In the era of machine learning (such as deep learning), data is the king. Large volumes of data are needed for model training purposes. To build machine learning models for business use, firms need to effectively collect, organize, store, and use data (e.g., process-, customer-, employee-related data, etc.) in order to harvest the values of machine learning.