Hi everyone! I'm looking for recommendations on where to learn more about Data Governance, especially from a strategic and requirements perspective, rather than just technical-focused content. Any good courses, training programs, or resources that dive deeper into governance frameworks and best practices? I’d appreciate your insights!

What do you think? https://www.perplexity.ai/page/rob-brennan-data-governance-ex-_A6D5187QSuZSGj1jG1w7A#f75fd228-7953-4580-80ad-9edf638ca383 Dr. Rob Brennan's research and expertise, the core principles of data governance encompass several key areas: 1. Data Quality Management: This is a fundamental aspect of data governance, focusing on ensuring the accuracy, completeness, and reliability of data. Brennan emphasizes the importance of maintaining high-quality data throughout its lifecycle[1][2]. 2. Data Privacy and Security: Protecting sensitive information and ensuring compliance with data protection regulations is a critical principle. Brennan's work highlights the need for robust security measures and privacy-preserving techniques in data governance frameworks[1][3]. 3. Regulatory Compliance: Adhering to relevant laws and regulations is a core principle of data governance. Brennan's research addresses the challenges of maintaining compliance in various sectors, including healthcare[1][4]. 4. Data Value and Risk Management: Brennan's work emphasizes the importance of understanding and maximizing the value of data while managing associated risks. This includes developing methodologies for data valuation and risk assessment[3][5]. 5. AI Governance: As an emerging area of focus, Brennan incorporates AI governance into data governance principles, addressing the ethical and practical challenges of managing AI systems and their data[1][6]. 6. Knowledge Graphs: Brennan's research highlights the use of knowledge graphs as a tool for enhancing data governance, particularly in organizing and linking complex data structures[3][6]. 7. Transparency and Accountability: Establishing clear roles, responsibilities, and processes for data management is a key principle in Brennan's approach to data governance[2][3]. 8. Interoperability and Standards: Brennan's work, including his contributions to international standards, underscores the importance of interoperability and standardization in effective data governance[3][7].

Rob Brennan is one of the leading researchers and publishers on Data Governance. Check out his contributions to the field here: https://www.perplexity.ai/page/rob-brennan-data-governance-ex-_A6D5187QSuZSGj1jG1w7A Rob Brennan, an Assistant Professor at the School of Computer Science, University College Dublin, is a leading researcher in data governance, with a significant body of work spanning data quality, data value, AI governance, and privacy. His extensive contributions to the field, including over 140 peer-reviewed papers and involvement in numerous research projects, have established him as a prominent figure in data governance research.

Check out this audio version of the top story from DataPro.news https://www.datapro.news/p/adapting-data-governance-age-llms We discuss the key considerations in Data Governance as Enterprises adopt LLM's. Let me know anything you liked!

Ever come across this author? "Designing Data-Intensive Applications" should be required reading for software engineers, according to Kevin Scott, Chief Technology Officer at Microsoft. He ranks in the top 5 most influential people in the Data Engineering and Data Pipeline field. Check out the full article here: https://www.perplexity.ai/page/martin-kleppmann-data-engineer-vd_XDQGJScSj7MeSk4m4YQ Martin Kleppmann, author of "Designing Data-Intensive Applications," is a prominent figure in data engineering and distributed systems whose work has significantly influenced modern data architecture and modeling practices. His expertise and practical insights have made him a valuable resource for data engineers seeking to build scalable, reliable data systems.