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/

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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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 !!!

Takeaways

• Ole's first book focuses on the importance of data catalogs.
• Data catalogs are often underutilized in organizations.
• User adoption is crucial for the success of data technologies.
• Ole emphasizes the need for a bridge between technology and business.
• The concept of the MetaGrid helps coordinate multiple metadata repositories.
• Metadata is defined as being in two places at once.
• The role of reference librarians can be applied to data management.
• Ole's background in library science informs his approach to data.
• Understanding metadata can improve data management practices.
• Ole's new book addresses the challenges of metadata management.

Sound bites

• "Data catalogs are where metadata goes to die."
• "Metadata is in two places at once."
• "It's a tech book, but it's a weird tech book."

Chapters

00:00 Introduction to Ole Olsen Bagneux

02:41 The Importance of Data and AI

05:41 Ole's Academic and Professional Journey

10:55 Understanding Data Catalogs

13:50 User Adoption and Data Catalogs

19:14 The Fundamentals of Metadata Management

25:01 The Journey of Metadata Management

27:49 Understanding the IT Landscape

31:08 The MetaGrid Concept

35:59 Defining Metadata

42:10 The Role of the Reference Librarian

48:16 Bridging the Gap in Data Management

Jessica Talisman 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

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

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/

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

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

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 

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 industry

This 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.

Chapters

00:00 Introduction to the Insurance Data Roast

04:51 When Algorithms Go Wild

09:57 Correlation vs. Causation

10:27 Claims Hall of Fame

14:05 Digital Dinosaurs

18:01 Data Privacy: The Emperor's New Clothes

22:13 Navigating Insurance and Financial Apps

24:30 The Quirky Side of Data and Death Lists

26:14 Interdepartmental Dynamics: Underwriters vs. Data Scientists

29:26 The Philosophy of Interdepartmental Warfare

32:56 Curation as a Service: Transforming Data Management

44:17 Future Trends in Insurance and AI Adoption

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.

Chapters

00:00 Introduction to the Love Your Data Event

03:04 Expert Panel Introductions and Backgrounds

07:04 Kahoot Quiz: Engaging the Audience

20:04 Panel Discussion: Future of Data and AI

22:01 Integrating AI into Management Mindset

26:20 Navigating the AI Hype Cycle

30:00 The Evolution of Agentic AI

35:10 Balancing Innovation with Responsibility

39:52 The Inspiration behind Curation as a Service

46:49 Introducing Praxi Curation as a Service (CaaS)

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.

Chapters

00:00 Introduction and Overview of 2024 Insights

02:55 Generative AI: Maturation and Realistic Expectations

05:58 Looking Ahead: Predictions for 2025

09:00 Data Quality: The Foundation for Success

11:52 Insurance Industry: Adapting to New Realities

14:56 Final Thoughts and Predictions for the Future

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.

Chapters

00:00 Introduction and Insights from 2024

03:00 The Role of AI in Data Management

05:55 Challenges of Data Quality and Legacy Systems

09:06 The Importance of Change Management in Data Strategy

11:54 Defining ROI in Data Initiatives

14:55 Collaboration Across Teams for Data Success

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.

Chapters

00:00 Introduction and Overview of 2024

02:47 The Noise of 2024: Insights and Challenges

05:55 Focus and Value in 2025

08:57 Tech Adaptability and Problem-Solving

11:45 Navigating the Chaos: Executive Challenges

15:08 Partnerships and the Future of Tech

17:58 Value-Driven Problem Solving

21:09 Conclusion and Future Outlook

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.

Chapters

00:00 Introduction and Christmas Greetings

01:11 Reflections on AI Progress in 2024

07:25 Navigating Economic Challenges in 2025

11:30 Understanding DORA Regulation and Its Impact

19:54 Conclusion and Future Insights

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.

Chapters

00:00 Introduction to the Praxi - Gift of Insight Series

01:06 Insights on Data and AI in 2024

06:20 The Importance of Resilience in Data Management

10:10 The Role of Human Decision-Making in Data

12:55 Translating Data Quality into Business Value

Chapters

00:00 Welcome to the Praxi Pod

10:04 Generative AI: Hype vs. Reality

22:06 The Intersection of AI and Software Engineering

27:59 Shifting Paradigms in AI Deployment

34:07 Organizational Structures for AI Teams

42:07 Exploring Opportunities in AI and Requirements Gathering

47:52 AI's Role in Continuous Learning and Governance

55:07 The Future of Engineering Skills and Organizational Change

01:01:45 The Rise of AI Agents and Knowledge Management

Patrick 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 Development

Patrick 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/@jedi4ever

Enjoy and if you listen to the FULL episode you will get Patrick's insight on his 2025 predictions

Takeaways

• Organizations 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.

Chapters

00:00 Introduction to Stephen Harris and His Journey

02:47 The Importance of Data Management and AI Readiness

05:54 Regulatory Compliance and Its Impact on Data Management

09:04 The Role of AI in Data Validation and Customer Experience

12:10 Understanding AI and Machine Learning in Business

14:54 The Need for Education in AI and Data Management

18:00 Validating AI Outputs and Managing Edge Cases

20:47 The Challenges of Data Ownership and Governance

24:05 The Role of Chief Data Officers in Organizations

27:08 The Future of AI and Data Management Roles

29:52 Data Curation and Classification in the AI Era

32:49 The Importance of Metadata and Data Quality

35:55 The Future of AI and Its Impact on Organizations

Chapters

00:00 Introduction and Background

02:15 The Resurgence of Data Governance

06:04 Challenges in Governing AI and Machine Learning Models

09:31 Adoption of AI Across Industries

13:29 Driving Progress in AI through Small Innovation Hubs

19:45 Understanding Data Mesh and its Principles

20:55 The Role of Human Oversight in AI Governance

21:10 The Concept of Data Mesh

23:24 The Evolution of Data Management

25:43 The Importance of Data Classification and Curation

28:36 The Role of Data Quality in Outcomes

29:26 The Challenges of Unstructured Data

30:30 The Impact of AI on Tedious Tasks

32:56 Motivating Data Owners and Providing Tools

36:57 The Boundaries of Human Creativity and AI

39:38 The Future of AI and Ethical Considerations

Our 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 

Takeaways

• The future of work is being shaped by AI and technology, and organizations need to adapt to stay competitive.
• Hybrid work is here to stay, and organizations should embrace flexibility and create a more inclusive work environment.
• Leadership plays a crucial role in driving the adoption of AI and technology in organizations.
• Diversity and inclusion are not just moral imperatives, but also lead to better business outcomes.
• Having an AI policy and training employees on how to use AI effectively is essential for organizations. AI is revolutionizing the future of work, and individuals and organizations need to embrace and manage this technology.
• Learning and development, upskilling, and understanding the tasks to be done are crucial for driving innovation and change.
• Leadership plays a key role in driving digital transformation and creating a culture that embraces AI.
• Incentives are important to motivate individuals to embrace AI and become more productive.
• AI has the potential to create a more equitable society and allow individuals to focus on meaningful work.

Chapters

00:00 Introduction and Busy Schedule

02:03 The Impact of AI and Technology on the Future of Work

08:56 Embracing Hybrid Work and Flexibility

13:29 The Role of Leadership in Driving Change

19:14 The Business Benefits of Diversity and Inclusion

21:37 The Importance of an AI Policy and Training

29:08 The Role of Learning and Development in the Future of Work

31:13 The Responsibilities of Leaders in Digital Transformation

32:47 Motivating Individuals to Embrace AI

33:45 AI's Impact on Work-Life Balance

39:01 The Power of Purpose and Passion in Work

43:12 AI's Potential to Create an Equitable Society

Takeaways

• The data space has experienced explosive growth in recent years, with advancements in technologies like artificial intelligence revolutionizing industries and society.
• Access and quality of data are major challenges in the data space, with issues of data sovereignty and legal ramifications adding complexity to the adoption of AI.
• Organizations need to embed data workers with hybrid skills into their teams and focus on the practicality and purpose of AI adoption, considering cost and bottom-line impact.
• The future of data workers lies in conversational data, where AI technologies enable more productive and efficient interactions with data.

Chapters

00:00 Introduction and Setting the Stage

01:00 The Explosive Growth of the Data Space

03:10 Challenges and Opportunities in the Data Space

05:02 The Importance of Data Integrity and Foundation

06:20 The Impact of Data on Different Industries

08:16 The Challenges of Access and Quality in Data

10:09 The Role of Artificial Intelligence in Data

11:35 The Importance of Real-Time and High-Quality Information

12:20 Organisational Changes and Skills Gap

17:34 Embedding Data Workers and Hybrid Skills

21:23 The Future of Data Workers and Conversational Data

22:01 The Challenges of Data Sovereignty and Legal Ramifications

29:43 Balancing Innovation and Practicality in AI Adoption

31:16 Considerations for AI Adoption: Cost and Purpose

32:17 Closing Remarks and Future Possibilities

Alasdair Anderson, with over 20 years in data technology, has worked in major financial firms and now leads the EMEA region for Protegrity. A recognised expert in data management and anti-financial crime, he's won multiple industry awards and is part of the Global Scot Network, mentoring organisations expanding globally.

In this conversation, Andrew and Edosa discuss the challenges and opportunities in the data world. They emphasize the importance of aligning data strategies with organizational goals and asking the right questions to ensure value creation. They also highlight the need to consider the human dimension and the impact on the workforce. The conversation explores the rapid pace of change and the need for organizations to adapt and navigate the complexities of data and AI. Edosa provides insights into the Middle East market and the unique constraints and opportunities in the region. The conversation concludes with key takeaways, including the importance of not reinventing the wheel, involving the right people, and focusing on return on investment.

Takeaways

• Align data strategies with organizational goals and ask the right questions to ensure value creation.
• Consider the human dimension and the impact on the workforce when implementing data and AI solutions.
• Adapt and navigate the rapid pace of change in the data world, considering the constraints and opportunities in different markets.
• Focus on not reinventing the wheel, involve the right people, and prioritize return on investment.

Chapters

00:00 Introduction and Setting

00:46 Purpose of the Conversation

03:07 The Data World and Challenges

08:01 The Value Equation

09:18 Defining Value in Different Perspectives

11:40 Importance of Alignment and Asking the Right Questions

16:15 The Pace of Change and the Future Workforce

20:10 Navigating the Buzz and Constraints

24:49 Challenges and Opportunities in the Middle East

29:08 Top Priorities for Data Leaders

36:04 Summary and Takeaways

Edosa is a highly accomplished data and AI leader, strategist, and investor, with extensive global experience. He's a Chief Data Analytics & Privacy Officer and serves on boards. With a strong academic background and a focus on people and culture, he's a published author, coach, and renowned global speaker, recognised with numerous prestigious awards.

In this episode, Andrew speaks with Roberto Maranca, Data Officer at an energy business, about the challenges and considerations of implementing new technology and data-driven processes. They discuss the limitations of viewing new technology as a panacea and emphasize the importance of having a well-designed architecture. They also explore the resistance to change and the difficulty of switching off old systems. Additionally, they highlight the value of lean thinking and streamlining processes to create more customer value. Finally, they stress the significance of effective communication and understanding in data initiatives.

Takeaways

• Data plays a crucial role in business and should be treated as a product that requires continuous improvement.
• Agile data modeling is essential for adapting to the complexity and changing demands of the business environment.
• Sustainability and the impact of data centers on CO2 emissions are important considerations for data leaders.
• The concept of a digital twin and enterprise intelligence can provide valuable insights and support decision-making.
• The hype surrounding AI and GenAI should be approached with caution, and the focus should be on translating these technologies into tangible business outcomes.
• The role of a Chief Data Officer evolves from focusing on dashboards to data to decision-making, with a strong emphasis on understanding the business and translating data initiatives into business value. Viewing new technology as a panacea is a mistake; it is important to have a well-designed architecture and consider the limitations of any new tool.
• Resistance to change is common, and organizations often struggle with switching off old systems.
• Applying lean thinking and streamlining processes can help create more customer value and improve efficiency.
• Effective communication and understanding are crucial in data initiatives to avoid miscommunication and alignment issues.

Chapters

00:00 Introduction and Background

02:10 The Role of Data in Business

06:24 The Importance of Agile Data Modeling

09:12 The Challenges of Sustainability and Data Centers

11:19 The Need for a Digital Twin and Enterprise Intelligence

15:22 The Hype Cycles and the Role of AI and GenV

20:47 The Journey of a Chief Data Officer

25:49 The Importance of People and Business Outcomes

28:57 Key Takeaways

31:47 The Limitations of New Technology

32:33 Resistance to Change

33:18 Lean Thinking and Streamlining Processes

33:47 The Importance of Communication and Understanding

With almost 30 years of IT and data experience, Roberto worked at Nissan, Ford, General Electric Capital, Lloyds Banking Group, and Schneider Electric. In 2023, he became Schneider Electric Energy Management BU's Data Officer, focusing on sustainability. He holds a master's degree in Aeronautical Engineering from "Federico II" Naples University and enjoys sailing, gardening, and cooking.

Chapters

00:00 Introduction and Background

03:01 The Importance of Data

04:05 The Hype Cycle of Data Industry

07:06 Challenges in Data Work Productivity

09:44 The Quantum Shift in Technology

11:49 The Need for Good Quality Data

13:34 The Role of Generative AI

19:27 Practical Advice for 2024

23:07 Conclusion

Specialised in Big Data and governance, with roles at Waterline Data, Hortonworks, and NYSE Euronext. Founded Praxi, developed three Data Catalogs, and achieved over $60M in sales.

We are launching the Praxi Pod as a weekly “Pod” of one, two or few people coming together to discuss all things data – every week, punchy, informative, discussing the topics that are top of mind in the industry and how data leaders are planning to tackle things head on.

So, in the Praxi Pod, we want to delve into the experiences of our guests, to understand their journey to-date, what keeps them awake at night, their views on data leaders in their organisation and industry, what’s going on with their POV [Point of View] on all the hype and buzz words that are flying around….and what’s their bulletproof plan to make life better for everyone who touches, feels and makes things happen with data

We're going to try to take you, the audience, on a journey through the stories and lessons of experienced data leaders who know you cannot be shy or quiet to drive the transformations that many organisations need to make best use of data. What are the mistakes they have made, what are the wins and what would they advise people to stay clear of during their own data journey

#PraxiPod #GetDataDoneRight.

Given Data is being held up as a key company asset, how are organisations supporting this at Board, Management and at the front line. What’s still missing and what’s essential foundations. How do you apply crawl, walk then run to build stakeholder support – deliver wins, create value then step up the game

#MakeDataWorkforYou #TheDataJourney

Your Host for The Praxi Pod

Andrew Turner has worked for corporates, start-ups within corporates and start-ups in UK, EMEA, USA and Asia and proud alumni of organisations including GE, SAP, Tesco, WANdisco, EE & Telefonica and now as GM, Praxi working with Data Leaders across the world to make things happen and have real value and impact. Andrew is the successful Founder & Host of The G&T Sessions Podcast talking about Growth, Tech with Gifted & Talented People now passed 100+ Episodes https://www.linkedin.com/in/iamandrewjturner/