Lovric Mario, Krebs Sarah, Cemernek David, Kern Roman
2018
The use of big data technologies has a deep impact on today’s research (Tetko et al., 2016) and industry (Li et al., n.d.), but also on public health (Khoury and Ioannidis, 2014) and economy (Einav and Levin, 2014). These technologies are particularly important for manufacturing sites, where complex processes are coupled with large amounts of data, for example in chemical and steel industry. This data originates from sensors, processes. and quality-testing. Typical application of these technologies is related to predictive maintenance and optimisation of production processes. Media makes the term “big data” a hot buzzword without going to deep into the topic. We noted a lack in user’s understanding of the technologies and techniques behind it, making the application of such technologies challenging. In practice the data is often unstructured (Gandomi and Haider, 2015) and a lot of resources are devoted to cleaning and preparation, but also to understanding causalities and relevance among features. The latter one requires domain knowledge, making big data projects not only challenging from a technical perspective, but also from a communication perspective. Therefore, there is a need to rethink the big data concept among researchers and manufacturing experts including topics like data quality, knowledge exchange and technology required. The scope of this presentation is to present the main pitfalls in applying big data technologies amongst users from industry, explain scaling principles in big data projects, and demonstrate common challenges in an industrial big data project