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Product Momentum Podcast

128 / Trusting Data Quality: The Key to AI’s Future, with Scott Ambler

Feb 7, 2024
Agile data strategist Scott Ambler discusses the importance of data quality in AI, managing bias, and the role of humans in harnessing AI's potential and risks. Clean and high-quality data is crucial for successful AI implementation. Understanding and addressing biases in data is necessary. The podcast also explores the challenges faced by product managers when integrating AI tools and provides book recommendations on deep learning, machine learning, and AI.
32:48

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Quick takeaways

  • Data quality is crucial for AI applications, as low-quality data leads to low-quality AI-driven insights and fractures trust.
  • Understanding the limitations and potential biases of AI is vital to utilize it appropriately and effectively, separating the hype from the reality.

Deep dives

Data quality is critical for AI and decision-making

Data quality is crucial not only for AI but for various aspects of decision-making. Organizations that have neglected data quality over time are now facing the challenge of poor data leading to low-quality AI-driven insights. AI operates at the speed and scale of technology and the internet, which means low-quality data can quickly result in significant problems. Fixing data quality issues is essential, especially when utilizing AI for data-driven decisions, business intelligence, and data warehousing.

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