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Discussion of “Experiences with big data: Accounts from a data scientist’s perspective”
in Quality Engineering, DOI: 10.1080/08982112.2020.1755689, Taylor & Francis, Juni 2020
In modern production, bad quality does not happen often enough to provide a meaningful comparison with good quality; simulation is a useful tool to generate artificial data. Every data scientist should develop branch relevant domain knowledge. They should have a clear view what, and how, data is recorded and archived. Quite often the given analytical task is not the only one or the real one. It is helpful to reflect on the task and to take into account the background and the business goals behind it. Communication is the key issue for good data science.
Keywords: big data, data science, data quality, Industry 4.0, practitioner experiences