Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 15th International Conference on Business Information Systems 2020 "Developments, Opportunities and Challenges of Digitization", WIRTSCHAFTSINFORMATIK 2020 |
| Herausgeber (Verlag) | Gito Verlag |
| Seiten | 309-325 |
| ISBN (elektronisch) | 9783955453350 |
| Publikationsstatus | Veröffentlicht - 9 März 2020 |
| Veranstaltung | 15th International Conference on Wirtschaftsinformatik - Potsdam, Deutschland Dauer: 8 März 2020 → 11 März 2020 Konferenznummer: 15 |
Abstract
There is a large gap between domain experts capable to identify business needs and data scientists who use insight producing algorithms, but often fail to connect these to the bigger picture. A major challenge for companies and organizations is to integrate practical data science into existing teams and workflows. We are driven by the assumption that efficient data science requires cross-disciplinary teams able to communicate. We present a methodology that enables domain experts and data scientists to analyze and discuss findings and implications together. Motivated by a typical problem from auditing we introduce a visualization method that helps to detect unusual data in a subset and highlights potential areas for investigation. The method is a first aid kit applicable regardless whether unusual samples were detected by manual selection of domain experts or by algorithms applied by data scientists. An applicability check shows how the visualizations facilitate collaboration of both parties.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Technologie- und Innovationsmanagement
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Management-Informationssysteme
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- BibTex
- RIS
Proceedings of the 15th International Conference on Business Information Systems 2020 "Developments, Opportunities and Challenges of Digitization", WIRTSCHAFTSINFORMATIK 2020. Gito Verlag, 2020. S. 309-325.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Hybrid intelligence with commonality plots
T2 - 15th International Conference on Wirtschaftsinformatik
AU - Stege, Nikolas
AU - Breitner, Michael H.
N1 - Conference code: 15
PY - 2020/3/9
Y1 - 2020/3/9
N2 - There is a large gap between domain experts capable to identify business needs and data scientists who use insight producing algorithms, but often fail to connect these to the bigger picture. A major challenge for companies and organizations is to integrate practical data science into existing teams and workflows. We are driven by the assumption that efficient data science requires cross-disciplinary teams able to communicate. We present a methodology that enables domain experts and data scientists to analyze and discuss findings and implications together. Motivated by a typical problem from auditing we introduce a visualization method that helps to detect unusual data in a subset and highlights potential areas for investigation. The method is a first aid kit applicable regardless whether unusual samples were detected by manual selection of domain experts or by algorithms applied by data scientists. An applicability check shows how the visualizations facilitate collaboration of both parties.
AB - There is a large gap between domain experts capable to identify business needs and data scientists who use insight producing algorithms, but often fail to connect these to the bigger picture. A major challenge for companies and organizations is to integrate practical data science into existing teams and workflows. We are driven by the assumption that efficient data science requires cross-disciplinary teams able to communicate. We present a methodology that enables domain experts and data scientists to analyze and discuss findings and implications together. Motivated by a typical problem from auditing we introduce a visualization method that helps to detect unusual data in a subset and highlights potential areas for investigation. The method is a first aid kit applicable regardless whether unusual samples were detected by manual selection of domain experts or by algorithms applied by data scientists. An applicability check shows how the visualizations facilitate collaboration of both parties.
KW - Commonality plots
KW - Data science
KW - Domain knowledge
KW - Hybrid intelligence
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85101706057&partnerID=8YFLogxK
U2 - 10.30844/WI_2020_C7-STEGE
DO - 10.30844/WI_2020_C7-STEGE
M3 - Conference contribution
SP - 309
EP - 325
BT - Proceedings of the 15th International Conference on Business Information Systems 2020 "Developments, Opportunities and Challenges of Digitization", WIRTSCHAFTSINFORMATIK 2020
PB - Gito Verlag
Y2 - 8 March 2020 through 11 March 2020
ER -