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. Complete the assessment for a chance to win a $500 gift card.

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ODI ASSESSMENT

Where does your data primarily land?

Select all that apply

What are your primary table/file formats?

Select all that apply

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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.

00
out of 100
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.

00
out of 100
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 it makes future AI initiatives more expensive to build, and the ones that don't get this right end up shelved instead of shipped.

00
out of 100
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 agent traffic grows from hundreds of human queries per day to thousands, even the strongest positions face new demands.

00
out of 100
opportunity 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 test new AI use cases in days instead of quarters, and freeing your data engineers from being the bottleneck on every experiment.

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.
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