Data is the foundation of every analytics and machine learning application. Data is the “raw material” that feeds into analytics and AI and, in turn, enables data modelling to optimize process, to predict machine failures before they happen and to support decision making. Tagging operating assets with low quality and noisy data at the start of the process is likely to generate inferior or inaccurate results at downstream, reducing the value that can be extracted from the data. Therefore, ensuring robust technology infrastructure to store, manage and contextualize data is a first step toward execution of digital transformation initiatives