The Praxi Pod

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Nov 2025

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Data is everywhere and growing fast. In an era where 80% of enterprise data remains untapped, and with a projected surge to 175 zettabytes of data by 2025 alongside a $190 billion AI market, the need for data analytics has never been more critical. The Praxi Pod is all about Data, AI and how you can make decisions and a real impact for your business. Talking to Data Leaders across the world to learn and change your game.

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November 4, 202550 min

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/

September 18, 202540 min

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

September 1, 202544 min

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 !!!

August 28, 202555 min

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

August 19, 20251 hr 10 min

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 n LinkedIn: https://www.linkedin.com/in/jmtalisman 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

June 17, 202521 min

Andrew Ahn, Beyond the Model: Why the Future of AI Is a Data Curation Economy

In this episode of the Praxi Pod, Andrew Turner and Andrew Ahn discuss the recent $15 billion investment by Meta in Scale AI, exploring its implications for the AI market. They delve into the importance of data quality over model size, the challenges of reinforcement learning, and the advantages of expert-driven data curation. The conversation also highlights compliance as a competitive advantage in AI development and offers insights on future-proofing AI strategies for organisations. Takeaways Meta's investment in Scale AI signifies a shift in AI focus. Data quality is crucial for effective AI and ML. Reinforcement learning assumes data is trustworthy, which is often not the case. Expert-driven data curation enhances model training and outcomes. Compliance should be integrated throughout the AI process, not just at the end. Specialisation in AI tools can lead to better productivity and effectiveness. High-quality data is essential for making informed decisions. Organisations need to adapt quickly to new data sources. Data curation can significantly improve operational efficiency. The future of AI lies in the data curation economy. Chapters 00:00 Introduction to AI Investment Trends 06:09 Reinforcement Learning and Its Challenges 11:56 Compliance as a Competitive Advantage 17:57 The Role of Data Curation in AI Success

June 11, 20251 hr 18 min

Ali Khan, The Strategic Importance of Data in Business

In this conversation, Andrew Turner and Ali Khan discuss the evolving role of Chief Data Officers (CDOs) in the context of AI and data management. They explore the recognition of CDOs, the challenges they face, and the importance of data in decision-making. Ali shares insights on the transformation of data roles, the balance between transformation and line management, and the growing trust in AI for decision-making. The discussion highlights the significance of data as a core asset in organizations and the need for continuous evolution in data practices. Ali covers the evolution of AI, particularly in relation to the Turing Test and its implications for businesses. He emphasises the importance of understanding the risks associated with AI integration, the growing customer expectations, and the fear of missing out (FOMO) in adopting AI technologies. The discussion also covers the critical role of data quality, the challenges of explainability and bias in AI, and the skills gap in AI development. Overall, the conversation highlights the complexities and considerations businesses must navigate in the rapidly evolving AI landscape. We also discuss the rapid evolution of AI in software development, the implications for the future of work, and the critical importance of AI ethics and governance. Highlighting the shift towards hybrid organszations where AI and human workers collaborate, the need for responsible AI practices, and the challenges of establishing ethical frameworks for AI behaviour. Emphasizing the transformative potential of AI while acknowledging the ethical dilemmas it presents. Takeaways The CDO role is gaining recognition but still faces challenges. Data management is crucial for making informed decisions. Transformation in data roles is necessary for organizational success. AI is reshaping the landscape of data management. Trust in AI is built through demonstrated value and results. Gut decisions are valid but should be supported by data. The CDO role involves both transformation and ongoing management. Data is a fundamental asset for organizations. Continuous evolution in data practices is essential. Collaboration and education are key in adopting AI solutions. The Turing Test has evolved, and AI can now mimic human interaction convincingly. Understanding the business value of AI is crucial for successful implementation. AI introduces risks and uncertainties that must be managed carefully. Customer expectations for AI capabilities are rising, making it essential for businesses to adapt. FOMO is driving many organizations to adopt AI without fully understanding its implications. Data quality is the most significant factor in the success of machine learning models. Explainability in AI remains a challenge, complicating trust and accountability. Bias in AI models can have serious ethical implications that need to be addressed. Integrating traditional models with AI can enhance robustness but requires careful planning. The skills gap in AI development is a significant barrier that organizations must overcome. AI can autonomously add code to existing software. The future is hybrid with augmented organizations. AI ethics is crucial for our future. Responsible AI is ethics in practice. AI governance encompasses ethics, safety, and accountability. Data governance should extend to AI governance. AI will become ubiquitous like big data. Philosophy is going to eat AI. We need a common ethical framework for AI agents. AI ethics is moving faster than our understanding. Sound Bites "It's about data. It's about this thing called AI." "There's a good understanding of the CISO role." "It's not a one and done, right?" "Gut decisions are real, and I rely on that." "We need to take our colleagues on that journey." "We're approaching that point, aren't we?" "If you don't have it now, it's just table stakes." "The FOMO is of a couple of flavors." "The biggest consideration is the data." "AI is not some sort of fairy dust." "You have to fight the urge to over-engineer." "AI ethics is crucial for our future." "Responsible AI is ethics in practice." "Data governance should extend to AI governance." "AI will become ubiquitous like big data." "Philosophy is going to eat AI.” Chapters 00:00 Introduction and Background 03:08 Recognition and the Role of CDOs 05:54 The Evolution of the CDO Role 09:13 Transformation vs. Line Management in Data Roles 11:57 The Importance of Data in Decision Making 14:57 AI and Its Impact on Data Management 18:11 The Journey of Trusting AI in Decision Making 28:14 The Evolution of AI and the Turing Test 30:08 Business Implications of AI 33:37 Understanding AI Risks and Uncertainties 35:13 Customer Expectations and AI Integration 37:26 FOMO in AI Adoption 39:19 Data Quality and Machine Learning 40:58 The Challenge of Explainability in AI 42:00 Bias and Ethical Considerations in AI 48:26 Integrating Traditional Models with AI 49:44 The Skills Gap in AI Development 56:28 The Evolution of AI in Software Development 01:00:08 The Future of Work: Hybrid Organisations 01:02:59 AI Ethics and Governance: Navigating the New Landscape Ali is a veteran multi-industry Data & AI thought leader with over 25 years of experience, including senior leadership positions at Scholastic, Verizon and Experian Consumer Services. He has started, scaled and operated multiple organisations and is an expert in all aspects of running a successful Data & AI practice - cultural, organisational and technical - ensuring that the group aligns precisely with business outcomes for maximal impact. Ali has led world-class, mission-driven teams of 100+ to deliver transformational data platforms and innovative products - realizing $100m+ in revenue uplift through optimisation, personalisation and monetisation. He is highly competent at data strategy, AI ethics, platform architecture, personalization and privacy compliance. As a recognised industry leader, Ali is also engaged in executive advisory for industry organisations, startups and venture capital firms. You cannot connect with Ali here https://www.linkedin.com/in/khana/

June 5, 20251 hr 3 min

Ako Sabir, Transforming Business with Data and AI

In this episode of Praxi Pod, host Andrew Turner speaks with Ako Sabir about his extensive career in business transformation, focusing on the role of data and technology, particularly in the insurance sector. They discuss the unique challenges and opportunities within insurance, the critical importance of data in risk assessment, and the impact of AI on underwriting and claims processes. The conversation also touches on the necessity of regulatory compliance, the evolving awareness of leadership regarding technology, and the balance between innovation and risk management. Ako shares insights on the importance of educating leadership on AI and the cautious approach needed for proof of concepts in integrating new technologies. In this conversation, the speakers delve into the complexities of implementing AI and technology in business, emphasising the importance of understanding use cases, operationalisation, and governance. They discuss the challenges faced in regulated industries and the necessity of a structured approach to data and technology strategies. The conversation also touches on the future of AI, the need for responsible use, and the significance of engaging leadership in driving transformation. Takeaways - Ako emphasises the importance of change and transformation in his career. - He has worked across various sectors, focusing on data and technology for business transformation. - Insurance presents unique challenges and opportunities for innovation. - Data is fundamental in understanding risk and pricing in the insurance industry. - AI can significantly enhance underwriting and claims processes. - Regulatory compliance is a major challenge for insurance companies. - Leadership in organisations is increasingly aware of the need for data and AI. - There is a balance to be struck between innovation and risk management. - Organisations must educate their leadership on the implications of AI. - Proof of concepts should be approached with caution, focusing on practical applications. Invest time in understanding use cases before proof of concept. - Operationalising AI requires a clear governance framework. - Engage stakeholders early in the process for successful implementation. - Data quality and trust are critical for AI initiatives. - Business transformation is essential for successful AI adoption. - Leadership engagement amplifies the impact of AI projects. - Avoid chasing trends; focus on practical applications of AI. - Incremental scaling of AI solutions is more effective than large-scale rollouts. - AI should augment human decision-making, not replace it. - The future of AI involves navigating complexities and ensuring accountability. Chapters 00:00 Introduction to Ako Sabir and The Praxi Pod 03:01 Career Journey and Transformation Focus 06:00 Insights on the Insurance Sector 09:06 Data's Role in Insurance 11:59 Challenges in Financial Processes 14:58 Adoption of AI in Underwriting and Claims 18:03 Navigating Regulatory Compliance 21:04 Leadership and Technology Awareness 24:09 Balancing Innovation and Risk 27:11 Proof of Concepts and AI Integration 36:11 Navigating Technology Implementation Challenges 43:12 Operationalising AI in Regulated Industries 50:55 Transforming Business Through Data and AI 59:29 The Future of AI and Business Transformation

May 21, 202520 min

Gaurav Chawla and Vrashank Jain from Dell Technologies joining The Praxi Pod LIVE from DTW Las Vegas

In this conversation, Andrew Turner hosts Vrashank Jain and Gaurav Chawla from Dell Technologies to discuss the evolution of AI solutions, particularly focusing on the AI Factory 2.0. They explore how Dell is bridging the gap between AI and data, the importance of decentralised data processing at the edge, and the rise of generative AI. The discussion also highlights Dell's internal transformation, the prioritisation of use cases, and the future of AI and data management. Takeaways - AI Factory 2.0 focuses on bringing AI closer to data. - Decentralised data processing is essential for effective AI. - Generative AI is becoming more accessible to various industries. - Data is the fuel for AI, requiring seamless integration. - Companies should prioritise use cases that align with their core business. - AI is impacting all aspects of life, from industry to personal use. - Dell's AI solutions are designed for both large enterprises and smaller companies. - The transition to unstructured data is crucial for generative AI. - Internal transformation at Dell is guiding customer solutions. - The future of AI involves curating and managing diverse data types. Chapters 00:00 Introduction to Dell Technologies and AI Factory 02:08 AI Factory 2.0: Bridging Data and AI 06:10 Decentralised Data Processing and AI at the Edge 10:00 The Rise of Generative AI and Its Impact 13:57 Internal Transformation and Use Case Prioritisation 17:57 The Future of AI and Data Management

May 13, 202558 min

Angus Gow, The Art of Programming in the Age of AI

In this episode of the Praxi Pod, Andrew Turner spends time with Angus Gow, exploring his extensive background in AI, data integrity, and the evolving landscape of technology. Angus shares insights from his career, emphasising the importance of data quality in AI applications and the integration of AI in logistics. The conversation delves into the current state of AI, its rapid growth, and the creative aspects of programming, highlighting the need for innovation and adaptability in the tech industry. In this conversation, Andrew and Angus explore the multifaceted implications of AI in society, focusing on ethical considerations, the evolution of human-AI interaction, and the future of entrepreneurship. They discuss the potential for AI to manipulate human behaviour, the importance of ethical development practices, and the contrasting visions of AI's future as depicted in popular culture. The dialogue also touches on the consolidation of AI tools in business, the impact on development teams, and the transformative potential of AI for aspiring entrepreneurs. Takeaways AI has been a long-standing interest for Angus. Data integrity is crucial for successful AI applications. The evolution of AI has been rapid in recent years. Logistics presents unique challenges that AI can help solve. Creativity is essential in programming and technology development. The current state of AI is reminiscent of the early internet days. Understanding data management is key to leveraging AI effectively. AI's limitations include a lack of divine inspiration. The hype around AI can be overwhelming and distracting. Future job skills will focus on creativity and adaptability. AI can manipulate human behavior, raising ethical concerns. The ethics of AI should focus on its potential to manipulate. AI technologies are powerful but must be used responsibly. Future AI may resemble Star Trek more than Terminator. Consolidation of AI tools is essential for effective business use. AI can enhance productivity but requires skilled developers. Junior developers may struggle with AI tools without experience. Prototyping with AI can lead to rapid development but may create technical debt. AI can lower barriers to entrepreneurship, enabling more people to start businesses. Creativity will differentiate businesses in an AI-driven future. Sound Bites "AI goes long, a long way back for me." "It's so much cheaper to get data right at the start." "We're still figuring out the best use of AI." "Programming is an art form." "AIs are not good at divine inspiration." "It's trying to make you happy as well." "These are powerful technologies." "I think it's actually Star Trek." "We've already rebranded it, it's Dreamcoding now." "AI will be all over that." "It's good, it's good, it's good. Fantastic." "You start again. You go, you just get them." Chapters 00:00 Introduction and Background 04:01 The Evolution of AI and Personal Journey 12:01 Data Integrity and Its Importance 18:04 Logistics and AI Integration 23:58 Current State of AI and Future Prospects 29:56 The Creative Aspect of Programming and AI Limitations 32:20 The Ethics of AI Manipulation 34:12 The Role of AI in Human Interaction 36:36 Future of AI: Star Trek vs. Terminator 42:49 Consolidation of AI in Business 49:39 The Impact of AI on Development Teams 55:09 The Future of Entrepreneurship with AI Angus Gow is a highly respected CTO and technology strategist with over 25 years’ experience leading digital transformation across fintech, insurance, supply chain, and e-commerce sectors . He has built and scaled complex systems across a wide range of organisations—from high-growth startups to global enterprises—delivering platforms that power financial products, real-time analytics, and AI-driven decision-making. Angus is the founder of FlowFoundry.io, a product lab, advisory and services business focused on applying emerging AI and open-source technologies to deliver scalable, cost-efficient systems. His work bridges deep technical expertise—including enterprise architecture, data engineering, and cybersecurity—with commercial insight, having guided companies through trade sales, platform overhauls, and product innovation. With a hands-on approach to leadership and delivery, Angus has led global engineering teams of over 200 people and managed multi-million-pound technology budgets. His recent work focuses on developing methodologies and architectural patterns for safe, scalable AI in enterprise settings. Links https://flowfoundry.io https://anjin.digital