FIMA Europe 2024

21 - 22 November, 2024 *With Tech Innovation Day on 20 November

Novotel London West, United Kingdom

Dan Onions

Global Head of Data Management Quantexa

Dan is a strategic advisor and thinker in enabling organizational and IT change, with a deep understanding of technology. He has 24 years’ experience in transformation consulting and delivery, working across financial services, government, education, and logistics. Prior to working at Quantexa, he was the founder and CEO of DASH Project Management App.

Main Day One | 21st November

2:40 PM Innovation Presentation - What are the Biggest Challenges in Data Quality and How Far Can AI Go To Solve Them?

In this presentation, Dan Onions, Global Head of Data Management at Quantexa, will delve into the pressing question on every data professional's mind: "How can AI help me?" As AI technologies, particularly LLMs, become increasingly integral to data management strategies, ensuring the quality and reliability of these systems' outputs is paramount.


Dan will explore the critical role of foundational data quality in harnessing AI effectively and responsibly. He will address key challenges, such as achieving consistency and accuracy in AI-generated outputs and aligning them with regulatory standards already on the horizon. Attendees will gain insights into practical applications of AI in the real world, understanding how to make AI outputs on data trustworthy across the entire organization.

By the end of the session, participants will be equipped with actionable strategies to navigate the intersection of technical innovation and regulatory compliance, ensuring their data is of the highest quality to leverage AI responsibly.

 

 

Agenda: 


The Importance of Data Quality in AI

Understanding the foundational role of data quality in AI effectiveness and common data quality challenges in AI-driven systems.


Exploring the Potential of AI in Data Management

How AI, including Large Language Models (LLMs), is transforming data management strategies and case studies of AI applications in real-world data quality scenarios


Key Challenges in AI-Driven Data Quality

Ensuring consistency and accuracy in AI-generated outputs and addressing the alignment of AI systems with emerging regulatory standards


Practical Strategies for Trustworthy AI Outputs

Best practices for making AI outputs reliable across an organization and actionable strategies for integrating AI responsibly within data management processes


Q&A Session

Open floor for attendee questions and discussion of specific concerns and queries related to AI and data quality

Check out the incredible speaker line-up to see who will be joining Dan.

Download The Latest Agenda