Data Governance Meets the EU AI Act

Driving Professional Excellence in Data and AI

Axel Schwanke
9 min readJan 28, 2025
  • Compliance as a Strategic Advantage: The EU AI Act, while challenging, offers organizations an opportunity to professionalize data governance, improve AI performance, and build trust. By addressing requirements such as dataset quality and traceability, businesses can turn compliance into a driver of innovation and growth.
  • Unified Governance for Sustainable AI: The implementation of structured frameworks, end-to-end provenance tracking and integrated quality management ensure compliance with the EU AI Act. These practices not only improve transparency and accountability, but also promote sustainable, ethical AI development and position companies for long-term success.

Introduction

The EU AI Act is no longer a distant threat — it’s here, and it’s changing the game. This groundbreaking legislation presents a unique opportunity to not just comply, but to excel. By prioritizing data governance and embracing ethical AI development, you can build a foundation of trust with your customers, gain a competitive edge in the market, and position your organization as a leader in responsible AI.
This article will guide you through the key considerations, from assessing your current data practices to implementing robust governance frameworks. Discover how to turn the EU AI Act into a catalyst for innovation and a cornerstone of your organization’s success.

The EU AI Act

The EU AI Act is a comprehensive piece of legislation that addresses various aspects of responsible AI development and sustainable use. Key provisions include:

  • Risk-based classification: AI systems are categorized into four risk levels — minimal, limited, high, and unacceptable — with stricter rules for high-risk applications like healthcare and autonomous vehicles.
  • Fundamental rights compliance: Ensures AI systems do not discriminate and respect human autonomy and dignity throughout their lifecycle.
  • Data governance: Promotes principles like data minimization and purpose limitation to reduce environmental impact from data processing.
  • Transparency and accountability: Requires developers to inform users about AI systems’ purposes and risks, and holds them accountable for outcomes.

“The AI Act aims to ensure that AI systems in the EU are safe and respect fundamental rights and values. Moreover, its objectives are to foster investment and innovation in AI, enhance governance and enforcement, and encourage a single EU market for AI” — [The EU AI Act: What it means for your business]

The Udemy courseEU AI Act Compliance Introduction” by Robert Barcik and Jana Gecelovska provides an overview of the EU AI Act, covering high-risk AI systems, transparency, compliance, bias prevention, governance frameworks, and practical strategies for implementing responsible AI in line with regulatory standards.

Article 10: Data and Data Governance

The EU AI Act emphasizes sustainable AI through robust data governance, promoting principles like data minimization, purpose limitation, and data quality to ensure responsible data collection and processing. It mandates measures such as data protection impact assessments and retention policies. Article 10 underscores the importance of effective data management in fostering ethical and sustainable AI development.

Part of Chapter III: High-Risk AI SystemSection 2: Requirements for High-Risk AI SystemsArticle 10: Data and Data Governance

Summary
This article states that high-risk AI systems must be developed
using high-quality data sets for training, validation, and testing. These data sets should be managed properly, considering factors like data collection processes, data preparation, potential biases, and data gaps. The data sets should be relevant, representative, error-free, and complete as much as possible. They should also consider the specific context in which the AI system will be used. In some cases, providers may process special categories of personal data to detect and correct biases, but they must follow strict conditions to protect individuals’ rights and freedoms.
[Article 10: Data and Data Governance]

The EU AI Act emphasizes robust data governance for high-risk AI systems, requiring high-quality datasets, bias mitigation, and strict privacy compliance. Organizations must implement structured procedures, document data management, and align with GDPR to ensure transparency, accountability, and ethical AI development.

Challenges in Meeting EU AI Act Requirements

Article 10 of the EU AI Act mandates the use of high-quality datasets for training, validating, and testing AI systems. However, achieving compliance presents several significant challenges:

  • Ensuring Dataset Quality and Relevance: Organizations must establish robust data and AI platforms to prepare and manage datasets that are error-free, representative, and contextually relevant for their intended use cases. This requires rigorous data preparation and validation processes.
  • Bias and Contextual Sensitivity: Continuous monitoring for biases in data is critical. Organizations must implement corrective actions to address gaps while ensuring compliance with privacy regulations, especially when processing personal data to detect and reduce bias.
  • End-to-End Traceability: A comprehensive data governance framework is essential to track and document data flow from its origin to its final use in AI models. This ensures transparency, accountability, and compliance with regulatory requirements.
  • Evolving Data Requirements: Dynamic applications and changing schemas, particularly in industries like real estate, necessitate ongoing updates to data preparation processes to maintain relevance and accuracy.
  • Secure Data Processing: Compliance demands strict adherence to secure processing practices for personal data, ensuring privacy and security while enabling bias detection and mitigation.

Example: Real Estate Data
Immowelt’s real estate price map, awarded as the top performer in a 2022 test of real estate price maps, exemplifies the challenges of achieving high-quality datasets. The prepared data powers numerous services and applications, including data analysis, price predictions, personalization, recommendations, and market research.

Test Immobilien-Preis-Maps im Internet 2022 , © Immowelt

The challenges involved in preparing real estate data include

  • Highly Structured Attributes: Properties often have hundreds of detailed attributes, requiring careful management.
  • Frequent Updates: New properties may be updated multiple times daily, demanding real-time data processing.
  • Complex Cleansing: Data cleansing must account for factors like age, characteristics, and location, with procedures updated several times a year.
  • Schema Changes: Data schemas evolve, sometimes without upward compatibility, complicating integration.
  • Dynamic Activation: Properties are frequently activated (and deactivated) multiple times a month, impacting the services and applications reliant on this data.
  • Relevance Metrics: Property relevance also depends on metrics like user visits and contact requests, requiring continuous monitoring of user activity.
  • Historical Data: Applications that rely on historical data spanning months or years face added complexity due to frequent updates, schema changes, and dynamic property activations.

These challenges highlight the critical need for robust data governance frameworks, supported by a layered medallion architecture, to ensure high-quality, reliable datasets for data analytics, engineering and AI.

Implementing Compliant Data Governance

To operationalize data governance under the EU AI Act, organizations should adopt a structured and systematic approach:

  • Develop a Data Strategy: Align data initiatives with overarching business objectives to foster a data-driven culture. This ensures that data practices support both compliance and organizational goals.
  • Establish a Governance Framework: Create clear structures and policies to enforce compliance in data management and AI practices. This includes defining roles, responsibilities, and processes to ensure accountability.
  • Leverage Unified Platforms: Utilize centralized platforms for managing data and AI assets, enabling seamless integration, collaboration, and oversight across teams.
  • Ensure End-to-End Lineage: Utilize platforms like Databricks Unity Catalog to capture and monitor data lineage, providing full visibility into data flows and transformations. This enhances transparency and accountability throughout the AI lifecycle.
  • Integrated Quality Management: Apply quality constraints and continuously monitor AI systems to ensure consistent performance and reliability. Automated solutions can streamline this process, maintaining high standards while reducing manual effort.

By following these steps, organizations can effectively align with the EU AI Act’s requirements, ensuring responsible and sustainable AI development while driving operational efficiency.

Databricks Unity Catalog, © Databricks Inc.
Advanced Data Quality Constraints using Databricks Delta Live Tables

To comply with the EU AI Act, organizations must balance decentralized data ownership with centralized governance frameworks. Combining agility with robust governance, tools like Databricks Lakehouse and Unity Catalog enable efficient, secure, and compliant management of high-risk AI systems, ensuring accountability and data quality.

Benefits of the EU AI Act

Complying with the EU AI Act offers significant advantages that extend beyond regulatory adherence, fostering sustainable and responsible AI development. These benefits are amplified when supported by robust data governance and management tools.

  • Enhanced Professionalism: Unified governance frameworks streamline data management across teams, fostering collaboration and elevating organizational standards. This ensures consistent compliance with the Act’s requirements.
  • Improved Data Quality: Standardized data cleansing procedures ensure the consistent use of high-quality, well-documented datasets, leading to more accurate, reliable, and ethical AI systems.
  • Bias Reduction: Proactively addressing biases in data and algorithms promotes fairness, improving AI system performance and building trust with stakeholders.
  • End-to-End Traceability: Comprehensive data lineage enables full traceability, simplifying issue resolution and ensuring accountability throughout the AI lifecycle.
  • Efficient Data Access: Simplified data discoverability facilitates smoother workflows, accelerates AI development, and empowers teams to make data-driven decisions.
  • Cost Efficiency: High-quality data reduces the need for excessively large AI models, enabling smaller, more efficient systems that are easier to maintain and scale.
  • Strengthened Customer Trust: Transparency in AI systems, supported by auditability and lineage tracking, builds consumer confidence by demonstrating fairness and accountability.
  • Revenue Growth: High-performing, compliant AI systems drive innovation, enabling businesses to unlock new opportunities and achieve sustainable growth.

By adhering to the EU AI Act, organizations can not only meet regulatory standards but also enhance operational efficiency, build trust, and drive long-term success through innovation and sustainable growth.

The EU AI Act is reshaping AI regulation, setting global standards and categorizing AI systems by risk levels. While compliance poses challenges, including tight deadlines and evolving technical standards, it offers strategic opportunities. Companies embracing proactive compliance can foster innovation, build trust, and gain a competitive edge.

“For companies, compliance is not just a legal obligation but also an opportunity to stand out in a competitive marketplace.” — [Turning EU AI Act compliance into your competitive advantage]

Strategic Perspective

The EU AI Act presents a unique opportunity for companies to elevate their data and AI governance practices. Rather than viewing it solely as a regulatory hurdle, companies should embrace the Act as a framework for achieving strategic goals. By proactively addressing compliance challenges, companies can:

  • Enhance Operational Efficiency: Streamline data processes, improve AI model performance, and reduce inefficiencies through robust governance frameworks.
  • Foster a Culture of Innovation: Encourage responsible AI development and experimentation, enabling teams to innovate while adhering to ethical standards.
  • Drive Sustainable Growth: Build trust with customers, strengthen brand reputation, and gain a competitive edge in the market by demonstrating commitment to ethical AI practices.

The EU AI Act is a catalyst for strategic change, not just compliance. By prioritizing data quality, bias mitigation and transparency, companies reduce risk and strengthen their ethical credibility. Aligning compliance with business goals promotes innovation, trust and sustainable competitive advantage and positions companies as leaders in responsible AI development.

Conclusion

The EU AI Act sets out comprehensive rules for the use of AI, which present both challenges and opportunities for companies. While organizations must ensure fairness, security and transparency in their AI systems, these requirements also bring significant benefits. By strengthening data governance, improving data quality, promoting transparency in decision-making and implementing sound AI usage policies, organizations can achieve compliance while maintaining professional excellence. These efforts build customer trust, improve AI performance and create a competitive advantage. As such, the Act promotes responsible AI development that benefits businesses and society alike.

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Axel Schwanke
Axel Schwanke

Written by Axel Schwanke

Senior Data Engineer | Data Architect | Data Science | Data Mesh | Data Governance | Databricks | https://www.linkedin.com/in/axelschwanke/

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