The road from data generation to data use is commonly approached as a data-driven, functional process in which domain expertise is integrated as an afterthought. In this contribution we complement this functional view with an institutional view, that takes data analysis and domain professionalism as complementary (yet fallible) knowledge sources. We developed a framework that identifies and amplifies synergies between data analysts and domain professionals instead of taking one of them (i.e. data analytics) at the centre of the analytical process. The framework combines the often-cited CRISP-DM framework with a knowledge creation framework. The resulting framework is used in a data science project at a Dutch inspectorate that seeks to use data for risk-based inspection. The findings show first support of our framework. They also show that whereas more complex models have a higher predictive power, simpler models are sometimes preferred as they have the potential to create more synergies between inspectors and data analyst. Another issue driven by the integrated framework is about who of the involved actors should own the predictive model: data analysts or inspectors.

从数据生成到数据使用的道路通常被视为一个数据驱动的功能性过程，其中领域专业知识被整合为事后的想法。在这篇文章中，我们用制度观点补充了这一功能观点，将数据分析和领域专业作为补充（但易出错）的知识来源。我们开发了一个框架来识别和放大数据分析师和领域专业人士之间的协同作用，而不是将其中一个（即数据分析）作为分析过程的中心。该框架将经常被引用的 CRISP-DM 框架与知识创建框架相结合。由此产生的框架用于荷兰检查机构的数据科学项目，该项目旨在将数据用于基于风险的检查。调查结果首先显示了我们的框架的支持。他们还表明，虽然更复杂的模型具有更高的预测能力，但有时更喜欢更简单的模型，因为它们有可能在检查员和数据分析师之间创造更多的协同作用。集成框架驱动的另一个问题是谁应该拥有预测模型：数据分析师或检查员。