Data Quality in the Interface of Industrial Manufacturing and Machine Learning

University essay from Uppsala universitet/Företagsekonomiska institutionen

Abstract: Innovations are coming together and are changing business landscapes, markets, and societies. Data-driven technologies form new or increase expectations on products, services, and business processes. Industrial companies must reconstruct both their physical environment and mindset to adapt accordingly successfully. One of the technologies paving the way for data-driven acceleration is machine learning. Machine learning-technologies require a high degree of structured digitalization and data to be functional. The technology has the potential to extract immense value for manufacturers because of its ability to analyse large quantities of data. The author of this thesis identified a research gap regarding how industrial manufacturers need to approach and prepare for machine learning technologies. Research indicated that data quality is one of the significant issues when organisations try to approach the technology. Earlier frameworks on data quality have not yet captured the aspects of manufacturing and machine learning as one. By reviewing data quality frameworks, including machine learning or manufacturing perspectives, the thesis aims to contribute with an area-specific data quality framework in the interface of machine learning and manufacturing. To gain further insights and to complement the current research in the areas, qualitative interviews were conducted with experts on machine learning, data and industrial manufacturing. The study finds that ten different data quality dimensions are essential for industrial manufacturers interested in machine learning. The insights from the framework contribute with knowledge to the data quality research, as well as providing industrial manufacturing companies with an understanding of machine learning data requirements.

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