<p>Machine Learning (ML) technologies have the potential to revolutionize industrial manufacturing by enhancing production efficiency, defect reduction, and intelligent process control. However, deploying ML in manufacturing faces significant challenges due to poor data quality. Industrial data is often incomplete or imperfect, and obtaining high-quality labeled data is resource-intensive. Attempting to fit ML models on low-quality data wastes time and resources. Yet often it is not clear if the data is of sufficient quality to attempt fitting or not; there are always some amount of imperfect missing data elements. These challenges highlight the need for robust data quality assessment and screening methodologies to guide further data preprocessing or sufficiency for ML model training. While various data quality metrics exist in the literature, their expression for and application directly to the manufacturing quality control domain is unexplored. This paper introduces the Tao Index, a novel metric combining topological and statistical methods to assess industrial data quality comprehensively. It integrates local topological metrics, such as distance, density, and intrinsic dimension, with global statistical measures, including Maximum Mean Discrepancy (MMD) and Kolmogorov-Smirnov (K-S) distance. The Tao Index is a rapid priori predictor of ML model performance, helping avoid investments in low-performance models and guiding data cleanup, collection, and trend analysis. Extensive experiments on benchmark and real-world datasets demonstrate that the Tao Index provides realistic data quality assessments, with deviations from estimated ground truth of good or poor fits under 10% in all separable datasets. It outperforms existing metrics in multiple manufacturing scenarios and generalizes well across industrial domains. The Tao Index is envisioned as a standard first step for ML analysis in manufacturing, ensuring efficient and effective data utilization.</p>