#45: Data Quality and AI

Stay Sharp with Razorleaf

In this insightful episode of Razorleaf's "Stay Sharp" podcast, hosts Jen Ferello and Jonathan Scott delve into the foundational topic of data quality in the realm of Artificial Intelligence (AI). They explore why high-quality data is crucial for AI algorithms and discuss key factors that determine data reliability and relevance. This episode is a must-listen for anyone in the digital engineering and manufacturing space looking to understand the importance of data quality in AI.

Key Discussion Points:

The Role of Data Quality in AI:

• Importance of having accurate and precise data.
• The old adage: "Garbage in, garbage out."

Foundational Aspects:

• Training and validating AI models.
• Ensuring the first step is correct to avoid compounding errors.

Data Relevance and Reliability:

• Selecting relevant data for training AI models.
• Avoiding the inclusion of outdated or irrelevant data.

Challenges in Data Quality:

• Understanding data behavior across different departments.
• Avoiding biases and ensuring comprehensive data integration.

Maintaining Data Integrity:

• Regularly updating and securing data.
• Ensuring regulatory compliance and avoiding bad data input.

Building Trust in AI:

• Creating transparency in AI processes.
• Building user trust through consistent and reliable data outputs.

Future Applications:

• Practical applications in the Product Lifecycle Management (PLM) community.
• Importance of getting ready for AI advancements by focusing on data quality.

Thank you for joining us on this episode of "Stay Sharp with Razorleaf." We hope this conversation has provided you with valuable insights into the importance of data quality in AI and how to ensure your data is reliable and relevant for AI applications. Until next time, stay sharp!

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