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/

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

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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 !!!

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

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/@jessicatalisman

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

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

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/

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

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

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