Augmented Governance: the Cure for Your Data and GenAI Woes?

Governance. It rarely gets the spotlight it deserves, but try running your data strategy without it. 

Suddenly, decisions are based on the wrong numbers; analysts are drowning in mismatched reports, and your compliance team? Well, they’re brewing coffee by the gallon. 

Enter Augmented Governance, offering a smarter, faster way to manage data—without sending your team into burnout mode. So, how does it all come together?

Governance: Your Data’s Unsung Hero

The role of governance is to make sure your data is not only properly managed—it’s trusted, it’s compliant, and it’s ready to support decision-making. It might sound simple: keep your data accurate, secure, and accessible to the right people. But in practice? It’s more like trying to conduct a symphony with instruments spread across different time zones. 

When data governance is humming along smoothly, your decisions are based on trusted data, regulatory compliance is met, and efficiency thrives. But when it falters, you’re looking at data chaos—duplicated reports, regulatory risks, and decisions driven by incomplete or inaccurate information. In industries like finance, insurance, and healthcare, governance is especially critical. Mishandling data can lead to serious consequences, including big fines or reputational damage. 

As organizations expand their data estates, governance challenges have grown significantly. With multiple teams accessing different systems, ensuring consistent data and AI governance is more difficult than ever.

This is especially true when dealing with structured data. Unlike unstructured data, which comes with its own meaning and context, structured data—like tables and columns in databases or files in data lakes—is organized but lacks semantics. It’s just numbers and labels. No meaning. No context. To make sense of structured data and make it useful, it needs semantic meaning, context, and usage metadata layered on top.

Mission Impossible? Handling Governance in Data-Heavy Enterprises

You’ve probably seen it all—data silos, inconsistent definitions, and the constant pressure to maintain compliance in an ever-changing and growing data landscape. If you’re wrangling massive amounts of data, this will sound familiar:

Siloed Data

Data usually doesn’t live in one place, especially in large enterprises. It’s scattered across different systems, platforms, and departments—each operating in its own silo, with its own data stack (which is often made up of several tools for data storage, analytics, and BI). So, you’ve got sales data in one stack, financial data in another, and operational data altogether elsewhere. This results in critical insights getting trapped in these isolated pockets, making it difficult to get a complete, unified view of the business. Teams, too, end up working in silos, leading to duplicated efforts and conflicting reporting.

Inconsistent Semantics

Even when you manage to break down silos, there’s no guarantee the structured data you’ll find speaks the same language. Different teams and tools have different definitions for the same terms, leading to confusing insights. One team’s “customer” is another team’s “user.” Without a common language (or semantic reconciliation), your analytics outputs can end up looking like a bad translation. Multiply that across different systems and platforms, and you’ve got a recipe for chaos. Cue: frustration and delays.

Manual, Resource-Heavy Processes

Tagging, documenting, reconciling, tracking changes in data—it’s a full-time job (or 10). Governance teams are constantly drowning in manual tasks. It’s slow, it’s error-prone, and frankly, it’s exhausting. Every new data source or regulation adds another layer of complexity. Increasing workloads drain resources and leave your team stretched thin, preventing them from focusing on higher-value strategic tasks.

Compliance and Privacy Risks

Ensuring that sensitive data, like Personally Identifiable Information (PII), is properly tagged and tracked can be a monumental task without the right tools. Manual processes make it easy to overlook critical details, increasing the risk of non-compliance with privacy regulations such as GDPR or HIPAA. If sensitive data isn’t accurately governed, it can lead to costly breaches, fines, and a significant loss of trust from customers and stakeholders.

Governance and GenAI: With Great Power Comes Great(er) Responsibility 

As if traditional data governance wasn’t challenging enough, when AI and GenAI are thrown into the mix, they introduce a whole new layer of complexity. AI thrives on data—learning, predicting, and adapting. But in large enterprises, where data flows in from every direction—different departments, different systems, sources, formats—it’s harder than ever to maintain control. A recent McKinsey survey does a good job of illustrating that point. While implementing generative AI is a high priority for 63% of the survey responders, 91% of them stated they don’t feel “very prepared” to do so in a responsible manner.

Data quality is at the heart of it all. GenAI needs clean, semantically meaningful, contextualized data to deliver accurate insights. This is especially challenging when dealing with structured data, which doesn’t come with inherent meaning and semantics. If teams across the organization are working with misaligned naming conventions or unclear definitions, GenAI models are left interpreting inconsistent data. The outcome is often misleading and confusing results, poor decisions, and a breakdown in user trust. And when your AI systems and GenAI agents can’t be trusted, then what’s the point of using them?

Beyond data accuracy, it’s also about speed. GenAI operates in real time. Your governance needs to match that pace. Manual reconciliation of terms, documentation, audits, and tagging just can’t keep up. What worked before is now outdated. Governance has to move as fast as AI—automated, always-on, ensuring data is accurate, secure, and compliant without slowing things down.

Then, there’s compliance. There’s always compliance, and the stakes are higher than ever. In 2023 alone, the number of AI-related regulations in the US has grown by 56.3%. AI-driven decisions need to meet tightening regulatory requirements, and the margin for error is shrinking fast. Sensitive data must be classified, stored, and accessed correctly, all while adhering to stricter privacy laws. One slip-up could mean fines, breaches, or worse—a loss of trust from customers and regulators alike. Speaking of fines, one example is a recent AI regulation proposed by the European Union, stating a company could be fined up to 7% of its annual global revenues. 

From Chaos to Control: Augmented Governance for Structured Data

The good news is that the future of governance is here—and it’s all about automation, augmentation, and smarter workflows. AI-driven governance tools are cutting through the complexity of managing massive structured data estates. Here’s how:

Automated Documentation & Semantic Alignment

Still chasing down outdated documentation, manually going column by column, table by table, while figuring out the right business context? Augmented Governance does the heavy lifting for you. It automatically builds a Business Glossary and Business Terms Library from your data’s metadata, mapping out relationships and adding semantics and business context—no manual effort required. With automated semantic mapping, your data stays aligned across teams and systems, ensuring everyone works from the same, trusted single source of truth. As your data evolves, the documentation updates to keep up with the data. As a result, you’re always audit-ready, compliant, and in sync without lifting a finger. 

Automated Data Classification

Gone are the days of manual, spreadsheet-driven governance. Through active metadata management, AI-powered governance tools can now automatically reconcile, classify, and label data based on its content, sensitivity, and regulatory needs. Siloes can now be broken without the need to move data. No more chasing down where sensitive data is stored or worrying about how it’s being used. Automation reduces human error and ensures your data is correctly categorized and compliant from the start—every single time.

Continuous Monitoring

Augmented governance continuously monitors your structured data, detecting changes in data and potential liabilities as they happen. This proactive approach uses metadata-activated guardrails to flag issues before they escalate, keeping your data compliant without waiting for the next audit. You’re not reacting to problems—you’re staying ahead of them.

Streamlined Compliance Reporting

Tired of scrambling to put together compliance reports and manually verifying every detail? Or worse yet, finding out there were changes to the data after you’ve already submitted your report? With augmented governance, those headaches disappear. AI-powered governance can ensure that your reports are not only accurate but also fully aligned with your business and data definitions. Any changes in your data are automatically and continually tracked, so you’ll never be caught off guard by updates that could impact your reporting. Forget sifting through endless documents or systems—augmented governance makes sure you’re always audit-ready and able to prove regulatory compliance with confidence and ease.

Scalable Accountability

In large organizations, governance isn’t a one-team job. Augmented governance makes it easy to spread accountability by assigning data ownership across teams. This keeps governance active and effective at every level, cutting through bottlenecks and speeding up decision-making. If a change happens, the assigned owner is instantly notified and can take the right actions immediately, keeping everything running smoothly and ensuring no detail slips through the cracks. 

Welcome to the Future: Augmented Governance Awaits

Augmented governance is a smarter, more efficient way to manage your data. Imagine reducing documentation efforts by 90%, improving compliance with automated checks, and effortlessly boosting security. Streamlined processes and improved data quality mean less risk, easier audits, and more reliable data.

Curious to learn more? Download our comprehensive guide on augmented governance workflows. Inside, you’ll find actionable strategies and best practices on how to use AI to manage complex data estates effectively.