Data portability: Freedom and AI-ready infrastructure

Data portability is the ability to move data freely between systems. For individuals, that means being able to receive their personal data under regulations like GDPR Article 20. For enterprises, it’s the freedom to migrate information across vendors without being locked in.

Portability is built into many of the infrastructural systems that businesses use every single day, so its value often becomes clear only when it’s gone. Without data portability, you lose the leverage to negotiate with cloud data providers, can’t use AI agents across all data sources, and might struggle to effectively comply with governance and regulatory requirements.

This article will demonstrate how vital portability is in modern enterprise architecture and how using Open Data Infrastructure (ODI) provides the baseline flexibility for AI and interoperability. 

Why data portability matters

Data portability is about freedom. It gives you full control over how you store or move your data. At an enterprise level, it allows you to work flexibly across different partners and systems, as your data can flow through apps regardless of which vendor owns them.

Here are the main benefits of data portability:

• Negotiating leverage: Data portability gives you flexibility with data storage and transfer. Without portability, your data might be locked into a proprietary storage format that limits what you can do with it. Switching costs are higher because the cloud storage vendor controls your data and, therefore, all the negotiating power.
• AI readiness: AI agents need broad access to organizational data, from internal documentation to SaaS apps. Portability makes sure no single vendor dictates how to share data with AI tools, allowing your AI systems to operate across multiple sources.
• Regulatory compliance: The GDPR and other emerging data sovereignty laws increasingly require businesses to demonstrate portability. Partnering with companies that don’t offer flexible formats may put you in breach of compliance obligations.
• Business continuity: When a vendor stores your data in a proprietary format, they control your access to it. If their system experiences an outage, your data is trapped until it comes back online. Portable formats allow data transfer across systems and vendors, building resilience.

What limits data portability?

The main limitations to data portability come from architectural choices that businesses make. Vendors can try to monopolize access to your data and reduce your control by using incompatible data formats or placing heavy fees on data egress.

Here are four common factors that limit data portability:

• Proprietary formats: When companies store data in vendor-specific schemas, you must convert it to a usable format before migration. This additional step slows down migration while creating the potential for data loss.
• API restrictions and egress fees: Some platforms allow a wide range of data formats but place hard limits on APIs in the form of limited requests or egress fees. If a platform charges egress fees that make data export economically prohibitive, they’re actively removing portability.
• Export limitations: Limited export formats may break schema structure or obscure relationships. Ensure your exports can handle metadata and carry the same structure as the original data files to avoid incomplete data.
• Contractual barriers: Some vendors prevent portability by writing data export restrictions into business contracts. Watch out for contracts that explicitly limit your data export rights or impose minimum commitments that make switching difficult.

How Open Data Infrastructure enables data portability

ODI is a foundational principle in design that builds portability into the fabric of data architecture. Beyond just technical specifications, it includes several approaches that embrace flexibility and interoperability.

ODI ensures portability across each of its four main pillars:

• Ingestion layer: Vendor-neutral connectors capture data from sources directly into open formats, removing the possibility of lock-in at the ingestion stage.
• Storage layer: Data is stored in open table formats (such as Apache Iceberg™ or Delta Lake) on object storage (S3, ADLS, or GCS). These formats ensure it’s readable by any other compliant compute engine and avoid the need for vendor-specific conversions.
• Transformation layer: Portable transformation frameworks (such as dbt, SQLMesh, and native SQL) use open transformation logic that can run across warehouses and lakehouses without relying on proprietary DSLs or vendor-specific languages.
• Governance and semantics: Standards-based governance and semantic layers keep your data accessible across all connected tools.

By building your data architecture around the principles of ODI, you make portability a structural element of your ecosystem.

How Fivetran supports your data portability strategy

Fivetran maximizes data portability at the ingestion and storage layers, offering your organization complete flexibility throughout the data stack. With over 750 pre-built source connectors to choose from and a Connector SDK for hard-to-reach systems, you have the ability to access and ingest data from all your business systems.

By building with open table formats on customer-controlled object storage, Fivetran’s Managed Data Lake Service ensures your data remains readable by Trino, Snowflake, Flink, Spark, or any other downstream compute engines without the need for vendor-specific adapters or conversions.

With Fivetran, you get fully automated, reliable data ingestion while retaining full control over how you store, access, interact with, and migrate data. Request a demo to get started today.

FAQ

What is the difference between data portability and data interoperability?

Portability answers the question “Can I leave this data ecosystem?” while interoperability answers the question “Can I use multiple tools on the same data?” They’re both about data freedom and flexibility but approach them from different angles.

How does data portability affect AI agent performance and cost?

Data architecture that doesn’t follow the principles of ODI is typically more costly, because you have to follow the specific rules and regulations that a provider sets for you. You may experience restrictions on APIs or have to build custom workflows to transform data from their proprietary format into a usable one, incurring additional costs for your business.

Is data portability in the GDPR?

Article 20 in the GDPR specifically explores the “right to data portability,” documenting the obligation for your business to allow users to receive and access their personal data.

How does data portability affect cloud migration and multi-cloud strategy?

Data portability lays the foundation for a multi-cloud strategy, as it allows you to move data from across systems without barriers. Without cloud data portability, you’d have to build out additional transformation and migration workflows to get data from one vendor to another. Data portability workflows are vital for multi-cloud approaches.

Apache Iceberg is a trademark of the Apache Software Foundation.

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