This whitepaper outlines the foundational role of data in effective AI implementation. It emphasizes that AI systems require large volumes of high-quality, diverse, and unbiased data to train models and avoid flawed outcomes. Key components include metadata management, data governance, and a well-structured architecture. It highlights how clean, well-labeled data and metadata enhance model accuracy and scalability. The document features real-world case studies where Mastech helped clients optimize shipping, banking, and marketing operations using AI/ML, resulting in improved insights, efficiency, and business growth.

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