Lakehouses solved a lot of enterprise problems by unifying and simplifying data storage. But the operating landscape at the enterprise level has shifted. Today, organizations are coordinating more tools, managing more data, operationalizing AI, and navigating increasing regulatory scrutiny.
As a result, data can no longer be treated as something that’s queried occasionally or in isolation. It now needs to be operational—meaning ready for real-time use, automated decision-making, and AI-driven workflows across the organization. This shift is pushing architectures beyond lakehouses and toward a more dynamic data intelligence platform.
What Changed? Analytics Became Multi-Platform
Modern enterprises rely on multiple analytics platforms to support a wide range of workloads, including business intelligence and reporting, real-time analytics, observability, machine learning, and AI.
Each team brings its own needs to the same data, and in practice, platform choices are driven by productivity and speed rather than architectural purity. Much of that data also remains on premises or in regulated environments, where moving it to the cloud isn’t practical or permitted.
The original lakehouse model assumed convergence on a small number of analytics platforms. Reality proved otherwise: tools, users, and workloads diverged. The challenge now is supporting that diversity without sacrificing consistency or control.
The Cost of Treating Data as Platform-Owned
Despite lakehouse implementations, enterprise data often remains tightly coupled to the platform that manages it. When another platform needs access, the data is often copied, transformed, or exported to fit that environment.
Over time, simply keeping data consistent and accessible across these various platforms becomes a challenge. Duplicate datasets, fragile pipelines, delayed insights, and inconsistent governance introduce operational risk and drive up costs.
The result is a familiar pattern: rising spend, growing complexity, and declining trust in the data and its outputs.
From Lakehouse to Intelligence Infrastructure
The lakehouse helped bring structure to a fragmented analytics landscape, making it easier for data systems to work together. As enterprises move into the era of full-scale data intelligence platforms, the focus changes.
Instead of data being shaped and owned by individual tools, it becomes the foundation of the architecture—anywhere that data physically resides. All tools sit on top of a shared data layer, rather than pulling data into isolated environments and producing siloed outputs.
This shift allows teams to choose the right compute engine for each workload—whether it’s SQL analytics, large-scale processing, or AI—confident they’re operating on the same governed, trusted data foundation.
What is a Data Intelligence Platform?
A data intelligence platform is a shared infrastructure for data. Think of it like city infrastructure—the roads, power lines, and plumbing beneath a city that every building taps into and relies on.
In the same way, a data intelligence platform provides a centralized foundation that powers many different tools, compute engines, and applications, with governance and context embedded by design rather than bolted on later.
It’s characterized by:
A shared data layer built on open data formats
Rich metadata lineage that captures structure, meaning, and history
Built-in governance that travels with the data
Support for multiple analytics and AI engines
The ability to evolve without re-architecting from scratch
Open Foundations Make Data Intelligence Possible
A platform like this only works if data can be shared safely across all tools and environments, whether on premises, in the cloud, at the edge, or a combination. Open table formats are the common foundation that makes cross-engine interoperability possible (to continue with our city metaphor: the building codes and street standards that make the city navigable by everyone).
Without them, connecting tools often means dealing with mismatched formats, inconsistent latencies, proprietary lock-in, or data that must be governed across geographic boundaries. This can lead to familiar pain points: reduced auditability, inconsistent views of data, and growing challenges around trust.
By contrast, open formats reduce lock-in and support a growing ecosystem of tools (i.e., set it up once and let it grow with your tech stack over time). They make it easier to define governance policies once and enforce them everywhere (including where data can’t easily move), regardless of which engine needs access. This also creates a consistent “memory layer” for AI-driven systems, making them more reliable, auditable, and adaptable through built-in traceability and historical context.
Without open formats and embedded governance, intelligence quickly fragments back into silos, eroding the very advantages data intelligence platforms are designed to deliver.
Want to see what a data intelligence platform looks like in practice?
See how Iceberg tables managed by Cloudera can be queried by Snowflake and Databricks without copying data or compromising governance.
How to Shift to an Intelligence-First Platform
Adopting an intelligence platform represents a fundamental shift not just in infrastructure, but in how organizations think about and trust their data. The transition period is especially critical because it sets the expectations for reliability, integration, and adoption across teams. Early missteps can create lingering challenges and resistance to longer-term adoption.
Done well, this shift balances stability and progress, keeping mission-critical processes running while delivering early wins that build confidence and momentum.
Cloudera’s Professional Services & Transformation (PS&T) team helps organizations navigate this shift with care—avoiding common architectural pitfalls and building a durable foundation that supports future analytics and AI use cases.
Learn more about our PS&T capabilities here.
