Towards a conceptual guideline for an economical assessment of manufacturing process data

Data will have an exponentially increasing economic value in the coming years. To harness the economic potential of data e.g. due to machine learning, data sharing is helpful to create the large amount of data needed to train models. For sharing data, the definition of prices for datasets is crucial. While current approaches exclusively take the data science perspective to evaluate the value of data based on quality dimensions, the consideration of a multidimensional assessability for transparent pricing is missing. The contribution of this paper is to sketch a conceptual guideline for assessing sensory-captured manufacturing process data from multidimensional perspectives.