Data governance in 2024

As we approach 2024, the landscape of data governance is set to undergo transformative changes. This article delves into the anticipated advancements and shifts in data governance, highlighting the integration of AI, the importance of metadata management, the emphasis on data quality and literacy, evolving operating models, and the integration of data warehousing and lake houses.

AI and Machine Learning Integration in Data Governance

The integration of AI and machine learning (ML) is reshaping data governance. AI-driven systems are expected to play a crucial role in automating data governance processes, including the classification, tagging, and management of data. These technologies are not just tools for data analysts and scientists; they are becoming integral in automating aspects of data governance, such as monitoring compliance and data quality. This integration will streamline data governance processes, making them more efficient and less prone to human error.

The Rising Importance of Metadata Management

In 2024, metadata management is poised to take center stage. At least that's my hope. With the increasing complexity of the data ecosystem, the focus is shifting towards enhancing metadata to provide richer context, aid in data discovery, and ensure more efficient data management. This advancement in metadata management is becoming a crucial component of effective data governance frameworks.

Emphasizing Data Quality and Data Literacy

Data quality and data literacy are becoming increasingly paramount. As AI continues to be a significant focus, the importance of data quality is amplified. High-quality data is essential for effective AI outcomes. Alongside this, there's a growing emphasis on data literacy. As data becomes more integral to business operations, ensuring that all stakeholders understand the importance of high-quality data and are skilled at interpreting and using it effectively is vital. There's no escaping them.

Operating Models in Data Governance

While there might be a trend towards decentralized models, the hybrid approach remains prevalent. Hybrid data governance models empower individual departments to govern their own data while still aligning with overarching data governance policies at the enterprise level. This approach offers a balance, allowing for department-specific needs to be met without compromising overall governance standards.

Integration of Data Warehousing and Lake Houses

A significant trend in 2024 is the integration of data warehousing and lake houses. This hybrid model combines the structured, organized approach of traditional data warehouses with the flexibility and scalability of data lake houses. Data governance strategies need to adapt to govern this integrated data architecture effectively, ensuring high-quality, consistent data across diverse platforms.

Data Democratization & Regulations

Data democratization is becoming a key trend, making data more accessible to a broader range of users within an organization. This trend empowers more employees to make data-driven decisions, enhancing overall business efficiency. At the same time organizations must continuously adapt to changing data protection and privacy regulations. Understanding and complying with both global and local regulations is crucial for effective data governance.


The landscape of data governance is evolving rapidly, driven by technological advancements like AI and changing regulations. The business landscape is also changing as a result of these shifts. Staying ahead in this dynamic environment requires adaptability, foresight, and a commitment to continuous learning.

The future of data governance in 2024 is marked by significant advancements and shifts. From AI and ML integration to the emphasis on metadata management, data quality, and literacy, these changes reflect the evolving needs of modern businesses. The hybrid approach in operating models and data architecture, along with the challenges of navigating regulations and the trend towards data democratization, highlight the dynamic nature of data governance. Staying ahead in this field requires a keen understanding of these trends, adaptability, and a commitment to continuous learning.

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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