. These inefficiencies slow decision-making, inflate costs, and cause organizations to miss critical opportunities in fast-moving markets.

The report details how data stack complexity is a persistent tax on data teams, with two-thirds of respondents (63%) spending more than 20% of their time on maintenance. Nearly 70% say understanding business requirements is their biggest time sink, and 65% lose one fifth of project time simply determining what data to use. These delays reduce agility and often result in the need to rework, eroding the return on data investments.

"Architecture complexity is the invisible force slowing down most data teams," said Srujan Akula, Co-founder and CEO of The Modern Data Company. "When teams say they spend one day a week on maintenance, that's not a staffing problem—that's a fundamental design problem. The industry's answer has been to add more tools, but more tools just create more integration headaches and slow things down even more. It's time to reimagine how data infrastructure should work."

Tool sprawl is a major challenge. Among data teams, 70% say they manage between 5 and 10 tools daily, and 85% cite integrating tools across the stack as one of their top three challenges. More than 40% spend one third of their time simply switching between platforms, while inconsistent outputs from disconnected systems further undermine trust in the data.

“Traditional stacks weren’t designed for this new reality,” said Animesh Kumar, CTO and Co-founder of The Modern Data Company. “AI needs data that is not only available but enriched with context, lineage, and governance from the start. That’s why we built DataOS as an operating system for data – a unified layer that abstracts integrations, automates orchestration, and embeds observability and metadata at its core. It ensures that data arrives business-ready, trustworthy, and immediately usable for both analytics and AI.”

The competitive pressures around AI adoption expose fundamental gaps in how data infrastructure serves AI initiatives. While current data stacks focus on storage and movement, AI demands data that arrives with business context, built-in governance, and productized formats ready for model training and inference.

The survey reveals that current fragmented approaches require teams to manually add context, retrofit governance, and constantly reformat data for AI use cases—creating bottlenecks that make rapid AI deployment nearly impossible. 65% of respondents believe that combining strong data models with effective data products, packaged as business-ready assets, is essential for compressing time to insight and restoring user confidence.

"Gartner research shows that there’s a shift towards converged data management platforms which deliver pre-integrated data, analytics and AI capabilities," said Srujan Akula. "This is exactly what we built DataOS to address—the need to move beyond fragmented tool sprawl toward a platform that scales AI through business-ready data products with governance and context built in from day one. DataOS puts high-quality, contextualized data directly in the hands of business users, enabling faster decision making and meaningful business impact at scale."

The 2024-25 Modern Data Report was conducted by The Modern Data Company and Modern Data 101, a community and content platform for people building data platforms and designing data teams. In late 2024, they surveyed 232 senior data professionals across 48 countries with an average of over 15 years of experience, representing diverse industries including financial services, technology, healthcare, manufacturing, and government.

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Download the full 2024-25 Modern Data Report.

About The Modern Data Company

The Modern Data Company is redefining enterprise data management for the AI era, transforming data from a technical challenge into an organizations' most powerful business asset. The company's flagship platform, DataOS, serves as the essential AI-ready data layer for any data stack. DataOS transforms fragmented, ungoverned data infrastructure into AI-ready data products in just weeks, delivering trusted, context-aware data that makes AI systems smarter while providing native integration support including built-in APIs and GraphQL for seamless connectivity to existing tools, workflows, and LLMs. 

Fortune 1000+ enterprises using DataOS are accelerating their AI adoption by 90% while reducing total cost of data ownership by up to 50%, delivering dramatic increases in business agility. The company's rapidly expanding customer base includes global category leaders across multiple industries who trust DataOS to power their AI and business transformation.