Zero-Copy Data Sharing and the Future of Enterprise Analytics

For most of my career, enterprise analytics has depended on copying data. Information was extracted from operational systems, transformed, and loaded into warehouses, then often copied again into marts, lakes, and departmental tools. Each copy added cost, delay, and the risk of inconsistency. Zero-copy data sharing offers a fundamentally different model, and I believe it will reshape enterprise analytics over the coming years.

What Zero-Copy Means

Zero-copy data sharing allows one platform to query data held in another without physically duplicating it. Rather than moving data to where analysis happens, the analysis reaches the data where it already resides. Modern data platforms increasingly support open table formats and sharing protocols that make this possible across vendors and environments.

Why It Matters

The benefits are considerable. Eliminating redundant copies reduces storage and pipeline costs. Because data is accessed at its source, analytics reflect current information rather than last night’s extract. Access controls defined at the source can be honored consistently, reducing the security exposure created by scattered copies. And data teams spend less of their time maintaining fragile pipelines.

Perhaps most significantly for AI, zero-copy sharing can preserve the business context of source data, particularly when paired with semantic models. An AI system querying ERP data in place, together with its definitions, can reason about it far more accurately than one working from a stripped-down extract.

Practical Considerations

Zero-copy is not appropriate for every scenario. Some workloads require data to be reshaped or aggregated for performance. Queries across platforms must be designed carefully to avoid latency. In my experience, the most effective architectures use zero-copy as the default and reserve physical replication for cases where it is clearly justified.

The Bottom Line

Zero-copy data sharing changes the economics and reliability of enterprise analytics by bringing analysis to the data rather than the reverse. Enterprises that adopt it will reduce cost, improve timeliness, and give AI more accurate access to business context. I would advise data leaders to evaluate their current pipelines with a simple question: which of these copies truly needs to exist? The answer is often fewer than expected, and each one retired is a step toward a simpler, faster data estate.