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Presented by Google Cloud
Data governance once felt like a compliance burden, tucked away in the back office. Today, it’s the bedrock for enterprises scaling AI responsibly and unlocking its true value. As companies race to deploy agentic AI, CIOs and data leaders face a critical mandate: deliver governed, trusted data that AI systems can understand.
This moment demands a shift in mindset. Governing data can no longer be an afterthought or a bottleneck. It must become an active contract layer that provides context, trust and traceability for every application and autonomous system. When done right, governance transforms scattered data into reliable data products, ready for an AI-driven future.
How data governance has evolved
The idea of governance has existed for decades, rooted in cataloging assets, tracking where data came from and controlling who could see it. In the early days of business intelligence, these tasks were mostly static and handled at a manageable scale. Reports refreshed overnight, and a small group of analysts made sense of the results.
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Today, AI has changed everything. Lineage, access enforcement and cataloging must operate in real time and cover vastly more data types and sources. Models consume data continuously and make decisions instantly, raising the stakes for mistakes or gaps in oversight. What used to be a once-a-week check is now an always-on discipline. This transformation has turned data governance from a checklist into a living system that protects quality and trust at scale.
Why cataloguing is evolving too
As the volume, variety and velocity of data continue to grow, the traditional model of a static catalog with fixed semantics and passive metadata no longer meets the demands of modern AI use cases. What once served business intelligence needs now limits the ability to scale autonomous decision-making.
CIOs and CDOs need to rethink the catalog as a living system. That means supporting structured and unstructured data, updating continuously, and operating more like a knowledge graph than a lookup table. It must power AI-assisted workflows across the governance lifecycle, capturing not just what data exists but how it is used. In this new model, the catalog is not just for visibility. It is the engine for context and trust at machine speed.
Turning data into trusted products
Traditional data governance focused on organizing structured tables, reports and compliance rules in silos. In a world where AI acts on real-time data, that old approach falls short. Agentic systems need more than raw access. They need clear semantics, guaranteed freshness and defined usage rights. This is why leading organizations now design their architectures around logical domains and treat data as a product.
Each data product comes with a clear contract that defines what it represents, how current it is, who can access it and under what conditions. This is what governed data really means: it’s automatically cataloged, indexed across structured and unstructured sources, has clear lineage and is protected by trusted usage policies that follow it wherever it goes. This contract-backed model mirrors how we have always handled SLAs for applications and now extends that discipline to the data itself. It gives developers, analysts and AI models a reliable source of truth they can trust.
Better data ergonomics through governance
One of the biggest misconceptions is that governance slows down innovation. In reality, good governance speeds it up. By clarifying ownership, policies and data quality from the start, teams avoid spending precious time reconciling mismatches and can focus on delivering AI that works as intended. A clear governance framework reduces unnecessary data copies, lowers regulatory risk and prevents AI from producing unpredictable results.
Getting this right also requires a culture shift. Producers and consumers alike need to see themselves as co-stewards of shared data products. Leaders like CIOs and CDOs set the standards and invest in the right technology, but people across the business keep the trust alive. This shared responsibility ensures that data stays reliable and AI stays accountable.
Governance ready for real-time AI
Enterprises deploying agentic AI cannot leave governance behind. These systems run continuously, make autonomous decisions and rely on accurate context to stay relevant. Governance must move from passive checks to an active, embedded foundation within both architecture and culture.
At Google Cloud, we continue to expand Dataplex and our Iceberg integration to help organizations govern data at scale. With open formats, trusted data products and intelligent policy enforcement, companies can finally break free from fragmented tools and deliver AI that is reliable, explainable and built for the future. Governance is not just an IT function anymore. It is the essential contract that connects your data to the full promise of AI.
Learn more about Google Cloud’s data to AI governance capabilities here.
Irina Farooq is Senior Director of Product Management, Data & Analytics, at Google Cloud.
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