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TechTarget - 9 Data Quality Issues That Can Sideline AI Projects
This article outlines common data quality issues that can derail AI initiatives, such as inaccurate, incomplete, and improperly labeled data. It emphasises the necessity of clean, well-prepared data for reliable AI model performance.
Business Wire - Data Quality is Not Being Prioritized on AI Projects, a Trend that 96% of U.S. Data Professionals Say Could Lead to Widespread Crises
This report reveals that 81% of companies still struggle with AI data quality, putting the return on investment of AI initiatives and overall business stability at risk. It highlights the disconnect between AI investments and data quality prioritization.
Forbes - Why 85% Of Your AI Models May Fail
This article discusses the high failure rate of AI projects, attributing a significant portion to poor data quality or insufficient relevant data. It provides insights into common data-related pitfalls and strategies to mitigate them.
RAND - The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI.
This report identifies five primary reasons for AI project failures, emphasizing that many organizations lack the necessary data to adequately train effective AI models. It underscores the importance of high-quality data and provides recommendations for successful AI implementation.
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