In the developed countries, the smart factory is being built in earnest to increase the manufacturing competitiveness. The Smart Factory is equipped with sensors on machines based on 4M2E (Man, Machine, Material, Method, Energy, Environment) which is the element of physical factory and converts (digitizes) physical signals into digital signals for smooth communication, and it is essential to connect the machines in the factory, parts, factory, manufacturing processes, people, and supply chain partners to each other, and to enhance the personalized value desired by customers through intelligent (smart) operation of smart factory platform using collected data. In the Smart Factory, the key is the data analysis ability through securing big data in and outside of the factory, and in order to do this, there is a growing need to build an infrastructure to secure high-quality big data. This study examines a large data platform component for big data infrastructure building process and internal/external data acquisition, data modeling which is the foundation of data standardization for securing high quality data and sharing real time data with cooperation partners, and the components for data quality management for securing of continuous high-quality big data. Through this, a plan to build a big data infrastructure is suggested for securing continuous high quality big data in Smart Factory. It is expected that it will be a guide to build big data infrastructure for companies wishing to introduce Smart Factory.