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Guest: Charlotte Ledoux – Data & AI Governance Expert Artificial Intelligence is transforming the way companies operate. But as AI adoption accelerates, a new challenge is emerging: how do you govern AI responsibly? In this episode of Let's Talk Data, Charlotte Ledoux explains why AI governance is becoming a critical business priority and how organizations can balance innovation, compliance, security, and risk management. Building on the foundations of data governance, we explore what companies need to put in place to scale AI successfully and avoid costly mistakes. In this episode, you'll learn: The difference between data governance and AI governance Why AI governance has become a strategic issue for business leaders The risks of "shadow AI" and uncontrolled AI adoption How to build an AI governance framework The importance of AI committees, guardrails, and clear ownership How the European AI Act is shaping the future of AI governance Practical first steps to implement AI governance in your organization The takeaway: AI can unlock tremendous business value, but without the right governance framework, it can also introduce significant risks. Organizations that act now will be better positioned to scale AI responsibly, securely, and with confidence. Brought to you by DQE – Data Quality Everywhere. Hosted on Ausha. See ausha.co/privacy-policy for more information.

Guest: Charlotte Ledoux - Data & AI governance expert In this episode of Let’s Talk Data, we take a step back to explore a foundational concept for any data-driven organization: data governance. As companies generate and rely on more data than ever, the challenge is no longer access - it’s trust, structure, and usability of data. With Charlotte Ledoux, we break down what data governance really means, where it comes from, and how it supports data quality, compliance, and business performance. The discussion highlights why data governance has become a critical business priority. From poor decision-making to failed AI initiatives, weak data foundations create real risks for organizations. On the other hand, a strong governance framework enables companies to unlock the full value of their data and scale AI with confidence. We also move into practical considerations: how to implement data governance within an organization, where to start, and the common challenges companies face. Is there a one-size-fits-all model, or does each company need its own data governance strategy? The takeaway: in a world saturated with data, success depends not on how much data you have, but on how well you manage, trust, and activate it to drive business outcomes. Brought to you by DQE – Data Quality Everywhere. Hosted on Ausha. See ausha.co/privacy-policy for more information.

Guests : Léonard Jochem and Steve Collinge - Managing Director - Insight Retail Group  How can retailers unlock more value from their data? In this episode of Let’s Talk Data, we explore one of the most critical forces shaping retail in 2026: the growing strategic importance of data. Retailers today operate in a world of unprecedented data abundance. Sales data, customer data, search behaviour, social signals, logistics information: the amount of information available to retailers has never been greater. Yet paradoxically, many companies still struggle to turn this data into actionable insights and better commercial decisions. Together with Steve Collinge, retail expert and industry advisor, we explore why retailers are often overwhelmed by the very data that should give them a competitive edge. This conversation covers: why retailers are sitting on mountains of data but failing to use it effectively how AI is transforming the way retailers analyse and activate data why data trust and governance are critical to decision-making how organisations can move from fragmented data silos to a coherent data strategy why retailers with physical stores hold a unique data advantage over pure e-commerce players The key takeaway: in retail, data is no longer just an operational resource: it is becoming the core driver of commercial strategy. Let’s Talk Data is a podcast exploring the intersection of data, AI and business transformation. Brought to you by DQE – Data Quality Everywhere. Hosted on Ausha. See ausha.co/privacy-policy for more information.

Guests : Léonard Jochem and Steve Collinge - Managing Director - Insight Retail Group What are the key challenges facing retailers in 2026 ? In this two-part episode of Let’s Talk Data, we explore the structural challenges shaping retail in 2026 - and what truly separates leaders from laggards in a permanently changed environment. Retail is no longer defined by disruption alone, but by execution. Customer expectations, cost pressure, technology capability and data maturity are colliding, forcing retailers to rethink long-held assumptions. Together with Steve Collinge, we examine why the physical store is being re-engineered rather than replaced, how convenience has become the primary competitive battleground, and why data is shifting from operational by-product to strategic commercial asset. The discussion also tackles range rationalisation as a profit lever and explains why, in an AI-enabled world, people and expertise are becoming stronger differentiators - not weaker ones. The takeaway: the gap between retail leaders and losers is widening, driven less by ambition and more by disciplined execution. Part 2 dives deeper into the data foundations retailers must fix to compete in 2026. Brought to you by DQE – Data Quality Everywhere. Hosted on Ausha. See ausha.co/privacy-policy for more information.

Guests: Philippe Boulanger and Dylan Anderson - Director of Data Strategy, Analytics & AI at Atombit, creator of “The Data Ecosystem” In Episode 2, Philippe Boulanger and Dylan Anderson explore why AI has become the most powerful companion to data, and why it raises the stakes on data quality like never before. Where business intelligence and analytics once required specialist skills, long lead times, and retrospective insights, AI now puts answers directly into the hands of business users. But this new accessibility comes with a paradox: AI is only as good as the data it consumes. Poorly structured or low-quality data doesn’t just produce wrong answers, it actively misleads AI systems and amplifies errors. The episode explains why structured, contextualised data and smaller, specialised AI models grounded in a company’s own information outperform broad, generic approaches, reducing hallucinations and increasing reliability at scale. The conversation then tackles the root of the problem: decades of accumulated “data quality debt.” Many organisations treated data quality as a nice-to-have, leaving gaps in ownership, governance, and processes that AI is now exposing. Dylan outlines how leading companies turn this challenge into a competitive advantage by investing in people, processes, and design. The episode also highlights how AI itself can help repay that debt, from deduplication and anomaly detection to standardising data entry and improving compliance.  The takeaway is clear: companies that fix their data foundations first are the ones that can build trustworthy AI, move faster, and make success repeatable. Brought to you by DQE - Data Quality Everywhere. Hosted on Ausha. See ausha.co/privacy-policy for more information.

Guests: Philippe Boulanger and Dylan Anderson – Director of Data Strategy, Analytics & AI at Atombit, creator of The Data Ecosystem Why do organisations generate more data than ever, yet still struggle to turn it into meaningful insight? In this two-part conversation, host Philippe Boulanger sits down with Dylan Anderson to cut through the noise and return to what truly matters: structured, reliable data as the foundation for analytics, AI, and business impact. In Episode 1, they explore why insights remain scarce despite data abundance, what “structured” and “reliable” data really mean, and how weak standards, siloed systems, and inconsistent inputs quietly undermine even the most advanced AI initiatives. Dylan shares real-world lessons, including a digital twin that couldn’t scale due to poor data quality, and outlines the practical steps leaders can take to strengthen their data foundations fast. A clear, actionable discussion that challenges the idea that “more data” is the answer, showing instead why real value comes from clarity, structure, and trust. Episode 2 will dive into how AI can turn data quality from a challenge into a competitive advantage. Brought to you by DQE – Data Quality Everywhere. Hosted on Ausha. See ausha.co/privacy-policy for more information.