As enterprises implement AI and automation, data quality is a crucial factor, often overlooked by industry leaders. 72% of enterprises have adopted AI for at least one business function, but the success of these initiatives hinges on quality data. Poor data quality can lead to poor patient care, flawed financial reports, increased operational risks, and regulatory penalties. High-quality data can enable AI and AI to provide output that are accurate, reliable, and context-rich, enabling users to make informed, confident decisions. The author outlines simple strategies for gathering and sharing data, including data governance and management practices. The effectiveness of hyperautomation depends on data quality, as well as its intelligence behind AI and ML models.
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