Introduction
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
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
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
Putting Your AI Ambitions on Pause? Think Again
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.
Luboš Frco
Data Management Portfolio Principal