Once upon a time, data within the manufacturing and industrial sector could be easily locked up in filing cabinets. Today, the swift expansion of industrial internet of things and cloud technology has resulted in an accumulation of sensitive data; much of which is prime for analysis to improve the efficiency of existing technology and instigate the innovation of new solutions. Unfortunately, with more data, complex regulatory pressures follow. More often than not, these regulations limit the ability to share sensitive data with external parties for collaboration. If sensitive data can’t be shared, how do we train machine learning models? In this webinar, Nicolai Baldin, offers practical tips that will enable organisations to build modern data architectures, whilst maintaining ethics and privacy at the core of these initiatives.