ODI ASSESSMENT
Is your data stack ready for AI?
See how close your stack is to an Open Data Infrastructure and get tailored next steps for building your foundation for AI at scale.
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Which data platforms are you currently using?
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Where does your data primarily land?
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What data ingestion tool(s) do you currently use?
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How do you primarily handle data modeling and transformation?
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What are your primary table/file formats?
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Where is your organization in your AI/ML journey?
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How do you manage metadata and context for AI systems?
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What are your top data infrastructure priorities?
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YOUR ODI SCORE:
Your stack is AI-blocked. AI agents need a foundation built for how they actually work.
Agents are continuous, autonomous, and unforgiving. Without a trusted source of data and context in place, the simplest AI use cases require expensive workarounds, and the more ambitious ones aren't possible at all.
YOUR ODI SCORE:
Your stack is AI-constrained. AI agents don't behave like human analysts, and the gap matters.
Agents consume data continuously, expect it to be consistent across every system, and amplify any inefficiency around them. Building real AI on top of infrastructure designed for human analytics means every initiative pays a tax in cost, in time, and in workarounds.
YOUR ODI SCORE:
Your stack is AI-approaching. You’re close, but agents are unforgiving data consumers.
The distance between where you are now and where AI agents need you to be isn't cosmetic. Every quarter that goes by without closing the gap makes future AI initiatives more expensive to build, and the organizations that don't get this right end up shelving AI initiatives instead of shipping them.
YOUR ODI SCORE:
Your stack is AI-ready. You’re ahead of the curve, but AI doesn’t stand still.
You've built the foundation that lets AI agents work the way they're designed to: continuously, autonomously, at scale. But as traffic grows from hundreds of human queries per day to thousands of agent queries, even the strongest positions face new demands.
Architecture snapshot
Your current stack
Movement
Unknown
Storage
Unknown
Storage format
Unknown
Compute
Unknown
Transformation
Unknown
Context
Unknown
gap analysis
Where can you level up
Data lake foundation
What to do
Establish a unified data lake layer as the foundation of your data architecture.
Why it matters
Centralizing data in object storage decouples it from any single compute engine, giving you the freedom to optimize compute for each workload, save on compounding cost, and prevent lock-in.
Lake-first destination
What to do
Land your data in the lake first, then route to specialized engines as needed.
Why it matters
Landing data in your lake first ensures every downstream consumer works from the same trusted source. It eliminates unnecessary duplication - reducing costs, compliance risks, and future-proofs your architecture.
Open table formats
What to do
Adopt an open table format for the data in your lake.
Why it matters
Open table formats let multiple compute engines read the same dataset directly — no copies, no proprietary translation, no per-engine ingest charges. They also keep you free to swap or add engines without re-platforming.
Portable transformation
What to do
Move modeling and transformation logic into a framework that isn’t bound to a single compute engine.
Why it matters
A portable transformation layer built on open standards keeps your business definitions stable while the tooling around them evolves, eliminating one of the largest hidden costs of stack changes.
Semantic layer
What to do
Implement a reusable semantic layer that defines core business entities and metrics.
Why it matters
A semantic layer ensures every model, dashboard, and agent works from the same definition of customer, revenue, churn, and everything else. It's the difference between AI that drives value and AI that erodes trust.
Automated, managed data lake ingestion
What to do
Adopt a managed ingestion platform that lands data in your lake in open table formats.
Why it matters
Automated, managed ingestion that delivers data to the lake in open table formats redirects engineering capacity to higher-value work and ensures your lake stays current for AI agents that need fresh data, not yesterday's data.
Governance for agents
What to do
Strengthen governance, access controls, and lineage to support autonomous agents.
Why it matters
Building fine-grained access, audit trails, and lineage now is materially cheaper than retrofitting them after a compliance incident, and it's a prerequisite for putting AI in any decision that affects customers, finances, or regulated data.
Data productization
What to do
Productize critical data sets built to be consumed by AI systems.
Why it matters
Data products turn your foundation into leverage, letting teams independently test new AI use cases in days instead of quarters, without handoffs slowing them down.
Operational AI
What to do
Move beyond AI for insights and start using it to drive operational workflows.
Why it matters
AI that takes action directly in operational workflows compounds value continuously and at machine speed. This is where AI investments shift from a cost center to a revenue lever.
Opportunities
What ODI unlocks for you
High Impact
Reduce ingest costs
Organizations that leverage a data lake foundation for interoperable storage reduce ingest compute costs by 77-95%, compared to ingesting into a data warehouse — while eliminating the risk of vendor lock-in.
High Impact
Manage one copy of data
When data lands in the lake first, every compute engine, agent, and analyst reads from the same copy, eliminating the hidden cost of duplicated data and compliance complexity of reconciling data across systems.
High Impact
Optimize your compute strategy
Open table formats like Apache Iceberg or Delta Lake make data readable by Snowflake, Databricks, BigQuery, and any other compatible engine simultaneously, with no need for translation and allowing you to choose the best engine for each job.
High Impact
Build logic that that evolves with business needs
An open-standard transformation layer means your business definitions survive warehouse migrations and multi-engine expansions without rewrites, refocusing engineering efforts on building and innovation.
High Impact
Turn data into trusted context
A holistic semantic layer travels with your data and is accessible by all data consumers. Agents query only pre-approved, governed metrics, and flag undefined terms rather than filling in the blanks in the absence of complete context.
High Impact
Let Fivetran manage your data lake
Fivetran’s Managed Data Lake Service incurs the compute cost of ingestion on your behalf and automatically converts your data into open table formats, with no manual maintenance required on your end.
High Impact
Build in trust before problems surface
An Open Data Infrastructure means policy enforcement, lineage, and business definitions carry consistently across every system. You control your data, context, and access to empower agents and humans alike to be data-driven.
Medium Impact
Ship new AI without starting from scratch
Documented, tested, and reusable data assets prevent rebuilds and ensure each new AI use case expands on what's already been built, compounding value rather than engineering time.
Medium Impact
Turn AI into a revenue driver
When ingestion, enrichment, transformation, and activation run as a connected flow rather than separate projects, the same infrastructure powering your analytics can start driving AI operational outcomes at machine speed.
BUSINESS IMPACT
What ODI means for your priorities
Accelerate AI and ML initiatives
A consistent, governed data foundation is what separates AI that ships from AI that stalls.
Avoid vendor lock-in
Open standards for storage, transformation, and data movement keep your architecture portable no matter what vendors do next.
Improve data reliability and trust
Define business metrics once so every downstream system — dashboards, agents, and apps — inherits the same version of the truth.
Reduce data infrastructure costs
Storing data once in a lake and letting any engine query it directly eliminates duplicate ingest costs, especially as data volumes scale for AI.
Scale operations without scaling headcount
Managed ingestion and automated transformation replace manual pipeline maintenance with infrastructure that runs itself.
Strengthen governance and compliance
Lineage, access controls, and audit trails built into the foundation let AI operate autonomously without creating compliance blind spots.
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