TY - JOUR
T1 - Data science as knowledge creation a framework for synergies between data analysts and domain professionals
AU - van der Voort, Haiko
AU - van Bulderen, Sabine
AU - Cunningham, Scott
AU - Janssen, Marijn
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Data science
KW - Knowledge
KW - Predictive model
KW - Professionalism
KW - Risk-based inspection
KW - Value creation
UR - http://www.scopus.com/inward/record.url?scp=85114129758&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2021.121160
DO - 10.1016/j.techfore.2021.121160
M3 - Article
AN - SCOPUS:85114129758
SN - 0040-1625
VL - 173
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 121160
ER -