三巨头：通过解决公司关心的问题来提高数据科学水平的方法论ROI
The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About
摘要：
企业可能只能从数据科学的行业应用中获得三分之一的价值。在本文中，我们提出了一种使用数据科学来分类和回答“三大”问题的方法（正在发生的事情，是什么导致了它，以及我可以采取什么行动来优化我关心的事情）。在当今的现代环境中，数据科学的应用似乎是无止境的，每家公司都在新的数据和见解经济中争夺一席之地。然而，数据科学家似乎只专注于使用分类，回归和聚类方法来回答“发生了什么”的问题。关于为什么事情会发生或如何采取最佳行动来改善指标的问题被归入利基研究领域，并且在行业数据科学分析中通常被忽视。我们调查技术方法来回答这些其他重要问题，描述其中一些方法正在应用的领域，并提供如何将我们的方法和所选方法应用于实际业务用例的实际示例。
英文摘要：
Companies may be achieving only a third of the value they could be getting from data science in industry applications. In this paper, we propose a methodology for categorizing and answering 'The Big Three' questions (what is going on, what is causing it, and what actions can I take that will optimize what I care about) using data science. The applications of data science seem to be nearly endless in today's modern landscape, with each company jockeying for position in the new data and insights economy. Yet, data scientists seem to be solely focused on using classification, regression, and clustering methods to answer the question 'what is going on'. Answering questions about why things are happening or how to take optimal actions to improve metrics are relegated to niche fields of research and generally neglected in industry data science analysis. We survey technical methods to answer these other important questions, describe areas in which some of these methods are being applied, and provide a practical example of how to apply our methodology and selected methods to a real business use case.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
