In the rush to adopt AI, many enterprises are realizing a hard truth: their data isn’t ready. Despite decades of investment in data warehouses, lakes, and lakehouses, organizations still struggle to answer three foundational questions: Where is the data I need? Can I trust it? How quickly can I use it to create something valuable for my customers, my teams, or my strategy?

The problem isn’t storage or computing. It’s not even the volume or variety of data. The real issue is intelligence — or rather, the lack of it. Without a deep, dynamic understanding of the data ecosystem, no platform can truly support modern use cases, such as machine learning, real-time analytics, AI assistants, or cross-functional data collaboration.

So, what does a platform need to be considered truly “data intelligent”? Let’s break it down.

Today’s enterprise data lives across hybrid and multi-cloud environments, often fragmented across departments and tools. A modern data intelligence platform must be able to connect to this entire landscape seamlessly, without requiring all data to be moved, duplicated, or reshaped first. 

Connectivity should not be a bottleneck. It should be an enabler — real-time, non-invasive, and infrastructure-agnostic.

Without metadata, data is just noise. Data needs context — where it came from, how it’s structured, who owns it, what it means, how it’s been used. Capturing this metadata is only the first step. To be useful, it must be searchable, explorable, and actionable.

The right platform doesn’t just store metadata — it leverages it to power discovery, automation, governance, and trust.

Governance can no longer be an afterthought — or a roadblock. It must be designed into the platform itself, woven through every action and layer. 

This includes policy enforcement at the point of data access, role-based permissions, clear visibility into data usage and compliance status, and support for privacy, cybersecurity, and regulatory frameworks. Crucially, governance must be automated enough to scale and flexible enough to support different domains and use cases.

Business analysts and data scientists speak different languages. So do APIs and compliance officers. A true data intelligence platform must offer a semantic layer that makes data meaningful to both technical and non-technical users, mapping raw structure to business concepts, unifying terminology, and enabling consistency across teams.

This is the foundation for data democratization — and for building tools like AI copilots or natural language analytics.

Centralized systems can’t keep up with the speed and scale of modern data needs. That’s why more organizations are adopting data product thinking — designing discrete, domain-owned assets that are discoverable, reusable, and built for consumption. 

Data products are self-contained, governed, and versioned — just like software. However, to work effectively, they require data contracts: clearly defined agreements between producers and consumers that encompass everything from format and access methods to usage rights and quality expectations. 

This shift enables data teams to move faster, with less friction, and build trust across the data value chain.

If data is hard to find, it won’t be used — or worse, it will be duplicated and fragmented.

A modern platform must provide an enterprise data marketplace: a centralized experience for searching, previewing, and requesting access to available data products. This marketplace should feel intuitive, stay up to date, and adapt to user needs, much like a consumer app, but with enterprise controls.

Discovery is not enough. Users need to know whether the data they’ve found is reliable, high quality, and compliant.

This is where data observability becomes essential. Observability enables continuous monitoring of data as it moves and transforms — proactively measuring quality, detecting anomalies, and surfacing issues before they impact business outcomes.

The best observability is not isolated — it’s integrated with metadata, lineage, policies, and contracts. Only then can data be trusted at scale.

At Actian, we believe these aren’t nice-to-haves — they are fundamentals for any enterprise looking to succeed in the age of AI. That’s why we’re building the Actian Data Intelligence Platform — a trusted, flexible, and easy-to-use foundation that brings all of these capabilities together:

• A graph-powered metadata foundation for rich discovery and understanding

• Embedded governance by design

• Semantic modeling that bridges technical and business domains

• Data products with contracts that formalize alignment between producers and consumers 

• A user-friendly enterprise data marketplace

• Integrated data observability and lineage to ensure trust across the pipeline

We’re designing it to be future-ready — not only supporting today’s data challenges, but empowering enterprises to move confidently into AI, analytics, and whatever comes next.

👉 Curious how it all comes together?

Watch this 3-minute explainer with Ole Olesen-Bagneux to see how it works.