Foundations First: Analysing the Dimensions of Data Quality for AI Use Case Success

​What makes data “fit for purpose” in AI systems?

As AI adoption accelerates, it's clear that good data is essential — but what qualifies as "good" isn’t universal. Defining data quality depends entirely on the context and goals of your specific AI use case.

Tune in at the LinkedIn Live Discussion titled, 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗶𝗿𝘀𝘁: 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗳𝗼𝗿 𝗔𝗜 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀.

On the 24th of April, join us for a live discussion featuring:

- Dr Martin Saerbeck, CTO and Co-Founder of AIQURIS - A TUV SUD Venture, brings 20+ years of experience in AI and certification, focusing on technology and regulation to develop safe, compliant, and performant AI systems. He contributes to international standards through ISO, CEN, IEEE SA, and other bodies.

- Dr. Daniel Kondermann, CEO and Founder of Quality Match GmbH, specialises in optimising dataset quality for AI, particularly in computer vision. His research includes metrics for dataset reliability and analysis of ambiguous annotations' impact on AI model accuracy.

The session will be hosted by Helene Mayne, Director of Customer Success at AIQURIS.

Key topics include:

1. How data quality shapes AI model performance
2. Applying standards to structure and assess datasets
3. Defining “good” dataset based on AI use case context

24th of April 2025, Thursday
17:00 Singapore Time | 11:00 Central European Time

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