Published
Jun 12, 2026
8 Pages
Open Data Lakehouses Are Now the AI-Ready Enterprise Standard
Fragmented, siloed data is the single biggest obstacle to enterprise AI and the open data lakehouse is the architecture built to solve it. This ISG Research white paper, sponsored by Qlik, examines how Apache Iceberg-based lakehouses unify batch, streaming, and unstructured data into a single governed platform that powers both BI and AI at scale. For data leaders evaluating their next architecture investment, this is the evidence-based case for making the move.
Qlik
Technology
AUTHOR
ISG Software Research
AUTHORED YEAR
2025
AUDIENCE
Data Architects & IT Leaders
INDUSTRY
Technology (Data Analytics)
TOPIC
Open Data Lakehouse, Apache Iceberg & BI/AI
White Paper Snapshot
Everything you need to know in under 30 seconds
Results & Impact
75% More Analytical Confidence
Enterprises using data lakehouses with open table formats are 28 percentage points more confident in their ability to analyze large volumes of structured and unstructured data compared to those without, a measurable competitive edge documented by ISG Research.
One Platform, Multiple Engines
Apache Iceberg's open architecture lets enterprises access the same data with multiple analytics engines simultaneously, eliminating vendor lock-in and enabling BI and AI initiatives to run in parallel without duplicating storage or pipelines.
Highlights
Data Lakehouse Success Requires More Than the Right Format
75% of enterprises using lakehouses with open table formats are confident in their ability to analyze large volumes of structured and unstructured data, compared to just 47% of those not using open formats, a 28-point confidence gap that underscores the architectural advantage.
AI raises the governance stakes significantly: Enterprise AI demands clean, well-organized, continuously refreshed data accessible to training frameworks, AI agents, and LLMs. Many organizations currently operate with fragmented, inconsistent data that falls short of these requirements.
High-throughput ingestion is not natively supported by open table formats: Data teams must add tooling for automatic schema evolution, concurrent multi-table commits, compute scaling, and data quality enforcement, all prerequisites for reliable lakehouse operations that go beyond what Iceberg provides out of the box.
Managed cloud services are the practical path forward: The cost and complexity of self-managed lakehouse environments has pushed enterprises toward cloud-managed offerings that bundle ingestion, pipeline management, table optimization, governance, lineage, and query tuning, accelerating time to value without the operational overhead.
Key Topics
Apache Iceberg Is Becoming the Universal Standard for Data Lakehouses
80%+ of enterprises using data lakehouses will adopt open table formats by 2027, according to ISG Research, driven by the need for ACID transaction support and CRUD operations on data stored in cloud object storage.
Apache Iceberg is pulling ahead of competing formats: The Iceberg REST Catalog provides a common API across all compatible implementations, enabling governance and access controls for diverse processing engines, a key reason platform providers are coalescing around it over Apache Hudi and Delta Lake.
Store data once, analyze it everywhere: Open table formats allow enterprises to use the same data across multiple analytics engines, Apache Spark, Snowflake, Databricks, Amazon Athena, Trino, and more, eliminating vendor lock-in for both compute and storage.
Interoperability efforts are reducing fragmentation risk: Recent work to enable Apache Hudi and Delta Lake to operate alongside Apache Iceberg lowers the barrier to adoption and positions Iceberg as the lingua franca for transactional and event data across enterprise lakehouse environments.