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

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

van der Voort, H.G. (TU Delft Organisation & Governance)
van Bulderen, Sabine (IVO Rechtspraak)
Cunningham, Scott (University of Strathclyde)
Janssen, M.F.W.H.A. (TU Delft Information and Communication Technology)

2021

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.

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

http://resolver.tudelft.nl/uuid:fdb38193-f5f1-43d1-a9a0-1bb49ae58599

https://doi.org/10.1016/j.techfore.2021.121160

0040-1625

Technological Forecasting and Social Change, 173

Institutional Repository

journal article

© 2021 H.G. van der Voort, Sabine van Bulderen, Scott Cunningham, M.F.W.H.A. Janssen