Can AI Agents Help You Achieve Data Trust and Compliance?

Tech Transformed

The ability to effectively manage and optimise data is key in an organisation today. But with the sheer volume and complexity of enterprise data, traditional methods are struggling to keep up with the change. This is where the agentic AI approach has swooped in to transform how organisations handle their most valuable resource.

"The promise of AI and agentic AI is that we're now building very meaningful automation into the platform such that these teams of 10 are now able to basically actually capture all of the metadata about all of the data cataloged across their entire company," stated Corey Keyser, the head of artificial intelligence (AI) at Ataccama.

In this episode of the Tech Transformed podcast, Shubhangi Dua, a B2B tech journalist and Podcast host at EM360Tech speaks with Keyser from Ataccama, about agentic AI, data quality, and data governance.

They explore how intelligent automation is shaping enterprise data management, the role of AI in improving data quality, and the importance of trust in AI systems. Additionally, Keyser shares significant insights on Ataccama's unique approach to data governance, practical applications of their AI agent, and how they are keeping pace with the constantly changing AI regulations.

While the speed and efficiency of AI are undeniable, the question of trust remains. Keyser addressed this directly: "The short answer is you can never fully trust these automations, right?

“That's why it's really critical to always have data stewards that we will serve. We will always have data engineers that we will serve. We're just looking to improve their productivity. We always assume that there will be humans in the loop who are verifying the tasks orchestrated by AI agents."

Ataccama's One AI Agent exemplifies the practical application of these principles. Keyser added that the AI agent can go and create data quality rules in bulk. “Go through the evaluation and testing of those quality rules in bulk, and then also assign the rules in bulk. Something that would take potentially weeks, can now actually kind of take hours depending on the person."

Takeaways

• Agentic AI is about dynamic planning and semi-autonomous task execution.
• Data governance involves cataloging and managing organisational data.
• Data quality assessment is crucial for ensuring high trust in data.
• AI can significantly speed up the creation of data quality rules.
• Human oversight is essential in AI-driven automation processes.
• Atacama's AI agent improves productivity for data management teams.
• Regulatory compliance is a growing concern for AI applications.
• User experience is key to successful AI integration in organisations.
• The relationship between data and AI is symbiotic and essential.
• Organisations must adapt to evolving AI regulations and standards.

Chapters

00:00 Introduction to Agentic AI and Data Governance

02:41 Understanding Data Quality and Governance

06:30 The Role of AI in Data Management

11:14 Practical Applications of One AI Agent

15:46 Differentiating Atacama in the AI Landscape

19:37 Case Studies of Transformation

20:05 Integrating Data Quality and Governance

22:07 Navigating Regulatory Changes

24:19 Enhancing User Experience with AI

25:29 Key Takeaway for CIOs