Data science as knowledge creation a framework for synergies between data analysts and domain professionals

Haiko van der Voort*, Sabine van Bulderen, Scott Cunningham, Marijn Janssen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
52 Downloads (Pure)


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.

Original languageEnglish
Article number121160
JournalTechnological Forecasting and Social Change
Publication statusPublished - 2021


  • Data science
  • Knowledge
  • Predictive model
  • Professionalism
  • Risk-based inspection
  • Value creation


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