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.

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.

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.

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.