Is your data strategy ready for AI?

Download the State of Data Intelligence snapshot to see how organizations are preparing for AI governance:

Benchmark maturity against your peers
Adopt ethical frameworks for data use
Harden risk controls around privacy and audit
Invest in trust tools for transparency

Then explore the four stages of data and AI readiness—and identify where your organization stands.

Download the Report

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Where are you on your AI readiness journey?

From foundational data modeling to enterprise-wide intelligence, erwin® by Quest® helps you govern, align and prepare – wherever you are.

FAQ

We’re building or modernizing our data platform - how do we know we’re doing it right?

Ask yourself:

Are our pipelines scalable, documented, and resilient?
Are we using data models to guide how we integrate and migrate?
Can we easily onboard new sources - or is it still manual and messy?

These are signs you’re in Phase 2: Platform Buildout, and investing now will unlock faster AI and analytics later

We’ve launched AI pilots, but scaling is a challenge. What might be missing?

Ask yourself:

Can our AI teams find, trust, and reuse the right datasets?
Do we have lineage and governance to support safe experimentation?
Is there a disconnect between the models we build and the value they deliver?

You’re in Phase 3: AI Enablement, but progress may be blocked by friction from earlier phases.

We want to explore adaptive / autonomous systems / Agentic AI - how do we prepare?

Ask yourself:

Can we monitor how data changes in real time - and act on it automatically?
Do we have policies in place to safely guide AI agents?
Are our systems designed to evolve, or stuck in manual cycles?

This is the ambition of Phase 4: Agentic AI—but success depends on strong foundations in data quality, governance and observability.

How do I know if we need to invest in data modeling or governance?

If you hear phrases like “We don’t trust the numbers” or “It depends who you ask”, you likely lack a shared understanding of your data. We help align business and technical teams with structured modeling, glossary, and stewardship foundations.

We’re modernizing our data platform. Where do we start?

Start with structure - not storage. Before you migrate to a data lakehouse or modern pipeline, we help you model your data, map dependencies and build a clear architecture that scales with you.

How do I get trusted data into the hands of more decision makers?

It starts with visibility and trust. We help you catalog, model and govern your data so it’s not only findable - but reliable and actionable. When data is trusted, teams use it. When it’s accessible, they innovate with it.

Because data intelligence isn’t just about control - it’s about confidence.

We’ve already invested in a dozen tools. Do we really need data modeling too?

If you're constantly firefighting, the problem might not be your tools – it’s people and/or process. It's how you structure and align them. Data modeling is like DevOps for data: a foundational practice that reduces rework, improves quality and creates clarity across teams. It's not more work.

It's work that makes everything else easier.