The Praxi Pod

Data & AI in Regulated Industries Brian Price & Tony Cassin Scott, Co-Founders of The Data Practice

In this episode of the Praxi Pod, host Andrew Turner engages with data experts Brian Price and Tony Cassin-Scott to explore the evolving landscape of data and AI. They discuss the importance of focusing on business outcomes rather than just technology, the challenges of data governance, and the need for organisations to understand the value of their data. The conversation highlights the risks associated with the AI hype and emphasises the necessity of foundational data capabilities for successful implementation. As they look ahead to 2026, the experts provide insights on how businesses can prepare for the future by starting small and proving value before scaling up their data initiatives. Takeaways - Data should be viewed through the lens of business outcomes, not just technology. - Organisations often focus too much on technology rather than the value of data. - Everyone in the organization should be passionate about data ownership. - Data governance should enable decision-making rather than prevent it. - Understanding the risks associated with data is crucial for effective governance. - AI should not be seen as a simple out-of-the-box solution; it requires careful planning. - Start small with data initiatives to prove value before scaling up. - The hype around AI can lead to shadow AI activities that pose risks. - Cultural change is necessary for effective data governance and utilization. - Organisations need to define clear objectives for their data strategies. Chapters 00:00 Introduction to Data and AI 01:29 Expert Backgrounds in Data 04:56 Shifting Focus from Technology to Business Outcomes 10:19 Understanding Data Governance and Its Role 15:54 The Importance of Organizational Design in Data Management 19:22 The Role of CDOs and Data Ownership 22:33 Challenges in AI Implementation and Accountability 26:07 Understanding the Black Box of Data 27:57 Cultural Implications of AI Usage 29:30 Navigating Technology Investments 30:49 The Challenge of Proving Value 32:35 The Role of AI in Cost Savings 34:34 The Shadow Side of AI Adoption 36:44 The Consumerization of AI 39:06 Foundational Data Challenges 40:33 The Importance of Business Objectives 43:04 Final Thoughts for Executives Linkedin Brian Price https://www.linkedin.com/in/brianprice4/ Tony Cassin-Scott https://www.linkedin.com/in/tonycassinscott/ The Data Practice https://www.linkedin.com/company/thedatapractice/

The Praxi Pod Room 101 : Unlocking the Power of AI: Data Classification & Curation Explained

In this conversation, CEO Andrew Ahn discusses the intricacies of AI and data classification, emphasising the importance of data quality, curation, and the challenges posed by dark and gray data. He highlights the risks of neglecting dark data and the benefits of automating data classification processes. The discussion also covers real-world applications and the significance of domain knowledge in ensuring accurate data classification. Takeaways - The first step in creating an AI model is obtaining the right data. - Data labelling, classification, and curation are distinct but interconnected processes. - Curation is essential for organising data relevant to specific questions. - Dark data represents unknown unknowns that can pose risks to businesses. - Automating data classification can significantly reduce manual workload. - 80% of a data worker's time is spent on data curation tasks. - Bad data leads to poor decision-making and outcomes. - Domain knowledge enhances the accuracy of data classification models. - Companies need to be proactive in managing their dark data. - The foundation of AI and analytics is high-quality, well-classified data. Chapters 00:00 Introduction to AI and Data Classification 02:32 Understanding Data Labelling, Classification, and Curation 05:36 The Importance of Data Quality and Curation 08:09 Exploring Dark and Gray Data 11:07 The Risks of Ignoring Dark Data 13:54 Benefits of Automated Data Classification 16:18 Real-World Applications of Data Classification 19:20 The Role of Domain Knowledge in Data Classification 21:54 Conclusion and Future of Data Classification Subscribe to be notified of future content from the Praxi.ai Team

Roberto Maranca, The Journey to Unlocking Data Excellence

In this episode of the Praxi Pod, Andrew Turner interviews Roberto Maranca about his new book, 'Data Excellence.' They discuss the importance of data governance, the cultural aspects of data management, and the role of data in organisational transformation. Maranca emphasises the need for clarity in data definitions, the significance of treating data as a product, and the human element in AI. The conversation also touches on the challenges of data debt and the importance of a sustainable data culture within organisations. Takeaways * Data excellence is a journey that requires cultural understanding. * Organisations often struggle with defining their data ambitions clearly. * Data governance is essential for steering data in the right direction. * Data should be treated as a product to enhance its value. * The role of the Chief Data Officer is crucial in guiding data strategy. * AI should not be confused with data; they serve different purposes. * Measuring data quality is vital to avoid data debt. * Regulation can act as a catalyst for better data practices. * A sustainable data culture is necessary for long-term success. * Human creativity remains central in the age of AI. Sound bites * "It's a labour of love." * "The journey is not there." * "Data is a team sport." Chapters 00:00 Introduction and Special Announcement 01:13 The Launch of 'Data Excellence' 03:12 Understanding Data Excellence 06:07 Cultural Challenges in Data Excellence 07:50 Exercises for Data Fitness 10:25 Data as a Team Sport 11:38 The Role of Coaches in Data Management 13:09 Distinguishing Data from AI 15:50 The Importance of Measuring Data Debt 20:49 Creating Sustainable Data Practices 23:08 Understanding Data Challenges in Business 24:36 The Role of Regulation in Data Management 26:11 Data Governance vs. Data Management 28:35 Treating Data as a Product 34:33 The Human Element in an AI-Driven World 39:36 Achieving Data Excellence Nirvana Roberto has been a regular guest on The Praxi Pod and is a Senior Exec with Schneider Electric. His new book "Data Excellence" is officially released on 3rd October and he is doing an in-person Book signing at Europes largest data focused event - Big Data LDN in September 2025 Excellent, insightful and enjoyable episode Enjoy !!!

Ole Olesen-Bagneux, Connecting the data dots with MetaGrid

In this episode of Praxi Pod, host Andrew Turner speaks with Ole Olesen-Bahneux about his journey in the data space, his first book on data catalogs, and his new book on metadata management. They discuss the importance of user adoption, the concept of the MetaGrid, and how Ole's background in library science informs his approach to data management. The conversation highlights the challenges organisations face in managing data and the need for better coordination of metadata repositories. Takeaways * Ole's first book focuses on the importance of data catalogs. * Data catalogs are often underutilized in organizations. * User adoption is crucial for the success of data technologies. * Ole emphasizes the need for a bridge between technology and business. * The concept of the MetaGrid helps coordinate multiple metadata repositories. * Metadata is defined as being in two places at once. * The role of reference librarians can be applied to data management. * Ole's background in library science informs his approach to data. * Understanding metadata can improve data management practices. * Ole's new book addresses the challenges of metadata management. Sound bites * "Data catalogs are where metadata goes to die." * "Metadata is in two places at once." * "It's a tech book, but it's a weird tech book." Chapters 00:00 Introduction to Ole Olsen Bagneux 02:41 The Importance of Data and AI 05:41 Ole's Academic and Professional Journey 10:55 Understanding Data Catalogs 13:50 User Adoption and Data Catalogs 19:14 The Fundamentals of Metadata Management 25:01 The Journey of Metadata Management 27:49 Understanding the IT Landscape 31:08 The MetaGrid Concept 35:59 Defining Metadata 42:10 The Role of the Reference Librarian 48:16 Bridging the Gap in Data Management

Jessica Talisman, Unlocking Knowledge: The Future of AI and Management

In this episode of The Praxi Pod, Andrew Turner speaks with Jessica Talisman about the evolving landscape of knowledge management and the role of AI. They discuss the importance of semantic engineering, the challenges organisations face in managing data, and the need for effective knowledge infrastructures. Jessica shares insights on the ontology pipeline and the significance of context in knowledge management, emphasising the need for organisations to embrace a service-oriented mindset. The conversation also touches on historical perspectives, the role of libraries, and future trends in knowledge management. Jessica Talisman is an information architect and semantic technologist with 25+ years designing semantic architectures across enterprise tech and cultural institutions. A formerly trained librarian and information scientist, Jessica works at the intersections of culture and technology, Former roles include Senior Information Architect at Adobe, Information Architect at Amazon, and positions at Pluralsight, GDIT, Overstock.com, and the Department of Justice. She created the Ontology Pipeline™ framework, to help organizations build coherent data ecosystems. Through consulting, courses, and interdisciplinary dialogue, Jessica seeks to advance collaboration between people, machines, and systems. Substack: https://substack.com/@jessicatalisma [https://substack.com/@jessicatalisman?r=ee6qm&utm_medium=ios&utm_source=profile]n LinkedIn: https://www.linkedin.com/in/jmtalisman [https://www.linkedin.com/in/jmtalisman?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app] Website: Ontologypipeline.com Takeaways * Knowledge management is crucial for organisational success. * Semantic engineering plays a vital role in data management. * Organisations face challenges in managing knowledge effectively. * AI should complement human knowledge, not replace it. * Context is critical in knowledge management practices. * Libraries offer valuable lessons for managing knowledge. * The ontology pipeline provides a structured approach to knowledge management. * Collaboration is key to effective knowledge management. * Organisations must validate their knowledge infrastructures. * Future trends indicate a shift towards more collaborative knowledge management practices. Chapters 00:00 Introduction to Jessica Talisman and Her Work 02:45 The Role of Semantic Engineering in Knowledge Management 05:24 The Evolution of Roles in Organizations 08:09 Knowledge Workers and Tools for Productivity 10:52 The Importance of Context in Data Management 13:35 Challenges with AI Implementation in Organizations 16:34 Cross-Functional Collaboration for AI Success 19:35 Historical Context of Library Science and AI 22:35 Navigating the AI Hype Cycle 25:20 The Future of AI Tools in Organisations 28:09 Stewardship of Knowledge Assets in Organizations 35:32 The Dynamic Role of Libraries in Education 38:55 AI Partnerships and Knowledge Structuring 43:23 Building Knowledge Ecosystems 48:55 Understanding Ontologies and Taxonomies 57:16 Creating Semantic Infrastructures 59:06 The Future of Knowledge Management