Source: Technology Decisions

Technology Decisions outlines critical strategies for preparing organizational data to maximize the success of AI initiatives. The article emphasizes the importance of data quality, completeness, and relevance as foundational elements for effective AI model training and deployment. It explains common pitfalls such as biased, incomplete, or poorly labeled data that can undermine AI performance.

The piece also provides practical guidance on data cleaning, integration, and governance frameworks to ensure data is trustworthy and compliant with privacy regulations. The article stresses collaboration between IT, data scientists, and business stakeholders to align data preparation with AI project goals, ultimately improving decision-making and value extraction from AI technologies.