Data governance vs data management
We recently sat in on an insightful debate at Gartner’s Data & Analytics Summit, where experts addressed the difference between data management and data governance. The discussion reinforced how often these two concepts are misunderstood.
Too many people still think of data governance purely as restrictive control or red tape. But that’s not what governance is really about.
Governance establishes business intent, whether it’s reducing costs, complying with regulations, or enabling self-serve analytics. It actually determines where democratization is encouraged, permitted, or limited.
Data management tackles the how. It handles the technical execution of governance, like enforcing data quality standards, managing delivery, and maintaining data assets.
In short, governance sets the rules, but data management makes sure they’re followed consistently.
AI is set to change how the two are done. AI-driven data management allows the automation of tedious and error prone tasks. This doesn’t just make data teams more productive, it uplifts data governance from static rules into practical, immediate actions.
The role of AI data management in modern data governance
Data management covers many areas, including data integration, data quality, metadata management, and data observability. Generative AI for data management integrates artificial intelligence into these processes, making them faster, more accurate, and less manual.
But when we talk specifically about governance, two AI-driven capabilities stand out:
- Querying metadata in natural language: Teams should be able to easily find data assets or detect governance policy violations by asking questions in plain English, rather than relying on manual queries. This saves valuable time and reduces errors from manual searching or scripting.
- Enriching metadata using AI: Rather than just static information, your metadata becomes dynamic, updated continuously by AI. For example AI can analyze code at scale to detect potential security vulnerabilities, code styling inconsistencies, or documentation inaccuracies, further enriching metadata by generating new insights about your SQL code and its documentation.
Together, these two capabilities turn metadata from something passive and that becomes outdated quickly into a live resource your teams use every day. They enable data teams to proactively spot data compliance issues and enforce policies without manual overhead, significantly improving the efficiency and accuracy of your governance practices.
Applications of AI data management in governance
Here are some practical examples of how AI- data management helps enforce governance:
1. Natural language queries to monitor compliance and flag discrepancies
A core part of data management is making it easy for teams to quickly find the information they need. When it comes to governance, that usually means being able to spot assets that aren’t following your data policies. For example, you might need to quickly identify all tables that were not created using dbt.
Without efficient search and filtering capabilities, enforcing these governance rules typically involves manually writing SQL or digging through logs and reports, which takes time and effort.
By integrating AI into your metadata management tool, you could simply ask, in natural language:
“Which highly used tables weren’t created by dbt?”
The AI translates your plain-language request into a metadata query, and your data management tool quickly flags the non-compliant assets. If you need to filter the results further, just say,
“Filter the dashboards that aren’t used by the sales team”.
This conversational approach saves hours of manual investigation, letting you refine your query until you get exactly the information that matters. It ensures your policies are actively enforced, rather than gathering dust in a document.
2. Automated code scanning to enforce governance policies
Many data teams democratize data modeling by encouraging analysts and engineers to directly contribute to dbt models. While this accelerates innovation, it also introduces the risk of contributors unintentionally breaking established governance policies.
AI-driven data management automates compliance checks by instantly scanning SQL code whenever someone submits new or modified models. It checks against your defined rules (for example, ensuring no cross joins exist in fact tables) and immediately flags any breaches. But it doesn’t stop there.
AI scanning results can feed directly into your metadata catalog, creating a new metadata property (e.g., “code_compliant?”). This compliance status becomes visible and manageable through your metadata management tool, letting you quickly see which models follow governance policies and which don’t.
By embedding AI in your review, governance won’t be something you enforce after the fact, it will be directly integrated into the daily data workflow.
3. AI-driven documentation quality checks
Clear and detailed documentation is critical for effective data governance. Many teams already track basic metadata properties (like “Is this asset documented?”) but this doesn’t guarantee quality. A simple checkbox can’t ensure documentation is accurate, sufficiently detailed, or aligned with internal guidelines.
Integrating AI into your metadata management takes documentation checks further. By using an LLM, you can automatically scan documentation to assess quality: checking if definitions are detailed enough, properly structured, and aligned with your standards. The AI can then automatically update your metadata catalog with a new property like “Documentation Quality,” clearly indicating whether documentation meets governance standards or needs improvement.
This ensures documentation quality isn’t subjective or inconsistent. Your governance becomes proactive, spotting documentation gaps before they cause confusion or mistakes downstream.
Benefits of adopting AI data management for data governance
Adopting AI-driven data management can transform your governance practices from reactive and manual processes into proactive, automated workflows. Here are the key benefits your organization will experience:
1. Improved visibility into governance compliance
AI-driven data management lets you quickly and easily verify compliance through natural language queries, dramatically reducing human error and time spent on manual checks.
2. Reduced manual effort
AI automates repetitive governance tasks, such as checking SQL code compliance or validating documentation. This reduces manual overhead, freeing your data teams to focus on strategic projects and higher-value tasks.
3. Real-time, continuous oversight
With AI embedded in your metadata management, governance moves from periodic checks to continuous oversight. Potential issues are flagged immediately, allowing teams to manage and resolve governance gaps as they arise.
4. Governance as an everyday practice
Integrating AI into your data management workflows ensures governance becomes an integral part of daily operations, rather than something teams only think about occasionally. Policies actively shape real-world data usage instead of being forgotten in documents.
5. Increased trust and adoption
Consistent, AI-powered enforcement of governance rules ensures data quality and compliance. This increases trust in data products and encourages broader adoption across your organization, driving more confident and informed decision-making.
AI and the future of data governance
Data governance and data management have always worked together. Governance defines why data should be used in a specific way, setting policies around compliance, security, and business standards. Data management, on the other hand, ensures those policies are actually implemented in the data pipeline. AI takes this to the next level by embedding governance directly into data management processes, making enforcement seamless rather than manual and reactive.
As organizations push for data democratization, giving more teams access to data and allowing wider contributions to data models, governance must keep up. AI-driven data management allows this balance by:
- Automatically detecting non-compliant data assets and flagging them before they create issues.
- Providing guided remediation so that teams can resolve discrepancies without slowing down innovation.
- Ensuring only trusted, high-quality data surfaces in analytics and AI models, reinforcing confidence in data-driven decisions.
This allows organizations to scale data access without losing control, making AI-driven data management the foundation for the next generation of governance.
How Euno powers AI-driven governance
Euno is a powerful metadata management platform that helps data teams govern their data assets with confidence. It ensures governance isn’t just a set of rules but an active, automated process that scales with your data environment.
AI is built into Euno to make governance seamless. With natural language querying, teams can ask governance-related questions in plain English and instantly surface issues. Euno also allows teams to define metadata properties that AI can continuously update, ensuring compliance statuses remain accurate without manual intervention.
With Euno, governance becomes automatic, ensuring only trusted data is surfaced for decision-making.
See it in action! Book a demo.
