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.
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Buber, R. (Ed.). (2007). Lehrbuch. Qualitative Marktforschung: Konzepte Methoden Analysen (1. Aufl.). Gabler. https://doi.org/10.1007/978-3-8349-9258-1Google ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- Creswell, J. W., & Creswell, J. D. (Eds.). (2018). Research design: Qualitative quantitative & mixed methods approaches (Fifth edition). Sage.Google Scholar
- Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (Third edition. International student edition). Sage.Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- Higson, C. & Waltho, D. (2010). Valuing Information as an Asset.Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- Kruschwitz, U. & Hull, C. (2017). Searching the Enterprise. Foundations and Trends in Information Retrieval. 11(1). 1-142. now publishers.Google ScholarCross Ref
- Laney, D. (2011). Infonomics: The Economics of Information and Principles of Information Asset Management., July 13-15.Google Scholar
- Laney, D. (2018). Infonomics: How to Monetize, Manage, and Measure Information As an Asset for Competitive Advantage. Routledge.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- Misoch, S. (2019). Qualitative Interviews (2., erweiterte und aktualisierte Auflage). De Gruyter Oldenbourg.Google ScholarCross Ref
- Moody, D., & Walsh, P. (1999). Measuring the Value Of Information - An Asset Valuation Approach. ECIS, 1–17.Google Scholar
- Morville, P. & Callender, J. (2010). Search Patterns. O'Reilly.Google ScholarDigital Library
- Moyne, J., & Iskandar, J. (2017). Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing. Processes, 5(3), 39.Google ScholarCross Ref
- 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 ScholarCross Ref
- Pietsch, T. (2003). Bewertung von Informations- und Kommunikationssystemen: Ein Vergleich betriebswirtschaftlicher Verfahren (2., neubearb. und erw. Aufl.). Erich Schmidt.Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- Tallon, P. P. (2013). Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost. Computer, 46(6), 32–38.Google ScholarDigital Library
- Treder M. (2020) Data Quality. In: The Chief Data Officer Management Handbook. Apress, Berkeley, CA.Google ScholarCross Ref
- 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 ScholarCross Ref
- UK Government Data Quality Hub (2021). Hidden costs of poor data quality. https://www.gov.uk/government/news/hidden-costs-of-poor-data-qualityGoogle Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
Index Terms
- Data Value Assessment in Semiconductor Production: An Empirical Study to Define and Quantify the Value of Data
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