Introduction
Do you remember the effort of tidying up your BI and reporting systems, only to find yourself facing a labyrinth of inconsistent data, missing values, and unclear ownership? Now, imagine handing that same data to an AI system tasked with advising your sales team, guiding sales decisions, or generating insights. Would you trust the results?
With 2025 poised to be another pivotal year for AI, particularly agentic AI, the stakes are higher than ever. While previous tech trends like Crypto and Web3 turned out to be more hype than substance, generative AI is demonstrating substantial, long-term potential. However, careful planning and robust data governance are essential to avoid repeating the mistakes of the machine learning bandwagon a decade ago, when many organizations struggled to move beyond proofs of concept.
Why AI Success Starts with Data Governance
A key component of data governance is data quality. For companies to move beyond POC, a robust governance framework is needed, especially due to the varying implications of AI-generated misinformation, which can be high or low. Unlike low-stakes interactions like asking a language model for sightseeing recommendations, enterprise AI applications can carry significant risks. The consequences of a chatbot providing false information on hotspot travel areas are low compared to providing false data to a customer, which can lead to legal and reputational fallout for a company.
To navigate these risks, organizations must prioritize data quality. A KPMG survey of business leaders revealed that data quality is the top concern for mitigating AI deployment risks after cybersecurity. This highlights the importance of solid data foundations to unlock AI’s potential while minimizing risks.
The Challenge of Untapped Data
AI is still a relatively new and exciting technology, so most use cases remain in the POC stage, and organizations have yet to master the development of production-ready solutions. Over the past five years, many organizations have invested heavily in data platforms. Yet, more than half of their unstructured data remains untapped. There will be increased pressure to leverage unstructured data effectively to justify these investments.
This urgency has fuelled renewed interest in data modeling, governance, and management. While the first step for many companies is to consolidate data in a lakehouse, this often leads to issues such as:
- Disorganized Data Models: Poorly conceptualized frameworks lead to inefficiencies.
- Unclear Naming Conventions: Confusing column names hinder collaboration.
- Lack of Quality Control: Missing checks compromise data integrity.
- Access Control Issues: Unauthorized access to sensitive data poses compliance risks.
This challenge has renewed focus on critical areas such as:
- Data Modeling: Designing cohesive frameworks for structured and unstructured data.
- Data Governance: Establishing clear policies, standards and processes for data usage.
- Knowledge Management: You can’t feed AI ‘just data’. There needs to be meaning or semantics attached to that data. This is not yet a very adopted concept, but it quickly gains prominence. We are going to hear a lot about knowledge graphs and ontologies in the near future.
The relationship between data governance and AI is symbiotic. Strong governance reveals pain points, improves collaboration, reduces risks, and accelerates decision-making. Without strong data governance frameworks, these problems turn your data foundation into a precarious structure built on sand.
Traditional Data Governance for AI Enablement
Despite the allure of modern solutions, traditional data governance principles remain indispensable. These programs encompass policies, processes, and structures to ensure data quality, availability, and security. By establishing clear ownership, standardized definitions, and rigorous validation processes, traditional governance lays the groundwork for trusted and actionable data. It further helps you strengthen your AI readiness by auditing your data to identify gaps in quality, accessibility, and documentation.
Putting Your AI Ambitions on Pause? Think Again
AI’s transformative potential is undeniable but requires a solid data foundation to succeed. Organizations can turn their data from a lingering liability into a strategic asset by prioritizing governance, fueling innovation and sustainable growth. Experimenting with AI isn’t just about innovation; it’s an opportunity to identify and address weaknesses in your data foundation. Before diving into complex AI use cases, take a step back to evaluate the “ground floor” of your data ecosystem.
Focus on essentials like your data catalogue, metadata management, and data stewardship practices. Strengthening these foundational elements is an investment in your organization’s future confidence in data. It helps align teams and processes, improves data discovery, and ensures everyone works from trusted, reliable sources.
With a solid governance program in place, you can mitigate risks like model bias or drift, make faster, more accurate decisions, and unleash the full potential of AI. Don’t let early challenges in data governance discourage you from pursuing AI’s potential.
Instead, use each project as an opportunity to learn, refine, and enhance your data foundation.
LUBOS FRCO
Data Management Portfolio Principal
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