论文标题
三巨头：一种通过回答公司关心的问题来增加数据科学ROI的方法
The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About
论文作者
论文摘要
公司可能只能实现他们从数据科学中获得的价值的三分之一。在本文中，我们提出了一种使用数据科学来对“三大”问题进行分类和回答“三大问题”（正在发生的事情，正在发生的事情以及可以优化我关心的事情的行动）的方法。数据科学的应用似乎在当今的现代景观中几乎是无穷无尽的，每个公司都在新数据和见解经济中的位置互动。然而，数据科学家似乎仅专注于使用分类，回归和聚类方法来回答“发生了什么”的问题。回答有关事情发生原因或如何采取最佳行动以改善指标的问题被降级为研究领域，并且在行业数据科学分析中普遍忽略了。我们调查了回答这些其他重要问题的技术方法，描述了应用其中一些方法的领域，并提供了一个实用的例子，说明了如何将我们的方法和所选方法应用于真实的业务用例。
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.
