Thanks to @Sheng Lan for putting together this classroom of useful resources for Data Engineering members wanting to learn about and experiment with RAG, LLM's and Ai in Enterprise environments. Brought to you by IBM watsonx in conjunction with Techdata.

Please let us know in the comments something like, or something you wish we could add or something that we could do to improve you Data and AI Engineering professional practice. 🙀 We'd love to hear what you would like more of and even less of!

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.