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Data Value Assessment in Semiconductor Production: An Empirical Study to Define and Quantify the Value of Data

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Published:09 July 2022Publication History

ABSTRACT

Quality of data and the assessment of data value have become essential requirements to compete in the knowledge economy. In this work, information needs and data requirements of three different user groups from the semiconductor manufacturing area were determined empirically, based on which a new Data Value Assessment (DVA) process could be designed. Three data collection phases were carried out, in which a total of 24 employees of ams-OSRAM International GmbH took part. First, variables that are relevant for the evaluation of data were identified by focus groups. These results made it possible to develop a basic DVA process. In the second step, the process was tested by means of a survey. Based on 14 data objects from a selected case project, an initial DVA was carried out by the user groups of managers, operational employees and the Data Governance Office. The survey also covered questions about the information behavior of the user groups. With the results of the questionnaire, it was possible to improve the DVA process and to identify roles during the data evaluation. The DVA process has been assessed against practical applications based on feedback from key stakeholders and the findings provide a clear roadmap for future directions.

References

  1. Ahituv, N. (1980). A Systematic Approach toward Assessing the Value of an Information System. MIS Quarterly, 4(4), 61. https://doi.org/10.2307/248961Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Attard, J., Debattista, J., & Brennan, R. (2019). Saffron: A Data Value Assessment Tool for Quantifying the Value of Data Assets. Iswc Satellites., 1–4. http://doras.dcu.ie/23802/Google ScholarGoogle Scholar
  3. Bansal, Manu (2021). Flying Blind: How Bad Data Undermines Business. https://www.forbes.com/sites/forbestechcouncil/2021/10/14/flying-blind-how-bad-data-undermines-business/?sh=6ec3bbec29e8Google ScholarGoogle Scholar
  4. Baur, N., & Blasius, J. (2014). Methoden der empirischen Sozialforschung. In N. Baur & J. Blasius (Eds.), Handbuch Methoden der empirischen Sozialforschung (pp. 41–62). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-531-18939-0_1Google ScholarGoogle ScholarCross RefCross Ref
  5. Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data- quality/140546Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Batini, C., Castelli, M., Viscusi, G., Cappiello, C., & Francalanci, C. (2018). Digital Information Asset Evaluation: A Case Study in Manufacturing. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 49(3), 19–33. https://doi.org/10.1145/3242734.3242737Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Buber, R. (Ed.). (2007). Lehrbuch. Qualitative Marktforschung: Konzepte Methoden Analysen (1. Aufl.). Gabler. https://doi.org/10.1007/978-3-8349-9258-1Google ScholarGoogle ScholarCross RefCross Ref
  8. Cai, L. & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Jorunal, 14(2),1-10. Httpy://datascience.codata.org/articles/10.5334/dsj-2015-002Google ScholarGoogle Scholar
  9. Chong, K. E., & Ng, K. C. (2016, December). Relationship between overall equipment effectiveness, throughput and production part cost in semiconductor manufacturing industry. In 2016 IEEE international conference on industrial engineering and engineering management (IEEM) (pp. 75-79). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  10. Creswell, J. W., & Creswell, J. D. (Eds.). (2018). Research design: Qualitative quantitative & mixed methods approaches (Fifth edition). Sage.Google ScholarGoogle Scholar
  11. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (Third edition. International student edition). Sage.Google ScholarGoogle Scholar
  12. Döring, N., & Bortz, J. (Eds.). (2016). Springer-Lehrbuch. Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften (5. vollständig überarbeitete, aktualisierte und erweiterte Auflage). Springer.Google ScholarGoogle ScholarCross RefCross Ref
  13. Engels, B. (2018). IW-Trends 4/2018 Ein unbekannter Schatz - Wie bestimmen Unternehmen in Deutschland den Wert ihrer Daten? Vierteljahresschrift Zur Empirischen Wirtschaftsforschung, Jg. 45, 39–59.Google ScholarGoogle Scholar
  14. Glazer, R. (1993). Measuring the value of information: The information-intensive organization. IBM Systems Journal, 32(1), 99–110. https://doi.org/10.1147/sj.321.0099Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Higson, C. & Waltho, D. (2010). Valuing Information as an Asset.Google ScholarGoogle Scholar
  16. Hsu, C. Y., Chien, C. F., & Chen, P. N. (2012). Manufacturing intelligence for early warning of key equipment excursion for advanced equipment control in semiconductor manufacturing. Journal of the Chinese Institute of Industrial Engineers, 29(5), 303-313.Google ScholarGoogle ScholarCross RefCross Ref
  17. Krotova, A., Rusche, C., & Spiekermann, M. (2019). Die ökonomische Bewertung von Daten: Verfahren Beispiele und Anwendungen. IW-Analysen: Vol. 129. Institut der deutschen Wirtschaft Köln Medien GmbH.Google ScholarGoogle Scholar
  18. Kruschwitz, U. & Hull, C. (2017). Searching the Enterprise. Foundations and Trends in Information Retrieval. 11(1). 1-142. now publishers.Google ScholarGoogle ScholarCross RefCross Ref
  19. Laney, D. (2011). Infonomics: The Economics of Information and Principles of Information Asset Management., July 13-15.Google ScholarGoogle Scholar
  20. Laney, D. (2018). Infonomics: How to Monetize, Manage, and Measure Information As an Asset for Competitive Advantage. Routledge.Google ScholarGoogle Scholar
  21. Lichy, J., & Kachour, M. (2019, June). Big Data Perception & Usage: A Micro-Firm Perspective (The Case of the French Traditional Restaurant Sector). In Proceedings of the 2019 3rd International Conference on E-commerce, E-Business and E-Government (pp. 89-94).Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Mayring, P., & Fenzl, T. (2019). Qualitative Inhaltsanalyse. In N. Baur & J. Blasius (Eds.), Handbuch Methoden der empirischen Sozialforschung (pp. 633–648). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-21308-4_42Google ScholarGoogle ScholarCross RefCross Ref
  23. McKinsey (2019). Managing data as an asset: An interview with the CEO of Informatica. McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/managing-data-as-an-asset-an-interview-with-the-ceo-of-informaticaGoogle ScholarGoogle Scholar
  24. Misoch, S. (2019). Qualitative Interviews (2., erweiterte und aktualisierte Auflage). De Gruyter Oldenbourg.Google ScholarGoogle ScholarCross RefCross Ref
  25. Moody, D., & Walsh, P. (1999). Measuring the Value Of Information - An Asset Valuation Approach. ECIS, 1–17.Google ScholarGoogle Scholar
  26. Morville, P. & Callender, J. (2010). Search Patterns. O'Reilly.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Moyne, J., & Iskandar, J. (2017). Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing. Processes, 5(3), 39.Google ScholarGoogle ScholarCross RefCross Ref
  28. Picot, A., Berchtold, Y., & Neuburger, R. (2018). Big Data aus ökonomischer Sicht: Potenziale und Handlungsbedarf. In B. Kolany-Raiser, R. Heil, C. Orwat, & T. Hoeren (Eds.), Technikzukünfte, Wissenschaft und Gesellschaft. Big Data und Gesellschaft: Eine multidisziplinäre Annäherung (pp. 309–416). Springer VS. https://doi.org/10.1007/978-3-658-21665-8_5Google ScholarGoogle ScholarCross RefCross Ref
  29. Pietsch, T. (2003). Bewertung von Informations- und Kommunikationssystemen: Ein Vergleich betriebswirtschaftlicher Verfahren (2., neubearb. und erw. Aufl.). Erich Schmidt.Google ScholarGoogle Scholar
  30. Poore, R. S. (2000). Valuing Information Assets for Security Risk Management. Information Systems Security, 9(4), 1–7. https://doi.org/10.1201/1086/43311.9.4.20000910/31364.4Google ScholarGoogle ScholarCross RefCross Ref
  31. Preis, S. J. (2021). Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry (Doctoral dissertation, University of Gloucestershire).Google ScholarGoogle Scholar
  32. Preis, S. J. (2022). Predictive Analytics for Equipment Maintenance Operations: A Case Study From the Semiconductor Industry. In Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies (pp. 340-358). IGI Global.Google ScholarGoogle ScholarCross RefCross Ref
  33. Raban, D. R. & Mazor, M. (2013). The willingness to pay for information in digital marketplaces. In A. Kobyliński & A. Sobczak (Eds.), Perspectives in business informatics research, 12th International Conference, BIR 2013, Warsaw, Poland, September 23-25, 2013. Proceedings (pp. 267–277). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-40823-6_21Google ScholarGoogle Scholar
  34. Schmarzo, B. (2016). Determining the Economic Value of Data – InFocus Blog | Dell Technologies Services. https://infocus.delltechnologies.com/william_schmarzo/determining-economic-value-data/Google ScholarGoogle Scholar
  35. Tallon, P. P. (2013). Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost. Computer, 46(6), 32–38.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Treder M. (2020) Data Quality. In: The Chief Data Officer Management Handbook. Apress, Berkeley, CA.Google ScholarGoogle ScholarCross RefCross Ref
  37. Turner, S. (2004). Defining and Measuring Traffic Data Quality: White Paper on Recommended Approaches. Transportation Research Record. 1870. 62-69. 10.3141/1870-08.Google ScholarGoogle ScholarCross RefCross Ref
  38. UK Government Data Quality Hub (2021). Hidden costs of poor data quality. https://www.gov.uk/government/news/hidden-costs-of-poor-data-qualityGoogle ScholarGoogle Scholar
  39. Wang, T. Y., & Pan, H. C. (2011). Improving the OEE and UPH data quality by Automated Data Collection for the semiconductor assembly industry. Expert Systems with Applications, 38(5), 5764-5773.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Zechmann, A., & Möller, K. (2016). Finanzielle Bewertung von Daten als Vermögenswerte. Controlling, 28(10), 558–566. https://doi.org/10.15358/0935-0381-2016-10-558.Google ScholarGoogle ScholarCross RefCross Ref

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            • Published in

              cover image ACM Other conferences
              ICEEG '22: Proceedings of the 6th International Conference on E-Commerce, E-Business and E-Government
              April 2022
              439 pages
              ISBN:9781450396523
              DOI:10.1145/3537693

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              Publication History

              • Published: 9 July 2022

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