Responsible AI Starts with Responsible Data: Building Trust at Scale

We live in a world where technology moves faster than most organisations can keep up. Every boardroom conversation, every team meeting, even casual watercooler chats now include discussions about AI. But here’s the truth: AI isn’t magic. Its promise is only as strong as the data that powers it. Without trust in your data, AI projects will be built on shaky ground.In this episode of Don’t Panic, It’s Just Data podcast, Amy Horowitz, Group Vice President of Solution Specialist Sales and Business Development at Informatica, joins moderator Kevin Petrie, VP of Research at BARC, to tackle one of the most pressing topics in enterprise technology today: the role of trusted data in driving responsible AI. Their discussion goes beyond buzzwords to focus on actionable insights for organisations aiming to scale AI with confidence.Why Responsible AI Begins with DataAmy opens the conversation with a simple but powerful observation: “No longer is it okay to just have okay data.” This sets the stage for understanding that AI’s potential is only as strong as the data that feeds it. Responsible AI isn’t just about implementing the latest algorithms; it’s about embedding ethical and governance principles into every stage of AI development, starting with data quality.Kevin and Amy emphasise that organisations must look at data not as a byproduct, but as a foundational asset. Without reliable, well-governed data, even the most advanced AI initiatives risk delivering inaccurate, biased, or ineffective outcomes.Defining Responsible AI and Data GovernanceResponsible AI is more than compliance or policy checkboxes. As Amy explains, it is a framework of principles that guide the design, development, deployment, and use of AI. At its core, it is about building trust, ensuring AI systems empower organisations and stakeholders while minimising unintended consequences. Responsible data governance is the practical arm of responsible AI. It involves establishing policies, controls, and processes to ensure that data is accurate, complete, consistent, and auditable.Prioritise Data for Responsible AIThe takeaway from this episode is clear and that is responsible AI starts with responsible data. For organisations looking to harness AI effectively:Invest in data quality and governance — it is the foundation of all AI initiatives.Embed ethical and legal principles in every stage of AI development.Enable collaboration across teams to ensure transparency, accountability, and usability.Start small, prove value, and scale — responsible AI is built step by step.Amy Horowitz’s insight resonates beyond the tech team: “Everyone’s ready for AI — except their data.” It’s a reminder that AI success begins not with the algorithms, but with the trustworthiness and governance of the data powering them.For more insights, visit Informatica.TakeawaysAI is only as good as its data inputs.Data quality has become the number one obstacle to AI success. Organisations must start small and find use cases for data governance.Hallucinations in AI models highlight the need for vigilant

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