From Storage Problems to Query-Ready Data: The New AI Bottleneck
AI-ready data infrastructure is a modern enterprise approach that turns raw operational and unstructured data into immediately queryable, governed, and analytics-friendly formats for AI, data lakehouses, and business intelligence tools without massive data migration or complex custom pipelines. Instead of worrying about where data is stored, enterprises now worry about whether their data can be accessed, shaped, and queried fast enough to keep training and inference pipelines fed. As AI adoption accelerates, data infrastructure has become a critical bottleneck for many advertising technology organizations, where platforms process hundreds of billions of events daily across bidding systems, attribution, and reporting. The lesson is broader: storage capacity is no longer the main constraint; the real drag on AI is how far data must travel, both technically and organizationally, before it is query-ready.
AdTech as the Stress Test for AI-Ready Data Infrastructure
Advertising platforms are a live stress test for AI-ready data infrastructure, and they are exposing how fragile traditional pipelines have become. To meet real-time bidding and attribution needs, organizations have stacked ingestion frameworks, transformation pipelines, monitoring systems, and semantic tools, all just to make data usable. That complexity does not scale for AI. Eon’s AI-Ready Data Lake Infrastructure responds by automatically transforming operational cloud data into an open Iceberg-based data lake as it lands, continuously optimizing storage, validating quality, maintaining metadata, and organizing data for analytics and AI consumption. Rise, which already processes more than 200 billion events and over a petabyte of data every day, built an open, AI-ready data foundation with sub‑minute freshness, automated data quality validation, 10x lower compute costs, and immediate access for analytics and AI.
The notable shift here is philosophical as much as technical. Instead of treating data preparation as a separate, sprawling layer, Eon pushes readiness into the infrastructure itself. "Every enterprise we talk to has the same problem in a different costume. The AI initiative is funded, but the data isn’t ready or readily accessible," said Ofir Ehrlich, CEO and co‑founder of Eon. That critique is hard to ignore. It implies that any AI program built on top of brittle pipelines will eventually buckle under the weight of scale, especially in sectors like SaaS, financial services, and e‑commerce, where similar patterns are emerging.
Making Unstructured Data Query-Ready Without Moving Files
If AdTech shows how painful event-scale data can be, unstructured enterprise data shows how much value is still locked away. Although unstructured data is more than 80% of an enterprise’s data footprint, less than 1% is used in AI, according to IDC. The reasons are familiar: inconsistent schema, poor quality, and the sheer cost and delay of moving petabytes across NAS and cloud vendors for ingestion. Komprise is attacking this problem not by moving files, but by exposing query-ready unstructured data as Apache Iceberg tables. Its Transparent File Tables present a structured view of globally classified unstructured data to AI, BI, and analytics platforms such as Snowflake and Databricks. Data engineers, scientists, and analysts can query and use this data in their existing tools while avoiding massive costs for large-scale data movement.
Technically, Komprise indexes enterprise data into a Global Metadatabase, enriches it with metadata and context through AI preparation and automation services, and then exports Transparent File Tables to data lakehouses. The table holds Komprise-enriched metadata and a pointer to the data using its Transparent Move Technology, enabling access to remote data without moving the files and dynamically loading full content only when needed. When full files are required for AI, Intelligent AI Ingest moves just the needed files at twice the speed of standard transfer tools. The opinionated takeaway: in a world where enterprises are suffocating under unstructured data, the winning path is not more copying but smarter exposure—using query-ready unstructured data to power AI without dragging storage systems through endless bulk transfers.
Operational Impact: Less Movement, More AI Throughput
The practical impact of these approaches is measurable and should force a rethink of how enterprises design AI pipelines. Rise’s experience with Eon shows that automatically transforming operational data into an open, fully managed, AI-ready foundation can deliver sub‑minute freshness, automated quality checks, an order-of-magnitude drop in compute costs, and immediate access for analytics and AI, all backed by Apache Iceberg tables. From an operations standpoint, this eliminates a large chunk of the custom pipeline maintenance that usually drags data teams down. Eon argues that by embedding readiness into infrastructure, organizations reduce complexity, lower costs, and accelerate AI initiatives without rebuilding their entire data stack.
Komprise reaches a similar destination from the opposite direction: treat existing storage as a stable base, and expose unstructured assets in query-ready form to AI and enterprise data lakehouses. Its Transparent File Tables make the Global Metadatabase or subsets available to lakehouses, where enterprise data experts can create queries in Apache Iceberg using their preferred tools without direct access to Komprise. Data governance is preserved based on user permissions, and file movement is reduced to targeted, accelerated transfer only when full content is genuinely needed. In both models, eliminating indiscriminate file movement is not a side benefit; it is the core strategy that improves time‑to‑insight and AI throughput while keeping infrastructure manageable.
The Road Ahead: AI-Ready and Query-Ready by Design
These developments suggest a clear direction for enterprise AI strategies: data infrastructure must be AI-ready and query-ready by design, not by retrofitted pipeline. The fact that Eon’s AI-Ready Data Lake Infrastructure is gaining adoption in one of the most demanding data environments, and that Eon and Rise are presenting their results at Cannes Lions International Festival of Creativity this week, underscores how central data readiness has become to competitive advantage in advertising. Meanwhile, Komprise is opening early access to Transparent File Tables, signaling an intent to turn petabytes of dark unstructured data into a source of AI insight rather than a liability.
Enterprises that continue to invest mainly in larger storage arrays or isolated AI platforms will miss the point. The emerging pattern is an infrastructure layer that exposes query-ready unstructured data to AI and enterprise data lakehouses without wholesale migration, and transforms operational streams into AI-ready data foundations as they land. The opinionated conclusion is simple: the next generation of AI winners will not be those with the biggest data stores, but those with the least data movement. Making data ready where it lives—without shuffling files back and forth—is how data bottleneck elimination becomes real rather than promised.
