AI You Can Trust: Embracing Data Integrity Throughout the Development Lifecycle

With AI, it’s not just garbage in, garbage out. It’s garbage in, garbage everywhere. AI-based decision-making can have profound business impacts and, unfortunately, trigger harmful consequences.

A leading cause of AI issues is the failure to use high-integrity data throughout the development lifecycle. It’s true! Ensuring that you use trustworthy data every step of the way leads to the success of any AI initiative.

Our session, “AI You Can Trust: Embracing Data Integrity Throughout the Development Lifecycle,” highlights how to achieve data integrity for AI. We share a cautionary tale about the repercussions of neglecting data integrity in enterprise AI development and show you how to avoid these pitfalls.

We explore cloud-native solutions that deliver data integrity, ensuring AI systems make precise predictions, automate processes effectively, and avoid costly mistakes by:

• Minimizing bias, improving accuracy and reliability, and enhancing understanding of the problem using all of the available, relevant, and critical datasets
• Training models with data that meets rigorous quality metrics—meaning it must be accurate, complete, validly structured, standardized, and free of duplicates
• Enriching AI training data with trusted third-party data and spatial insights to enhance output accuracy and contextual relevance

If you want to trust your AI, you must first trust your data. Take advantage of the opportunity to learn more about data integrity for AI, and let’s ensure your initiatives succeed!