Hybrid Intelligence with Commonality Plots: A First Aid Kit for Domain Experts and a Translation Device for Data Scientists

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Hybrid Intelligence with Commonality Plots: A First Aid Kit for Domain Experts and a Translation Device for Data Scientists", 
							Author= "Nikolas Stege, Michael H. Breitner", 
							Doi= "https://doi.org/10.30844/wi_2020_c7-stege", 
							 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.

", 
							 Keywords= "Commonality Plots, Domain Knowledge, Hybrid Intelligence, Visualization, Data Science
", 
							}
					
Nikolas Stege, Michael H. Breitner: Hybrid Intelligence with Commonality Plots: A First Aid Kit for Domain Experts and a Translation Device for Data Scientists. Online: https://doi.org/10.30844/wi_2020_c7-stege (Abgerufen 16.04.24)

Abstract

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.

Keywords

Schlüsselwörter

Commonality Plots, Domain Knowledge, Hybrid Intelligence, Visualization, Data Science

References

Referenzen

1. Agarwal, R., Dhar, V.: Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research (ISR). 25(3), 443–448 (2014)
2. Chen, H., Chiang, R., Storey, V.: Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly (MISQ), 36(4), 1165–1188 (2012)
3. Reif, R.: The Data Scientist Shortage is Huge. Here’s How to Beat It. https://insidebigdata.com/2018/12/27/data-scientist-shortage-huge-heres-beat (Accessed: July 25, 2019)
4. Knechel, W.R.: Audit Quality and Regulation. International Journal of Auditing. 20(3), 215–223 (2016)
5. Bedard, J.C., Biggs, S.F.: Pattern Recognition, Hypotheses Generation, and Auditor Performance in an Analytical Task. Accounting Review. 66(3), 622–642 (1991)
6. Raphael, J.: Rethinking the Audit: Innovation is Transforming How Audits are Conducted – and Even What it Means to Be an Auditor. Journal of Accountancy. 223(4), 28 (2017)
7. Kokina, J., Davenport, T.H.: The Emergence of Artificial Intelligence: How Automation is Changing Auditing. Journal of Emerging Technologies in Accounting. 14(1), 115–122 (2017)
8. Eilers, D., Köpp, C., Gleue, C., Breitner, M.H.: It’s Not a Bug, It’s a Feature: How Visual Model Evaluation Can Help to Incorporate Human Domain Knowledge in Data Science. Proceedings of the International Conference on Information Systems (ICIS) (2017)
9. Gandomi, A., Haider, M.: Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management. 35, 137–144 (2015)
10. Labrinidis, A., Jagadish, H.V.: Challenges and Opportunities with Big Data. Proceedings of the VLDB Endowment. 5(12), 2032–2033 (2012)
11. Bresciani, S., Eppler, M.J.: The Benefits of Synchronous Collaborative Information Visualization: Evidence from an Experimental Evaluation. IEEE Transactions on Visualization and Computer Graphics. 15(6), 1073–1080 (2009)
12. Alpar, P., Schulz, M.: Self-Service Business Intelligence. Business & Information Systems Engineering. 58(2), 151–155 (2016)
13. Cairo, A.: The Truthful Art: Data. Charts, and Maps for Communication. New Riders (2015)
14. Boslaugh, S.: Statistics in a Nutshell: A Desktop Quick Reference. O’Reilly Media (2012)
15. Hoadley, B.: The Compound Multinomial Distribution and Bayesian Analysis of Categorical Data from Finite Populations. Journal of the American Statistical Association. 64(325), 216–229 (1969)
16. Janardan, K.: Chance Mechanisms for Multivariate Hypergeometric Models. Sankhyā: The Indian Journal of Statistics. 35(4), 465–478 (1973)
17. Wasserman, L.: All of Statistics: A Concise Course in Statistical Inference. Springer (2013)
18. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Routledge (1998)
19. Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall/CRC (1994)
20. Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data. John Wiley & Sons (2019)
21. García-Laencina, P.J., Sancho-Gómez, J.L., Figueiras-Vidal, A.R.: Pattern Classification with Missing Data: A Review. Neural Computing and Applications. 19(2), 263–282 (2010)
22. Kuhn, M., Johnson, K.: Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press (2019)
23. Van Buuren, S.: Flexible Imputation of Missing Data. Chapman and Hall/CRC (2018)
24. Hilfiger, J.J.: Graphing Data with R: An Introduction. O’Reilly Media (2015)

Most viewed articles

Meist angesehene Beiträge

GITO events | library.gito