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.

中文翻译：

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讨论“具有大数据的经验：从数据科学家的角度考虑”

在现代生产中，不良质量的发生频率不足以提供与优质质量的有意义的比较。模拟是生成人工数据的有用工具。每个数据科学家都应开发分支机构相关领域的知识。他们应该清楚地了解什么以及如何记录和归档数据。通常，给定的分析任务不是唯一的还是真正的任务。反思任务并考虑其背后的背景和业务目标将很有帮助。沟通是好的数据科学的关键问题。