Data science as knowledge creation a framework for synergies between data analysts and domain professionals
Bibliographic Data
| ID | 21400876 |
|---|---|
| Authors | Haiko Van Der Voort (0000-0002-2795-9444, Delft University of Technology, corresponding author), Sabine van Bulderen, Scott W Cunningham (0000-0001-7140-916X, University of Strathclyde), Marijn Janssen (0000-0001-6211-8790, Delft University of Technology) |
| Year | 2021 |
| Volume | 173 |
| Pages | 121160 |
| Publication date | 2021-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Technological Forecasting and Social Change (JOURNAL) |
| Journal identifiers | ISSN: 0040-1625 • E-ISSN: 1873-5509 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.techfore.2021.121160 |
| OpenAlex | W3198664845 |
| Language | EN |
| Citations received | 2 |
| References cited | 37 |
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
Analytics · Complement (music) · Data Analysis · Data mining · Data science · Domain (mathematical analysis) · Domain knowledge · Knowledge management · Process (computing) · Big Data and Business Intelligence · Computer Science · Data Quality and Management · Research Data Management Practices
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Organizational Learning
The Sage Handbook of Organization Studies
Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making
Techno-optimism and policy-pessimism in the public sector big data debate
Research and policy in lifelong learning
Developing the Information and Knowledge Agenda in Information Systems
Doing What Works
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Theory of professionalism
Critical Questions for Big Data
What is responsible and sustainable data science
Beyond mystery
Democratic governance in an age of datafication
Data in the smart city
The Image of Objectivity
| Unique citing works | 2 |
|---|---|
| Citations per year | 0.67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |