Open Data Infrastructure
A CDO's Guide to Open Data Infrastructure
A board-ready ODI guide for CDOs: data control, AI readiness, platform economics, and the first moves that reduce lock-in.
The Chief Data Officer owns a problem that rarely shows up cleanly on an org chart: the company wants more value from data, but the data itself is often trapped in systems the business can barely change.
The CDO owns the control question
A CDO can delegate pipelines, dashboards, catalogs, and governance workflows. They can't delegate the control question. Who can use the data? Who explains it? Who can move it? Who pays when the architecture says no?
Open data infrastructure gives the CDO a cleaner executive story. It says data value depends on customer control across storage, metadata, access, governance, and AI context. That is different from saying "we bought a modern platform." Buying a platform is easy. Keeping options open after the platform becomes business-critical is the hard part.
Core idea: the CDO's job is not to make every tool open. It is to make sure the company's most important data contracts can survive tool change, model change, and vendor change.
The board narrative is risk plus option value
The board does not need a lecture on table formats. It needs a business narrative that connects data control to risk, cost, and AI speed.
Closed data infrastructure creates hidden concentration risk. One vendor boundary can decide which engines can query data, which policies are enforceable, which metadata can travel, and how expensive an exit becomes. AI makes that worse because agents, retrieval systems, and context services need governed access across many data products, not a weekly extract from the one blessed platform.
The CDO should frame ODI as option value. Open formats, open catalogs, portable lineage, and explicit policy contracts keep the company from turning every future data initiative into a migration project (fancy word for expensive apology tour).
Measure the architecture, not the slogans
The useful metrics are not vanity adoption numbers. They are control metrics.
- How many critical data products can be read by more than one engine without copying the data?
- How many governed datasets have machine-readable ownership, freshness, quality, and lineage?
- How many AI use cases can retrieve data through an audited access path?
- How many vendor exits have a documented path with real costs and sequence?
Those measures turn openness from marketing language into operating evidence. They also give platform teams a way to prioritize work that would otherwise look like plumbing.
Start with one valuable path
The first move is not a giant platform rewrite. Start with one high-value data product that matters to analytics and AI. Put it on an open table format when that makes sense. Register it in a catalog that exposes useful metadata. Attach lineage and ownership. Make access narrow, logged, and testable.
Then ask the uncomfortable question: could another engine, workflow, or agent use this data without a side deal? If the answer is yes, you have a pattern. If the answer is no, you have a dependency map.
The CDO does not need to own every implementation detail. They do need to own the standard for what good looks like.
Sources to start with
These are the primary sources I would start from when checking the claims in this piece.