LEAD WITH DATA Podcast

Season 6 - Episode 3 - "The one question that changed HR’s influence on business performance forever" with Sarah Novelli

HR doesn’t lose influence because it lacks insight. It loses influence because it talks in the wrong language. In this episode of Lead With Data, we speak to a senior business executive who didn’t come from a data background like our usual guests but fundamentally changed her seat at the table by doing one thing differently: connecting workforce data directly to revenue, margin and productivity. No dashboards for show. Just data translated into decisions executives actually care about. This conversation shows what happens when people metrics stop being “HR numbers” and start being business performance indicators. If you work in HR, people and culture or any function that wants to influence outcomes, not just report activity, this one is worth your time. Key takeaways * People data is business performance data, not HR reporting * Vacancies aren’t an HR problem, they’re delayed revenue * If you can’t link initiatives to margin or productivity, they won’t get traction * Influence comes from translation, not more dashboards

Season 6 - Episode 2 - “Why data and AI teams need translators not just tools” with Clare Kitching

WHY DATA TEAMS NEED TRANSLATORS, NOT JUST TOOLS In this episode of Lead With Data, Rina Gami sits down with Clare Kitching, a senior data leader who works closely with organisations navigating complex data and AI decisions. The conversation explores why many data and AI initiatives stall and not because of tooling gaps. The conversation unpacks how data teams are navigating generative AI, agentic AI and increasing pressure to move quickly, often without the shared language, context and alignment required to succeed. Clare shares what she is seeing first-hand across organisations grappling with executive expectations, shadow AI, governance concerns and the widening distance between technical teams and business leaders. This episode focuses on the growing importance of translators in data teams' people who can bridge strategy, technology and value and what leaders should be paying attention to before scaling AI. In this episode, we cover: * Why tools alone do not solve AI and data challenges * The shift from traditional data initiatives to generative AI and agentic-based AI * The critical role of senior executive sponsorship in AI success * How translators help close the gap between technical teams and the business * Practical ways organisations can start or scale AI responsibly * Evolving roles in data teams, including analytics translators and AI governance specialists * Common challenges such as shadow AI, data quality and organisational buy-in * Indicators that signal true readiness to scale AI * Why value-led AI initiatives matter more than experimentation TIMESTAMPS 00:00 – AI as a high-return capability in modern data teams 01:32 – Claire’s career journey and move into consulting 03:16 – What has changed in data and AI over the last five years 04:00 – Where organisations are really at in their AI journeys 05:24 – Pressure to move quickly and what gets missed 06:03 – Executive sponsorship and its impact on outcomes 07:30 – Data team structures and emerging gaps 08:53 – Organisational barriers to AI adoption 10:38 – Shadow AI and responsible use 12:34 – Initial assessment points when working with organisations 14:47 – Asking the right questions to align AI to value 17:11 – Why communication is now a core data capability 19:09 – Telling the data story in a way the business understands 22:18 – Structuring data teams for sustainable AI outcomes 25:26 – Evolving roles across data, engineering and governance 28:11 – Everyday AI, generative AI and agent AI explained 32:21 – Leadership priorities when advancing AI initiatives 33:49 – Signs an organisation is ready to scale AI responsibly 35:11 – Maintaining momentum without creating risk 36:08 – Hiring for AI capability beyond technical skills 38:25 – When and why to involve external expertise 41:17 – The value of an external perspective 44:48 – Avoiding stagnation in AI programs 45:27 – Communication and storytelling as critical capabilities 46:16 – Leadership alignment and organisational readiness 46:44 – Final reflections and next steps RESOURCES & LINKS * Experian Data Studio [https://www.experian.com.au/business/solutions/data-quality/aperture-data-studio] https://www.experian.com.au/business/solutions/data-quality/aperture-data-studio– supporting stronger data governance and trust * Claire Kitching Linkedin [https://www.linkedin.com/in/clarekitching/] Website - cambiq.com.au [https://www.linkedin.com/safety/go/?url=https%3A%2F%2Fcambiq.com.au%2F&urlhash=zui4&isSdui=true&lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3BRf6nW8NZQ069vycPtJRCUg%3D%3D]

Season 6 - Episode 1 - Why "fail fast" is a powerful mindset if you want to succeed in Data Innovation

In this episode, Nick Jameson shares his journey from aerospace engineering and defence to data leadership. He discusses why the fail fast mindset is important for Data Innovation, how high-stakes environments influence building resilient data platforms, the importance of problem-solving, stakeholder engagement and managing technical debt. Gain insights into effective leadership, balancing innovation with foundational stability and the future of data engineering and analytics. Data Engineering, Data Leadership, High-Stakes Environments, Data Platforms, Analytics, Tech Debt, Leadership, AI, Data Strategy Key topics: * Transferable skills from aerospace to data * Building resilient and scalable data platforms * Managing technical debt and platform maturity * Leadership principles in data teams * The impact of AI and emerging technologies Chapters 00:00 Embracing Opportunities and Learning from Failure 02:50 Transitioning from Aerospace to Data Analytics 05:58 Core Principles from High-Stakes Engineering 08:33 Decision-Making Under Pressure 11:41 Balancing Processes and Flexibility in Data Analytics 14:36 Managing Expectations and Prioritization 17:16 Building Robust Data Platforms for Future Challenges 20:04 The Interplay of Data Engineering and Advanced Analytics 22:25 Building Robust Data Pipelines 23:23 The Risks of Premature Analytics 25:46 Challenging Assumptions in Data Teams 27:16 Balancing Investments in Data Tools 28:43 Leadership Lessons from Engineering 31:03 Managing Stress in Leadership 34:38 Advice for Nonlinear Moves into Data 36:07 Building a Modern Data Platform 38:26 Investing in People as a Leader 40:15 The Future of Data Engineering and Analytics Resources Experian Data Solutions [ https://www.experian.com.au] Nick Jameson on LinkedIn [https://www.linkedin.com/in/nickjameson]

Episode 20 - AI at the tipping point: Governance, Trust and ROI

As we wrap up the final episode of Season 5 of the Lead with Data podcast, we explore why so many AI pilots fail. Joining me is Noel Hanssens, and together we unpack the biggest challenges organisations face when implementing AI, including governance gaps, misconceptions and misalignment with strategy. We also discuss the need for AI literacy across teams and the critical role of CFOs in guiding AI investments. Packed with real-world insights, this episode highlights how to unlock AI’s potential while keeping implementation responsible and strategic.

Episode 19 - The Data You Don’t Control Controls You with Pushkar Mishra

Joining me in this week’s episode is Pushkar Mishra, who brings sharp insights into the powerful connection between data risk, governance and organisational transformation. Pushkar shares why neglecting data quality can be costly, how engaging stakeholders effectively can showcase the true business value of data governance and the pivotal role data champions play in driving change. We also dive into the ethical implications of AI and the emerging risks shaping the future of data management. This episode is packed with actionable insights for leaders looking to turn data governance into a strategic advantage that fuels growth and innovation. Key topics we discuss include: * The Interconnection of Transformation and Data Risk * Challenges in Perception of Data Governance * The Cost of Ignoring Data Governance * Execution Challenges in Data Governance Strategies * Understanding Business Value in Data Governance * Future Data Risks and Ethical Considerations