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  • Building AI Without Foundations
    2025/09/10

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    In this episode of the Data Analytics Chat podcast, we dive deep into the world of data and AI with Linda Powell, former Deputy Chief Data Officer. Linda shares insights from her unique career journey, spanning roles at the Federal Reserve, Treasury Department, Citibank, and more. She discusses the differences and similarities between public and private sectors, the importance of data foundations in AI, and offers advice for those looking to build their careers in data management. Learn about challenges, pivotal career moments, and the value of education and curiosity in navigating the evolving data landscape.

    00:00 Public vs. Private Sector: A Personal Insight
    01:08 Introducing Linda Powell: Career Highlights
    01:52 Linda's Career Journey: From Economics to Data Management
    04:20 Public vs. Private Sector: Motivations and Misconceptions
    10:57 Defining Moments and Career Setbacks
    14:09 The Importance of Education and Curiosity
    18:57 Overcoming Challenges and Embracing Leadership
    21:56 Learning Leadership Through Books
    22:43 The Importance of Kindness in Leadership
    23:12 Building a Motivated Team
    24:00 Leadership by Example
    25:33 Transition to Data and AI
    26:06 The Hype and Strategy of AI
    27:20 The Speed and Complexity of AI
    30:42 The Importance of Data Foundations
    36:05 AI in Sensitive Industries
    39:34 Balancing Quick Wins and Long-Term AI Strategy
    42:29 Starting Points for AI Foundations
    44:11 Conclusion and Final Thoughts

    Thank you for listening!

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    45 分
  • Manoj Mohan
    2025/09/03

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    43 分
  • How To Solve Business/ Consumer Problems Using AI
    2025/08/27

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    In this episode of Data Analytics Chat, host Ben Parker interviews Bilal Ahmed, Chief AI and Data Officer at Paul Tech AG, about his career journey and invaluable insights into the world of data and AI.

    Bilal shares his experiences from his extensive career, highlighting pivotal moments, challenges, and the transition from a technical to a leadership role. He discusses the importance of self-belief, taking calculated risks, and the role of persistence and resilience in achieving success. We look into solving business and consumer problems using AI, emphasising the importance of asking the right questions and having the correct data.

    Bilal also touches upon the hype surrounding generative AI and the need to educate businesses on realistic expectations.

    00:00 Introduction and Personal Journey
    00:12 Believing in Yourself and Embracing Failure
    01:14 Career Highlights and Future Plans
    03:17 Transitioning from Technical to Leadership Roles
    04:49 Learning and Mentorship
    06:54 Key Advice for Innovation and Change Management
    07:53 Stakeholder Management and Career Setbacks
    13:08 AI in Business: Misconceptions and Realities
    35:09 Balancing Quick Wins with Long-Term Solutions
    37:42 Conclusion and Final Thoughts

    Thank you for listening!

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    39 分
  • How Do We Thread Gen AI Into Our Processes?
    2025/08/20

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    In this episode of Data Analytics Chat, host Ben Parker welcomes Tony Giordano, the Senior Partner and VP for Global Data Platform Services at IBM. They discuss Tony's extensive career, including pivotal moments and lessons learned from working globally in the data space.

    The conversation covers the transformative impact of Gen AI, the evolution of data architecture, and the strategic integration of AI into business processes. Tony shares valuable insights on navigating technological advancements, maintaining domain expertise, and preparing for a future driven by data innovations.

    00:00 Introduction to the Exciting World of Data Consulting
    01:08 Welcome to Data Analytics Chat
    01:45 Tony Giordano's Career Journey
    05:31 The Impact of Working in Different Countries
    08:48 Leading a Global Team at IBM
    10:43 Cultural Lessons from Japan
    22:56 Career Setbacks and Lessons Learned
    24:45 The Future of Data and AI
    28:58 The Calculator Debate and Modern Tools
    30:31 The Importance of Domain Expertise
    32:08 Celebrating Data Luminaries
    33:29 The Evolution of Data Patterns
    35:40 The Shiny Object Syndrome
    39:48 The Role of Gen AI in Business
    44:20 Cultural and Organizational Challenges
    48:57 The Future of Data and Gen AI

    Thank you for listening!

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    58 分
  • What Happens When We Adopt AI – The Change Management Journey
    2025/08/13

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    Kartick Kalaimani, VP Enterprise MDM Data and Transformation Lead at Dentsu, joins Ben Parker to demystify AI adoption and its critical human element. This insightful episode delves into common misconceptions about AI, highlighting the importance of clean data, clearly defined use cases, and strong change management. Kartick shares his extensive career journey and provides actionable advice on bridging the gap between technology and people, emphasizing that successful AI transformation is ultimately a human journey focused on belief and adoption.

    In this episode, you will learn:
    * Kartick's four key career phases: mastering operations, quality and transformation, scaling transformation across finance and media, and data transformation.
    * The importance of pushing beyond your comfort zone for career growth and leadership development.
    * How to overcome major career setbacks by learning from challenges and focusing on the human side of change.
    * Kartick's unique edge in connecting business value, technology, and people.
    * Common misconceptions about AI adoption, such as viewing it as "plug and play" or self-sufficient.
    * Why AI transformation is fundamentally a human journey that requires active engagement and belief.
    * How businesses can effectively align AI adoption with real business problems rather than just chasing trends.
    * Strategies for leaders to address fear, resistance, and cultural challenges during AI transformation.
    * The crucial role of reskilling and upskilling the workforce for successful AI integration.
    * Advice for non-technical professionals on how to effectively participate in the AI journey.
    * Why the biggest myth about AI is that it serves as a "silver bullet" solution.
    * Kartick's vision for a future AI-ready organization where humans and AI co-create value.

    Timestamps:
    00:02:00 Welcome and Introduction
    00:02:40 Kartick's Career Journey: Four Key Phases
    00:05:45 Pushing Beyond Your Comfort Zone for Growth
    00:08:00 Gaining Leadership Expertise and Mentorship
    00:09:20 Major Career Setbacks and Their Lessons
    00:12:30 Finding Your Edge in a Competitive Environment
    00:13:50 Common Misconceptions in AI Adoption
    00:18:10 Aligning AI with Real Business Problems
    00:21:50 Human and Cultural Challenges in AI Transformation
    00:24:00 How Leaders Can Approach AI Challenges
    00:26:00 Advice for Non-Technical Listeners on AI
    00:27:30 Busting the Biggest Myth About AI in Business
    00:30:10 What a Well-Transformed AI-Ready Organization Looks Like
    00:32:00 Turning Point in Successful Change Initiatives
    00:33:15 Closing Remarks

    Thank you for listening!

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    34 分
  • Why Have Firms Not Focused on Data Readiness
    2025/08/06

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    In this episode of Data Analytics Chat, host Ben Parker interviews Nipa Basu, a seasoned data analytics executive. Nipa shares her captivating career journey from pursuing a PhD in economics in India to becoming a Chief Data Officer in the United States. They discuss pivotal moments that shaped her career, the evolution of data science, and what distinguishes good leaders from great ones in the tech industry. They also delve into why many firms struggle with data readiness and the importance of balancing technical expertise with soft skills.

    In this episode, you will learn:

    • Nipa Basu’s career journey from technical contributor to C-suite executive in data analytics
    • The difference between good and great leadership in data science
    • The importance of storytelling and communication skills for data professionals
    • How to build and lead large, high-performing data teams
    • The evolving landscape of data science and the need for continuous learning
    • Why many companies struggle with data readiness before launching AI initiatives
    • Common misconceptions about data readiness and the role of the C-suite
    • Practical steps for organisations to become truly data-ready
    • The impact of organisational culture, skills gaps, and leadership on data success
    • Real-world examples of career setbacks and adaptability in the data industry


    00:00 Introduction and Early Career

    01:37 Welcome to Data Analytics Chat

    02:22 Nipa Basu's Career Journey

    03:23 Leadership in Data Science

    05:20 Navigating to the C-Suite

    00:00 Transitioning Between Roles

    21:30 Building and Leading Teams

    27:07 Challenges in Data Readiness

    41:48 Practical Steps for Data Readiness

    45:00 Conclusion and Final Thoughts

    Thank you for listening!

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    46 分
  • Turning AI into ROI: How to Build Value, Not Just Models
    2025/07/30

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    In this episode of Data Analytics Chat, we welcome David Ellison, Chief Data Scientist and Director of AI Engineering at Lenovo.

    We’ll explore David's career journey from studying computer science to transforming businesses with AI.

    Learn about his pivot into data science, lessons from working at the postal service, and his strategies for delivering ROI with AI projects.

    In this episode, you will learn:

    · How to turn AI initiatives into real business ROI, not just prototypes

    · Why most AI projects fail and what successful companies do differently

    · How to align AI with business KPIs from day one

    · Why soft skills like communication and storytelling are critical for data professionals

    · How to fail fast and deliver value quickly in AI projects

    · What executives actually want to hear from data teams

    · How to stand out in a competitive data job market

    · David Edison’s personal journey: from PhD graduate to Chief Data Scientist at Lenovo

    · Real-world case studies: including saving millions in manufacturing and helping NASCAR win races

    00:00 Introduction to Data Analytics Chat Podcast

    00:28 Guest Introduction: David Ellison

    01:04 David's Career Journey

    04:08 Transitioning Across Industries

    06:08 Learning and Adapting in AI

    09:30 Defining Moments in David's Career

    13:48 Soft Skills and Leadership

    21:56 Achieving ROI with AI

    33:21 Real-World AI Success Stories

    37:07 Final Thoughts and Advice

    Thank you for listening!

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    38 分
  • Can We Trust GenAI?
    2025/07/23

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    In this episode of Data Analytics Chat, host Ben Parker welcomes Michael Hicks, Executive Director at Novartis.

    We explore Michael's unconventional career journey, which transitions from academia to business and from procurement to advanced analytics within the pharmaceutical industry.

    Michael shares his thoughts on career decisions, the importance of positivity in teams, and the essential skills for success. The episode also dives into the crucial discussion on the trustworthiness of generative AI, its ethical implications, and the balance between human intuition and AI decision-making.

    00:00 Introduction and Guest Welcome

    00:46 Michael Hicks' Career Journey

    01:25 Transition from Academia to Business

    02:56 Entering the Pharmaceutical Industry

    04:34 Role in Analytics and Procurement

    06:30 Challenges and Career Pivots

    10:08 Building Trust and Relationships

    16:41 Defining Career Moments

    24:10 Career Setbacks and Lessons Learned

    27:16 Gaining a Competitive Edge

    29:31 Innovative Team Leadership

    29:50 Creating a Positive Team Culture

    31:44 Hiring for Empathy and Curiosity

    33:51 Trust in AI Systems

    40:03 AI Accountability and Governance

    44:01 Ethical Boundaries for AI Decisions

    45:58 Human Context and AI Limitations

    49:42 Deferring to AI vs. Human Judgment

    53:40 Podcast Conclusion and Final Thoughts

    Thank you for listening!

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    54 分