Responsible AI Starts with Responsible Data: Building Trust at Scale

Don't Panic! It's Just Data

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 Data

Amy 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 Governance

Responsible 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 AI

The takeaway from this episode is clear and that is responsible AI starts with responsible data. For organisations looking to harness AI effectively:

1. Invest in data quality and governance — it is the foundation of all AI initiatives.
2. Embed ethical and legal principles in every stage of AI development.
3. Enable collaboration across teams to ensure transparency, accountability, and usability.
4. 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.

Takeaways

• AI 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 data oversight.
• Reputational damage from AI failures can be severe for organisations.
• Metadata plays a crucial role in data management and governance.
• Collaboration between data, AI, and development teams is essential.
• Data governance is a must-have, not a nice-to-have.
• Organisations need to enable their lines of business for effective AI implementation.
• Everyone is ready for AI, except for the quality of their data.

Chapters

00:00 The Importance of Responsible AI and Trusted Data

02:49 Defining Responsible AI and Data Governance

05:40 Challenges in Data Quality and Governance

08:51 Real-World Examples of Data Quality Issues

11:51 The Role of Employees in Data Governance

14:41 Successful AI Outcomes Through Responsible Data Practices

17:42 The Risks of AI Governance and Reputational Damage

20:42 Collaboration Across Data, AI, and Development Teams

23:34 The Future of Metadata and Data Management

26:42 Key Takeaways for Data and AI Leaders

About Informatica

Informatica, founded in 1993, is an enterprise data management company headquartered in Redwood City, California. The company provides software products for data integration, data quality, master data management, and data governance. With approximately 9,000 global customers across various industries, Informatica has positioned itself as a significant player in the data management market.