The Praxi Pod

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.Chapters00:00 Introduction to Data and AI01:29 Expert Backgrounds in Data04:56 Shifting Focus from Technology to Business Outcomes10:19 Understanding Data Governance and Its Role15:54 The Importance of Organizational Design in Data Management19:22 The Role of CDOs and Data Ownership22:33 Challenges in AI Implementation and Accountability26:07 Understanding the Black Box of Data27:57 Cultural Implications of AI Usage29:30 Navigating Technology Investments30:49 The Challenge of Proving Value32:35 The Role of AI in Cost Savings34:34 The Shadow Side of AI Adoption36:44 The Consumerization of AI39:06 Foundational Data Challenges40:33 The Importance of Business Objectives43:04 Final Thoughts for ExecutivesLinkedinBrian 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/

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.Chapters00:00 Introduction to AI and Data Classification02:32 Understanding Data Labelling, Classification, and Curation05:36 The Importance of Data Quality and Curation08:09 Exploring Dark and Gray Data11:07 The Risks of Ignoring Dark Data13:54 Benefits of Automated Data Classification16:18 Real-World Applications of Data Classification19:20 The Role of Domain Knowledge in Data Classification21:54 Conclusion and Future of Data ClassificationSubscribe to be notified of future content from the Praxi.ai Team

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.TakeawaysData 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."Chapters00:00 Introduction and Special Announcement01:13 The Launch of 'Data Excellence'03:12 Understanding Data Excellence06:07 Cultural Challenges in Data Excellence07:50 Exercises for Data Fitness10:25 Data as a Team Sport11:38 The Role of Coaches in Data Management13:09 Distinguishing Data from AI15:50 The Importance of Measuring Data Debt20:49 Creating Sustainable Data Practices23:08 Understanding Data Challenges in Business24:36 The Role of Regulation in Data Management26:11 Data Governance vs. Data Management28:35 Treating Data as a Product34:33 The Human Element in an AI-Driven World39:36 Achieving Data Excellence NirvanaRoberto 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 2025Excellent, insightful and enjoyable episodeEnjoy !!!

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.TakeawaysOle'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."Chapters00:00 Introduction to Ole Olsen Bagneux02:41 The Importance of Data and AI05:41 Ole's Academic and Professional Journey10:55 Understanding Data Catalogs13:50 User Adoption and Data Catalogs19:14 The Fundamentals of Metadata Management25:01 The Journey of Metadata Management27:49 Understanding the IT Landscape31:08 The MetaGrid Concept35:59 Defining Metadata42:10 The Role of the Reference Librarian48:16 Bridging the Gap in Data 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/@jessicatalismanLinkedIn: https://www.linkedin.com/in/jmtalismanWebsite: Ontologypipeline.comTakeawaysKnowledge 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.Chapters00:00 Introduction to Jessica Talisman and Her Work02:45 The Role of Semantic Engineering in Knowledge Management05:24 The Evolution of Roles in Organizations08:09 Knowledge Workers and Tools for Productivity10:52 The Importance of Context in Data Management13:35 Challenges with AI Implementation in Organizations16:34 Cross-Functional Collaboration for AI Success19:35 Historical Context of Library Science and AI22:35 Navigating the AI Hype Cycle25:20 The Future of AI Tools in Organisations28:09 Stewardship of Knowledge Assets in Organizations35:32 The Dynamic Role of Libraries in Education38:55 AI Partnerships and Knowledge Structuring43:23 Building Knowledge Ecosystems48:55 Understanding Ontologies and Taxonomies57:16 Creating Semantic Infrastructures59:06 The Future of Knowledge Management

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.TakeawaysMeta'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.Chapters00:00 Introduction to AI Investment Trends06:09 Reinforcement Learning and Its Challenges11:56 Compliance as a Competitive Advantage17:57 The Role of Data Curation in AI Success

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.TakeawaysThe 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.”Chapters00:00 Introduction and Background03:08 Recognition and the Role of CDOs05:54 The Evolution of the CDO Role09:13 Transformation vs. Line Management in Data Roles11:57 The Importance of Data in Decision Making14:57 AI and Its Impact on Data Management18:11 The Journey of Trusting AI in Decision Making28:14 The Evolution of AI and the Turing Test30:08 Business Implications of AI33:37 Understanding AI Risks and Uncertainties35:13 Customer Expectations and AI Integration37:26 FOMO in AI Adoption39:19 Data Quality and Machine Learning40:58 The Challenge of Explainability in AI42:00 Bias and Ethical Considerations in AI48:26 Integrating Traditional Models with AI49:44 The Skills Gap in AI Development56:28 The Evolution of AI in Software Development01:00:08 The Future of Work: Hybrid Organisation

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.Chapters00:00 Introduction to Ako Sabir and The Praxi Pod03:01 Career Journey and Transformation Focus06:00 Insights on the Insurance Sector09:06 Data's Role in Insurance11:59 Challenges in Financial Processes14:58 Adoption of AI in Underwriting and Claims18:03 Navigating Regulatory Compliance21:04 Leadership and Technology Awareness24:09 Balancing Innovation and Risk27:11 Proof of Concepts and AI Integration36:11 Navigating Technology Implementation Challenges43:12 Operationalising AI in Regulated Industries50:55 Transforming Business Through Data and AI59:29 The Future of AI and Business Transformation

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.Chapters00:00 Introduction to Dell Technologies and AI Factory02:08 AI Factory 2.0: Bridging Data and AI06:10 Decentralised Data Processing and AI at the Edge10:00 The Rise of Generative AI and Its Impact13:57 Internal Transformation and Use Case Prioritisation17:57 The Future of AI and Data Management

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.TakeawaysAI 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."Chapters00:00 Introduction and Background04:01 The Evolution of AI and Personal Journey 12:01 Data Integrity and Its Importance18:04 Logistics and AI Integration23:58 Current State of AI and Future Prospects29:56 The Creative Aspect of Programming and AI Limitations32:20 The Ethics of AI Manipulation34:12 The Role of AI in Human Interaction36:36 Future of AI: Star Trek vs. Terminator42:49 Consolidation of AI in Business49:39 The Impact of AI on Development Teams55:09 The Future of Entrepreneurship with AIAngus 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.Linkshttps://flowfoundry.iohttps://anjin.digital 

Welcome to the Praxi Insurance Data Roast with our expert panel of Alasdair Anderson, Edosa Odaro, Andrew Ahn and hosted by Andrew Turner. The conversation evolves around humorous anecdotes and insights from the insurance industry, focusing on data mishaps, algorithmic failures, and the challenges of legacy systems. The panelists share their experiences in a light-hearted roast format, discussing topics such as correlation versus causation, bizarre insurance claims, and the ongoing struggle with outdated technology. The session aims to entertain while shedding light on serious issues within the industryThis conversation delves into the complexities of the insurance industry, focusing on the interplay between traditional actuarial practices and modern data science.It highlights the challenges of data management, the quirks of data usage in insurance, and the evolving role of technology in shaping the future of the industry. The discussion also touches on the dynamics between underwriters and data scientists, emphasizing the need for collaboration to enhance outcomes. Finally, it explores and explains Praxi Curation as a Service, which aims to improve data quality and accessibility for insurance companies.Chapters00:00 Introduction to the Insurance Data Roast04:51 When Algorithms Go Wild09:57 Correlation vs. Causation10:27 Claims Hall of Fame14:05 Digital Dinosaurs18:01 Data Privacy: The Emperor's New Clothes22:13 Navigating Insurance and Financial Apps24:30 The Quirky Side of Data and Death Lists26:14 Interdepartmental Dynamics: Underwriters vs. Data Scientists29:26 The Philosophy of Interdepartmental Warfare32:56 Curation as a Service: Transforming Data Management44:17 Future Trends in Insurance and AI Adoption

The Love Your Data event, hosted by Andrew Turner, features a panel of data experts discussing the future of data and AI. The event included an interactive Kahoot quiz to engage the audience and insights from panelists about the transformative impact of generative AI in business. The conversation delves into the integration of AI into management practices, emphasising the importance of data quality and ethical considerations. It discusses the current hype cycle surrounding AI technologies and the evolution of Agentic AI. The speakers highlight the need for a balance between innovation and responsibility, particularly in regulated industries. The introduction of Curation as a Service (CaaS) is presented as a solution for effective data management and classification, aiming to enhance productivity and governance in organisations.Expert panel is Morgane Calenca, Angus Gow and Andrew Ahn.Chapters00:00 Introduction to the Love Your Data Event03:04 Expert Panel Introductions and Backgrounds07:04 Kahoot Quiz: Engaging the Audience20:04 Panel Discussion: Future of Data and AI22:01 Integrating AI into Management Mindset26:20 Navigating the AI Hype Cycle30:00 The Evolution of Agentic AI35:10 Balancing Innovation with Responsibility39:52 The Inspiration behind Curation as a Service46:49 Introducing Praxi Curation as a Service (CaaS)

We recorded a series of episodes with our Praxi Advisory Board and Andrew Ahn, our Founder & CEO to discuss what were their main takeaways from 2024, what they see as the learnings and what's happening in 2025.Enjoy this episode with Andrew Ahn recorded in early January 2025.In this episode of the Praxi - The Gift of Insight Series, Andrew Turner speaks with Andrew Ahn about the significant insights gained from 2024, particularly in the realm of generative AI and its maturation within various industries. They discuss the realistic expectations surrounding AI technology, the importance of data quality, and the evolving landscape of the insurance industry. Looking ahead to 2025, they share predictions about productivity, compliance, and the necessity for businesses to adapt to new realities. The conversation emphasises the critical role of data quality in achieving successful outcomes in analytics and decision-making.Chapters00:00 Introduction and Overview of 2024 Insights02:55 Generative AI: Maturation and Realistic Expectations05:58 Looking Ahead: Predictions for 202509:00 Data Quality: The Foundation for Success11:52 Insurance Industry: Adapting to New Realities14:56 Final Thoughts and Predictions for the Future

We recorded a series of episodes with our Praxi Advisory Board and Andrew Ahn, our Founder & CEO to discuss what were their main takeaways from 2024, what they see as the learnings and what's happening in 2025.Enjoy this episode with Morgane Calenca recorded in early January 2025.In this conversation, Andrew Turner and Morgane Calenca discuss the evolving landscape of data management and AI as they transition from 2024 to 2025. Morgane emphasises that while AI is becoming ubiquitous, it is not a panacea; rather, it is a tool that requires high-quality data to be effective. They explore the challenges of data quality, the importance of change management, and the need for collaboration across various teams to ensure successful data initiatives. Morgane also highlights the significance of defining ROI for data projects and the necessity of top management support in driving data transformation.Chapters00:00 Introduction and Insights from 202403:00 The Role of AI in Data Management05:55 Challenges of Data Quality and Legacy Systems09:06 The Importance of Change Management in Data Strategy11:54 Defining ROI in Data Initiatives14:55 Collaboration Across Teams for Data Success

We recorded a series of episodes with our Praxi Advisory Board and Andrew Ahn, our Founder & CEO to discuss what were their main takeaways from 2024, what they see as the learnings and what's happening in 2025.Enjoy this episode with Edosa Odaro recorded in late December 2024.In this episode of the Praxi - Gift of Insight Series, host Andrew Turner engages with Edosa Odaro to discuss the significant insights and challenges of 2024, particularly in the realms of technology and AI. They explore the overwhelming noise of information and the need for focus and value in 2025. The conversation delves into the adaptability of technology, the importance of partnerships, and the necessity of problem-solving in a rapidly changing environment. The episode concludes with reflections on the future outlook and the importance of prioritising meaningful objectives.Chapters00:00 Introduction and Overview of 202402:47 The Noise of 2024: Insights and Challenges05:55 Focus and Value in 202508:57 Tech Adaptability and Problem-Solving11:45 Navigating the Chaos: Executive Challenges15:08 Partnerships and the Future of Tech17:58 Value-Driven Problem Solving21:09 Conclusion and Future Outlook

We recorded a series of episodes with our Praxi Advisory Board and Andrew Ahn, our Founder & CEO to discuss what were their main takeaways from 2024, what they see as the learnings and what's happening in 2025.Enjoy this episode with Alasdair Anderson recorded in late December 2024.In this episode of Praxi - The Gift of Insight Series, host Andrew Turner engages with Alasdair Anderson to discuss the evolving landscape of AI and its implications for various industries. They reflect on the challenges faced in 2024, particularly the slow adoption of AI technologies in enterprises, and the economic hurdles anticipated in 2025. The conversation also delves into the upcoming DORA regulation and its potential impact on the financial services sector, emphasising the need for organisations to adapt to new compliance requirements. The episode concludes with insights on the future of AI and the importance of understanding its practical applications.Chapters00:00 Introduction and Christmas Greetings01:11 Reflections on AI Progress in 202407:25 Navigating Economic Challenges in 202511:30 Understanding DORA Regulation and Its Impact19:54 Conclusion and Future Insights

We recorded a series of episodes with our Praxi Advisory Board and Andrew Ahn, our Founder & CEO to discuss what were their main takeaways from 2024, what they see as the learnings and what's happening in 2025.Enjoy this episode with Roberto Maranca recorded in late December 2024.In this episode of the Praxi - The Gift of Insight series, Andrew Turner and Roberto Maranca discuss the significant insights and challenges surrounding data and AI as we approach 2025. They explore the rapid advancements in generative AI, the importance of resilience in data management, and the critical role of human decision-making in leveraging data effectively. The conversation emphasises the need for data leaders to translate data quality into actionable business value and foster critical thinking within their organisations.Chapters00:00 Introduction to the Praxi - Gift of Insight Series01:06 Insights on Data and AI in 202406:20 The Importance of Resilience in Data Management10:10 The Role of Human Decision-Making in Data12:55 Translating Data Quality into Business Value

In this episode of the Praxi Pod, host Andrew Turner speaks with Patrick Dubois about the evolving landscape of AI and technology. They discuss the hype surrounding generative AI, the importance of understanding its practical applications, and the need for organizations to adapt to new technologies. Patrick shares his journey through various roles in tech, emphasizing the significance of continuous learning and the intersection of AI with software engineering. The conversation also touches on the organizational structures needed to support AI initiatives and the implications for business operations. In this conversation, Andrew and Patrick delve into the evolving landscape of AI in software development, discussing the opportunities and challenges presented by new tools and methodologies. They explore the importance of collaboration, the need for continuous learning, and the role of AI in enhancing productivity while addressing its limitations. The discussion also touches on the future of engineering skills, the rise of AI agents, and the significance of knowledge management in navigating organizational changes and improving incident response.Chapters00:00 Welcome to the Praxi Pod10:04 Generative AI: Hype vs. Reality22:06 The Intersection of AI and Software Engineering27:59 Shifting Paradigms in AI Deployment34:07 Organizational Structures for AI Teams42:07 Exploring Opportunities in AI and Requirements Gathering47:52 AI's Role in Continuous Learning and Governance55:07 The Future of Engineering Skills and Organizational Change01:01:45 The Rise of AI Agents and Knowledge ManagementPatrick Debois, often hailed as “the Father of DevOps,” is a Belgian Tech professional and thought leader renowned for pioneering the cultural and technical paradigm that unites software development and IT operations. Born and raised in Belgium, Debois initially trained in computer science and spent the early part of his career working in both development and system administration roles, giving him firsthand experience with the friction and communication gaps that often hindered effective software delivery. In the late 2000s, Debois became increasingly focused on streamlining the collaboration between development teams and operations teams—two traditionally siloed groups within IT organizations. He was inspired by the principles of Agile, continuous integration, and continuous delivery, as well as ideas presented by John Allspaw and Paul Hammond about bridging Dev and Ops at the Velocity Conference in 2009. Recognizing the need for a dedicated forum to explore and refine these concepts, Debois co-organized the first DevOpsDays conference in Ghent, Belgium, in 2009. This intimate gathering of practitioners and thinkers is widely regarded as the birthplace of the DevOps movement. From that seminal event, DevOps spread rapidly throughout the global IT community, with Debois serving as a key advocate, educator, and facilitator. He helped define the ethos of DevOps, emphasizing culture, collaboration, automation, measurement, and sharing—often summarized as the “CAMS” principles. Debois’s approach highlighted the importance of breaking down silos, enhancing empathy, and encouraging continuous learning to improve software quality, delivery speed, and organizational resilience. Over the years, Debois has held various consulting and leadership roles at different technology companies, advising organizations on their DevOps transformations. He has spoken at numerous international conferences, written extensively on the topic, and maintained a strong presence in the DevOps and Agile communities. His influence extends beyond tooling and methodologies into the broader cultural changes that help organizations evolve and thrive in a fast-paced, technology-driven world. Today, Patrick Debois continues to shape the DevOps conversation through advocacy, research, and mentoring. His legacy endures in the global network of DevOpsDays events, the widespread adoption of DevOps practices, and the ongoing conversation around how best to integrate people, processes, and technology. Through his vision and community-building efforts, Debois remains a seminal figure in how modern IT organizations conceive, build, and deliver software.Renowned for his groundbreaking contributions to DevOps and DevSecOps, Patrick is once again at the forefront, navigating the evolving intersection of human intelligence, automation, and AI Native DevelopmentPatrick is currently the Principal Product engineer AI at Humans and code and can be found on Linkedin here https://www.linkedin.com/in/patrickdebois/But also has a great Youtube channel with great content https://www.youtube.com/@jedi4everEnjoy and if you listen to the FULL episode you will get Patrick's insight on his 2025 predictions

In this episode of the Praxi Pod, host Andrew Turner speaks with Stephen Harris, a seasoned expert in data management and AI. They discuss Stephen's extensive background in the data space, the importance of data management for AI readiness, and the impact of regulatory compliance on data practices. Stephen emphasizes the need for organizations to validate their AI outputs, understand data ownership, and the evolving role of Chief Data Officers. The conversation also touches on the significance of data curation, classification, and the future of AI in organizations.TakeawaysOrganizations must test AI internally before external deployment.Data management is crucial for AI readiness.Regulatory compliance drives better data practices in industries.AI should be used to validate and improve data quality.Education on AI and data management is still lacking.Data ownership issues can create internal conflicts.The role of Chief Data Officers is evolving and critical.Data curation and classification are foundational responsibilities.Metadata management is essential for data confidence.The future of AI will see a shakeout based on data quality.Chapters00:00 Introduction to Stephen Harris and His Journey02:47 The Importance of Data Management and AI Readiness05:54 Regulatory Compliance and Its Impact on Data Management09:04 The Role of AI in Data Validation and Customer Experience12:10 Understanding AI and Machine Learning in Business14:54 The Need for Education in AI and Data Management18:00 Validating AI Outputs and Managing Edge Cases20:47 The Challenges of Data Ownership and Governance24:05 The Role of Chief Data Officers in Organizations27:08 The Future of AI and Data Management Roles29:52 Data Curation and Classification in the AI Era32:49 The Importance of Metadata and Data Quality35:55 The Future of AI and Its Impact on Organizations

In this new episode of The Praxi Pod, Andrew Turner spends a very enjoyable time with Tiankai Feng talking about data governance and AI governance. Tiankai shares his approach to making data more approachable and fun, using his musical talents to create songs and parodies about data. They discuss the resurgence of data governance, driven by innovation and regulations. They also explore the challenges of governing AI and machine learning models, including data quality, bias, and ethical considerations. Tiankai emphasizes the importance of human oversight in AI governance. They touch on the adoption of AI in different industries and the role of small innovation hubs in driving progress. Tiankai also explains the concept of data mesh and its principles. Finally, Tiankai shares his role at ThoughtWorks in helping clients with data strategy and governance, including implementing data mesh projects. In this conversation, Andrew and Tiankai discuss the concept of data mesh and its relevance in today's data landscape. They explore the role of data governance and metadata management in ensuring data quality and usability. They also touch on the impact of AI on creativity and the challenges of ownership and responsibility in the AI era. The conversation concludes with a discussion on the future of AI and the need for organizations to embrace new technologies while considering legal and ethical implications.Chapters00:00 Introduction and Background02:15 The Resurgence of Data Governance06:04 Challenges in Governing AI and Machine Learning Models09:31 Adoption of AI Across Industries13:29 Driving Progress in AI through Small Innovation Hubs19:45 Understanding Data Mesh and its Principles20:55 The Role of Human Oversight in AI Governance21:10 The Concept of Data Mesh23:24 The Evolution of Data Management25:43 The Importance of Data Classification and Curation28:36 The Role of Data Quality in Outcomes29:26 The Challenges of Unstructured Data30:30 The Impact of AI on Tedious Tasks32:56 Motivating Data Owners and Providing Tools36:57 The Boundaries of Human Creativity and AI39:38 The Future of AI and Ethical ConsiderationsOur guest Tiankai Feng, is a Data & Analytics leader and author of "Humanizing Data Strategy". He is currently the Data Strategy & Governance Lead in Thoughtworks Europe and the Head of Marketing of DAMA Germany. With 11+ years experiences in Data Analytics, Data Governance and Data Strategy, he found a passion for the human aspect of data: how to collaborate, communicate and be creative around data. He is passionate about making data more understood, approachable and fun through unconventional methods like music and memes.Links: Book: https://technicspub.com/humanizing-data-strategy/LinkedIn: https://www.linkedin.com/in/tiankaifeng/Youtube: https://www.youtube.com/kaifeng