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FabLabs are makerspaces, where you can transform your sketch into a real product with easy-to-use machines such as 3D printers, laser cutters, or desktop CNC mills. However, FabLabs, like most production environments, have a problem: the digital manufacturing machines provide only a one-way-interaction: human to computer to machine – from bits to atoms. A 3D printer, for example, produces objects unaware of its environment. But, what if we could change the machine so it could react to outside influences, such as other machines, people, or environmental conditions? This is the starting premise for the project in which we implemented a vast number of sensors at FabLab Graz and carried out Big Data Analyses. The research project is structured along the Big Data approach under supervision of Prof. Viktor Mayer-Schönberger from Oxford University, and is divided into three main project phases: First, integration of sensors in the laser cutter, 3D printer, CNC mill and also the environment of FabLab Graz. Second, the collection, analysis and processing of the collected data. In the third phase we focus on the derivation of use cases and product improvements based on data correlation. The aim of the project is to establish a smart FabLab that provides insights into operational enhancements and new product concepts to optimize machine interaction. These product concepts and the basic set up of the sensor system are open source and can be implemented easily at other FabLabs throughout the worldwide community. The paper describes the first and second phase of this ongoing research project in detail and gives first insights in phase three. Data, however, is unstoppable and while it is being gathered new correlations or enhancements can be discovered even in the course of writing this sentence.