Towards a Guideline Affording Overarching Knowledge Building in Data Analysis Projects




Data Mining, Knowledge Bases, Reference Model, Methodological Knowledge, Domain-Specific Information Systems Development


Tight and competitive market situations pose a serious challenge to enterprises in the manufacturing industry domain. Competing in the use of data analytics to enhance products and processes requires additional resources to deal with the complexity. On the contrary, the possibilities afforded by digitization and data analysis-based approaches make for a valuable asset. In this paper we suggest a guideline to a systematic course of action for the data-based creation of holistic insight. Building an overlaying corpus of knowledge accelerates the learning curve within specific projects as well as across projects by exceeding the project-specific view towards an integrated approach.


Download data is not yet available.


Schäffer, T., Leyh, C.: Master Data Quality in the Era of Digitization - Toward Inter-organizational Master Data Quality in Value Networks: A Problem Identification. In: Piazolo, F., Geist, V., Brehm, L., and Schmidt, R. (eds.) Innovations in Enterprise Information Systems Management and Engineering. pp. 99–113. Springer International Publishing, Cham (2017).

Zschech, P., Heinrich, K., Pfitzner, M., Hilbert, A.: Are you up for the challenge? Towards the development of a big data capability assessment model. Proc. 25th Eur. Conf. Inf. Syst. ECIS. 2613–2624 (2017).

Huber, S., Wiemer, H., Schneider, D., Ihlenfeldt, S.: DMME: Data Mining Methodology for Engineering Applications – A Holistic Extension to the CRISP-DM Model. In: Procedia CIRP (2018). pp. 403–408., Gulf of Naples, Italy (2018).

D. Gliem, C. Laroque., U. Jessen, J. Stolipin, S. Wenzel, W. Kusturica: SimCast – Simulationsgestützte Prognose der Dauer von Logistikprozessen. Abschlussbericht. Universität Kassel, Fachgebiet Produktionsorganisation und Fabrikplanung; Westsächsische Hochschule Zwickau, Fachgebiet Wirtschaftsinformatik (2019).

Weick, K.E.: The social psychology of organizing. McGraw-Hill, New York (2006).

Seidel, S., Chandra Kruse, L., Székely, N., Gau, M., Stieger, D.: Design principles for sensemaking support systems in environmental sustainability transformations. Eur. J. Inf. Syst. 27, 221–247 (2018).

Gregor, S., Jones, D.: The anatomy of a design theory. J. Assoc. Inf. Syst. 8, (2007).

Zschech, P., Fleißner, V., Baumgärtel, N., Hilbert, A.: Data Science Skills and Enabling Enterprise Systems: Eine Erhebung von Kompetenzanforderungen und Weiterbildungsangeboten. HMD Prax. Wirtsch. 55, 163–181 (2018).

Debortoli, S., Müller, O., Brocke, J. vom: Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-Spezialisten: Eine Text-Mining-Studie auf Basis von Stellenausschreibungen. Wirtschaftsinformatik. 56, 315–328 (2014).

Hevner, A.R., March, S.T., Ram, S., Park, J.: Design Science in Information Systems Research. MIS Q. 28, 75–105 (2004).

Hevner, A.R.: A three cycle view of design science research. Scand. J. Inf. Syst. 19, 87–92 (2007).

Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. J. Manag. Inf. Syst. 24, 45–77 (2007).

Stachowiak, H.: Allgemeine Modelltheorie. Springer, Wien [u.a.] (1973).

Vom Brocke, J., Grob, H.L.: Referenzmodellierung: Gestaltung und Verteilung von Konstruktionsprozessen. Logos Verlag, Berlin (2015).

Schlieter, H., Esswein, W.: Reference Modelling in Health Care - State of the Art and Proposal for the Construction of a Reference Model. Enterp. Model. Inf. Syst. Archit. 6, 36–49 (2011).

Kurgan, L.A., Musilek, P.: A survey of Knowledge Discovery and Data Mining process models. Knowl. Eng. Rev. 21, 1 (2006).

Brachman, R.J., Anand, T.: The Process of Knowledge Discovery in A First Sketch. AAAI Tech. Rep. WS-94-03 (1994).

Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–37 (1996).

Xu, L.D., He, W., Li, S.: Internet of Things in Industries: A Survey. IEEE Trans. Ind. Inform. 10, 2233–2243 (2014).

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C.R., Wirth, R.: CRISP-DM 1.0: Step-by-step data mining guide, (2000).

IBM Corporation 2016: Analytics Solutions Unified Method. Implementations with Agile principles,, (2016).

Weick, K.E., Sutcliffe, K.M., Obstfeld, D.: Organizing and the Process of Sensemaking. Organ. Sci. 16, 409–421 (2005).

Gregor, S., Kruse, L.C., Seidel, S.: The Anatomy of a Design Principle. J. Assoc. Inf. Syst. (2020).

Chandra, L., Seidel, S., Gregor, S.: Prescriptive Knowledge in IS Research: Conceptualizing Design Principles in Terms of Materiality, Action, and Boundary Conditions. In: 2015 48th Hawaii International Conference on System Sciences. pp. 4039–4048. IEEE, HI, USA (2015).

Michalczyk, S., Scheu, S.: Designing an analytical Information Systems Engineering method. In: ECIS (2020).

Jennex, M.E.: Re-Visiting the Knowledge Pyramid. In: 2009 42nd Hawaii International Conference on System Sciences. pp. 1–7. IEEE, Waikoloa, Hawaii, USA (2009).

Sanfilippo, E.M., Belkadi, F., Bernard, A.: Ontology-based knowledge representation for additive manufacturing. Comput. Ind. 109, 182–194 (2019).

Gulcehre, C., Bengio, Y.: Knowledge Matters: Importance of Prior Information for Optimization. J Mach Learn Res. 17, (2013).

Mariscal, G., Marbán, Ó., Fernández, C.: A survey of data mining and knowledge discovery process models and methodologies. Knowl. Eng. Rev. 25, 137–166 (2010).

Ferstl, O.K., Sinz, E.J.: Grundlagen der Wirtschaftsinformatik. Oldenbourg, München (2013).

Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital Twin in manufacturing: A categorical literature review and classification. IFAC-Pap. 51, 1016–1022 (2018).

Hall, D., Paradice, D., Courtney, J.F.: Building a theoretical foundation for a learning-oriented knowledge management system. J. Inf. Technol. Theory Appl. JITTA. 5, 7 (2003).

QualiPro. Accessed 10.05.2021.

DIN SPEC 91345:2016-04. Deutsches Institut für Normung, DIN SPEC 91345:2016-04. Beuth-Verlag, 2016,; Accessed 10.05.2021.

Simon, H.A.: The sciences of the artificial. MIT Press, Cambridge, Mass. (2008).

Klaus, G., Buhr, M.: Philosophisches Wörterbuch. VEB Enzyklopädie, Leipzig (1971).

Angée, S., Lozano-Argel, S.I., Montoya-Munera, E.N., Ospina-Arango, J.-D., Tabares-Betancur, M.S.: Towards an Improved ASUM-DM Process Methodology for Cross-Disciplinary Multi-organization Big Data & Analytics Projects. In: Uden, L., Hadzima, B., and Ting, I.-H. (eds.) Knowledge Management in Organizations. pp. 613–624. Springer International Publishing, Cham (2018), doi:



How to Cite

Schneider, D., & Kusturica, W. (2021). Towards a Guideline Affording Overarching Knowledge Building in Data Analysis Projects. Business Information Systems, 1, 49–59.

Conference Proceedings Volume


Big Data