August 22, 2025 By: Prabhakar Jayade
Cells are the smallest unit of life that can divide, multiply, and organize themselves into tissues and organs to form the body. Similarly, Data is the smallest unit of any enterprise. However, unlike a cell, data does not organize, multiply, or classify itself autonomously. It exists across systems, awaiting someone to find, label, and govern it. This is achieved by the process of Data Discovery, through the identification, classification, and organization of dispersed information so that it can be used meaningfully for analysis, research, etc. But it’s only half the job done; assuming the security, compliance, and ethical use of the organized data is the process of Data Governance.
Both data discovery and governance are the basis of an organization’s data strategy. However, businesses often find it difficult to implement them well. The rising amount of data and increasing regulatory pressure make these challenges even tougher.
Enter Generative AI for data discovery and AI for data governance, new-age technologies transforming how organizations discover, manage, and protect their data assets.
Why Discovery and Governance Matter
A 2025 ZipDo report found that manual data cleaning and standardization can take up to 80% of a data project timeline. While poor clarity of existing data increases the risk of non-compliance with privacy laws. According to Gartner, the average cost of poor data quality to organizations is $12.9 million annually.
Currently, data governance is often achieved through manual processes, some documentation & mostly is consensus-driven. It is difficult to measure & scale in a fast-paced, dynamic, and digital environment, but the direct and indirect cost of non-compliance can be up to 19% of annual revenue, depending on the company’s size.
To tackle this, enterprises are increasingly turning to ontology-driven data strategies. An ontology is a structured framework that defines the relationship between different data, business context, or rules, enabling systems to understand the meaning behind the data. It is the semantic blueprint that aligns business terms, governance rules, and policy logic across departments. This enables centralized governance with federated ownership, allowing teams to define and maintain business, policy, & governance rules, data quality standards, and compliance prompts in a unified, interpretable framework. Importantly, it also provides the ability to generate evidence logs of compliance failures, capturing the source, owner, and the specific failed rule, giving governance real “teeth” by making accountability measurable and auditable.
More importantly, it enables the ability of cognitive analysis of data to find “semantic breaks”, the hidden breaks in meaning and context that typically create operational inefficiencies and accretion of cost. By using open-source cognitive classifiers, businesses can identify and repair semantic breaks with direct contribution to bottom-line accountability.
Pain Points in Discovery and Governance
Despite investing in tools and frameworks, many organizations face hindrances such as:
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Data dispersion across departments and systems, making discovery difficult
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Absence of metadata and data lineage makes referencing difficult, resulting in poor trust in data
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Unclean data due to duplicates, inconsistencies, and missing values contributes to poor quality
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Manual policy enforcement, which can be error-prone and time-consuming
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Low adoption of governance tools due to complexity or lack of user awareness
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Changing regulations and policies makes compliance difficult and a perpetual process
These pain points are precisely where Gen AI in data management can make a difference.
How Gen AI Can Help
Generative AI for data discovery is revolutionizing how organizations think about their data. Pairing Gen AI with underlying ontology, systems can automatically scan databases, documents, and even an email to recognize and classify data considering the context, thereby minimizing the need to manually tag, classify or find datasets. It recognizes data beyond names or numbers, understanding its meaning. For example, it can recognize that a column named “ClientID” contains sensitive information and classify it accordingly.
In addition to data discovery, an AI-based data catalog can enhance the process further. Essentially, a data catalog is an organized inventory of all enterprise data, including metadata, sensitivity labels, and usage history. This enables analysts to find the right datasets quicker, and indexing datasets ensures that only high-quality data is referenced in any reporting and decision-making process. When driven by a shared ontology, this catalog improves consistency across departments by aligning terms and classifications.
Policy enforcement and documentation can be automated by employing Gen AI for enterprise data governance. It can create draft compliance policies based on an organization’s data structure and read and interpret legal documents like GDPR and HIPAA. Additionally, it can be used to track and spot trends in data usage and highlight activities that raise privacy or security issues. Since policy updating takes place continually, automated compliance can be used as an effective time-saving mechanism. For instance, OneTrust uses agentic AI on an ontology-driven platform to interpret regulations, draft updates, and automate governance at scale.
Furthermore, organizations can employ AI in data lifecycle management, starting from data creation to deletion. Gen AI, for example, can identify duplicate copies, suggest data retention schedules, and guide the disposal of outdated records. When informed by a domain-specific ontology, these decisions are more consistent, ethical, and efficient, minimizing legal exposure while boosting operational performance.
Making It Work in the Real World
To truly realize the potential of Gen AI in data management, organizations need to prioritize focus on two areas: cultural readiness and the right infrastructure. Cultural readiness is cultivating and trusting teams in their use of AI-powered tools and their outputs for decision-making. This is probably going to require retraining, clearly communicating expectations, and thinking differently.
On the infrastructure side, it is important to consider the approaches toward the completeness of integration of Gen AI into existing systems (for example, data warehouses, CRM platforms, governance dashboards). This will make the discovery, governance, and compliance of all data touchpoints easier to handle.
Some examples of organizations already implementing are:
Amazon: Amazon’s Finance division has deployed Gen AI to auto‑generate metadata and business descriptions for thousands of datasets that previously lacked documentation. This AI-powered data catalog capability dramatically reduced time to insight and enforced consistent metadata standards across assets, boosting discoverability and trust.
Deloitte: Deloitte Australia’s MyAssist has processed over 3.65 million questions and 20 billion words, powering AI tools such as tax report summaries and audit workflows. Although broader than data governance, it demonstrates large-scale, secure AI deployment in regulated enterprise environments, with built-in compliance and governance capabilities
Strategic Edge of Gen AI in Data Management
Gen AI alone cannot guarantee semantic integrity when data volumes are expected to exceed 180 zettabytes by 2025, along with the evolution of compliance landscapes. A well-designed ontology ensures that AI powered data catalogs, governance engines, and lifecycle tools speak the same business language. Investing in a Gen AI powered solution underpinned by ontology is no longer just a technological upgrade, it’s the semantic backbone of intelligent digital transformation.
