6 ways Open Data Infrastructure accelerates your workflows

We’ve entered a new phase of data engineering and analytics, where data will be used by agents as well as humans. Unlike humans, who learn through training, experience, observation, and self-directed learning, agents require explicit, comprehensive context to reason correctly. To then power agents, data engineering teams require access to more datasets than ever before. But existing data infrastructures create workflow friction, making it hard to scale while controlling costs and ensuring data reliability and security.

Open Data Infrastructure (ODI) is an open, standards-based architecture for control, interoperability, and AI-ready data. By using open formats and shared standards, organizations can introduce new workloads, including AI, without repeatedly copying data or rebuilding their foundations. Below are 6ways ODI can make your life easier.

1. Access your data on your terms 

A lot of "data friction" isn't really technical; it's contractual. Software vendors increasingly gate access to your data behind premium API tiers, per-call pricing, aggressive rate limits, or terms of service that restrict exports and AI use. You technically own the data, but the vendor decides how affordable and practical it is to actually use. Recent policy changes at Salesforce, SAP, Slack, Workday, and others have all made it harder or more expensive to move data out of their platforms — locking your data in a walled garden. 

For AI to be valuable, it needs to access more systems, and do so more frequently. Walled gardens make data movement harder, and AI projects more expensive and difficult. ODI addresses this by making open data access a requirement of the architecture, and increasingly, a requirement you can build into vendor contracts. Instead of asking permission every time a new use case comes up, you get to keep building on your own terms.

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2. Scale infrastructure while controlling costs

Traditional architecture forced an uncomfortable choice: warehouses were reliable but expensive to scale because compute and storage were bundled together, and lakes were cheap but harder to govern for serious analytical work.

Open table formats close that gap. They bring schema enforcement and transactional reliability to lake-based storage, so you get warehouse-grade dependability on commodity-priced infrastructure. Storage stays cheap — you store data once in object storage such as S3, Azure Data Lake Storage, or Google Cloud Storage, written in open table formats like Apache Iceberg* or Delta Lake. Then, you choose the best compute engine for the given workload, such as one for BI, another for data science, another for operational apps, without duplicating the underlying data for each. 

3. Introduce or swap tools more easily 

In a tightly coupled stack, changing a compute engine, a BI tool, or an orchestration layer often means touching everything around it, requiring significant time and resources. 

When storage, compute, and metadata are decoupled and standardized, you get to swap in a better engine, negotiate harder on price, or adopt a new AI framework without rebuilding your foundation. You get to introduce new tools and technologies to experiment, iterate and adopt new capabilities, and pick the most cost-effective option for each job. Instead of forcing every workflow to conform to a particular platform, your infrastructure meets your current needs while keeping options open for the future.

A Senior Software Development Manager at Shutterstock shared how powerful this value proposition was to their business: "An Open Data Infrastructure gives us the flexibility to use the right tool for the job. Whether it's Snowflake, AWS services, or something new in the future, our data is already where it needs to be without having to move or rebuild."

4. Reduce duplicate pipelines and reconciliation

Dashboards, operational workflows, and AI agents all draw from the same underlying data, but in a typical stack, each gets its own copy with its own definitions. When those definitions drift, you see misaligned decisions and unreliable AI outputs. When every tool maintains its own copy of the data, tracing a bad number or a hallucinated AI output means precious time and resources spent reconciling several different systems before you even find the source of the problem.

ODI treats the data layer as a shared foundation, creating an interoperable infrastructure. At the core of that is a data lake, where you move data once and query as needed. When data is stored in a data lake in open formats and governed through shared standards, it becomes easier to share data across business units, support integrations, and change downstream tools without forcing data teams to duplicate data or pipelines. Every system works from the same consistent view of the business. This eliminates the constant, low-grade work of maintaining parallel pipelines and chasing down data discrepancies, saving data team time and resources.

5. Reduce investigation with shared lineage and definitions

A column named "revenue" doesn't tell an agent whether it includes refunds or taxes. A human analyst fills that gap with tribal knowledge while an agent just guesses. A confident wrong answer is often worse than no answer at all. 

In an ODI, you build these definitions into the metadata layer of your data lake, enabling policy enforcement, lineage, and business definitions that remain consistent across analytics, operations, and AI systems. Confidence in the data is high, and data teams aren’t scrambling to reconstruct context instead of delivering analysis and data products.

Our Agents Schema simplifies this process by designating a shared, governed schema where metric definitions, lineage, and business documentation live in plain SQL tables that any agent can query before it acts. When an agent is asked something like "what's our MRR this month," it looks up the approved definition and lineage first, rather than inventing its own logic. When a definition changes, it changes once, at the source, and every agent and analyst downstream sees the update.

6. Standardize security to accelerate audits and approvals

"Open" tends to make security teams nervous because the connotation is “exposed.” But in an ODI, openness refers to committing to widely adopted standards, not loosening controls — it’s about making data available to the right systems under the right controls. Access policies, classifications, and lineage travel with the data itself, so a new pipeline, application, or agent inherits the existing security model instead of needing a new one built from scratch.

That matters more as AI agents start acting autonomously and agent-driven interactions increase. By grounding data and semantics in open standards, within a unified storage layer, ODI ensures that agents access consistent, trusted data through controlled pathways instead of brittle integrations. Policies are applied across the infrastructure, not recreated for every workload, granting security teams full visibility into how data actually moves, which makes audits faster and approvals easier to grant.

To see how close your stack is to an Open Data Infrastructure and get tailored next steps to build your foundation for AI at scale, take our ODI assessment.

*Apache Iceberg is a trademark of the Apache Software Foundation.

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