The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research., {"references":["Bärenfänger, R., Otto, B.: Proposing a Capability Perspective on Digital Business Models. Presented at the 2015 IEEE 17th Conference on Business Informatics (CBI), Lisbon, Portugal July (2015).","Hermann, M., Pentek, T., Otto, B.: Design Principles for Industrie 4.0 Scenarios. In: Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS-49 2016). , Kauai, HI, USA (2016).","Yoo, Y., Henfridsson, O., Lyytinen, K.: Research Commentary—The New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research. Inf. Syst. Res. 21, 724–735 (2010).","Bärenfänger, R., Otto, B., Österle, H.: Business value of in-memory technology – multiple-case study insights. Ind. Manag. Data Syst. 114, 1396–1414 (2014).","Leveling, J., Edelbrock, M., Otto, B.: Big Data Analytics for Supply Chain Management. In: Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2014). pp. 918–922. IEEE Computer Society, Selangor Darul Ehsan, Malaysia (2014).","Timm, C., Weichert, F., Fiedler, D., Prasse, C., Muller, H., ten Hompel, M., Marwedel, P.: Decentralized Control of a Material Flow System Enabled by an Embedded Computer Vision System. In: 2011 IEEE International Conference on Communications Workshops (ICC). pp. 1–5. IEEE Computer Society, Kyoto, Japan (2011).","Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 Working Group.","Mithas, S., Ramasubbu, N., Sambamurthy, V.: How Information Management Capability Influences Firm Performance. MIS Q. 35, 237–256 (2011).","Otto, B., Aier, S.: Business Models in the Data Economy: A Case Study from the Business Partner Data Domain. In: Alt, R. and Franczyk, B. (eds.) Proceedings of the 11th International Conference on Wirtschaftsinformatik (WI2013). pp. 475–489. , Leipzig, Germany (2013).\n[10]\tFranklin, M., Halevy, A., Maier, D.: From Databases to Dataspaces: A New Abstraction for Information Management. SIGMOD Rec. 34, 27–33 (2005).\n[11]\tOtto, B., Jürjens, J., Schon, J., Auer, S., Menz, N., Wenzel, S., Cirullies, J.: Industrial Data Space. Digital sovereignity over data. Fraunhofer-Gesellschaft, München (2016).\n[12]\tOtto, B., Österle, H.: The Corporate Data League: One Approach for Cooperative Data Maintenance of Business Partner Data. In: Corporate Data Quality: Prerequisite for Successful Business Models. pp. 168–174. Springer Gabler, Berlin (2015).\n[13]\tHenderson, D., Mosley, M., Brackett, M.H., Earley, S. eds: DAMA guide to the data management body of knowledge (DAMA-DMBOK guide). Technics Publications, Bradley Beach, N.J, USA (2009).\n[14]\tOtto, B.: Enterprise-Wide Data Quality Management in Multinational Corporations, (2012).\n[15]\tEFQM: EFQM Framework for Corporate Data Quality Management: Assessing the Organization's Data Quality Management Capabilities. , Brussels (2011).\n[16]\tWhite, A.: 2013 Strategic Road Map for Enterprise Information Management. Gartner, Inc. (2013).\n[17]\tThomas, G.: The DGI Data Governance Framework. The Data Governance Institute, Orlando, FL, USA (2014).\n[18]\tKhatri, V., Brown, C.V.: Designing data governance. Commun. ACM. 53, 148–152 (2010).\n[19]\tIBM: IBM Data Governance Council Maturity Model. Building a roadmap for effective data governance. IBM Corporation (2007).\n[20]\tGold, A.H., Malhotra, A., Segars, A.H.: Knowledge Management: An Organizational Capabilities Perspective. J. Manag. Inf. Syst. 18, 185–214 (2001).\n[21]\tCohen, J.F., Olsen, K.: Knowledge management capabilities and firm performance: A test of universalistic, contingency and complementarity perspectives. Expert Syst. Appl. 42, 1178–1188 (2015).\n[22]\tNonaka, I., Takeuchi, H.: The knowledge-creating company: how Japanese companies create the dynamics of innovation. Oxford Univ. Press, New York, NY, USA (1995).\n[23]\tAlavi, M., Leidner, D.E.: Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research, (2001).\n[24]\tDenford, J.S.: Building knowledge: developing a knowledge‐based dynamic capabilities typology. J. Knowl. Manag. 17, 175–194 (2013).\n[25]\tWallace, D.P., Fleet, C.V., Downs, L.J.: The research core of the knowledge management literature. Int. J. Inf. Manag. 31, 14–20 (2011).\n[26]\tNielsen, B.B., Michailova, S.: Knowledge Management Systems in Multinational Corporations: Typology and Transitional Dynamics. Long Range Plann. 40, 314–340 (2007).\n[27]\tPeffers, 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).\n[28]\tMarch, S.T., Smith, G.F.: Design and natural science research on information technology. Decis. Support Syst. 15, 251–266 (1995).\n[29]\tÖsterle, H., Otto, B.: Consortium Research. A Method for Researcher-Practitioner Collaboration in Design-Oriented IS Research. Bus. Inf. Syst. Eng. 52, 283–293 (2010).\n[30]\tYin, R.K.: Case Study Research: Design and Methods. Sage Publications Ltd., Los Angeles (2013).\n[31]\tSusman, G.I., Evered, R.D.: An Assessment of the Scientific Merits of Action Research. Adm. Sci. Q. 23, 582–603 (1978).\n[32]\tvom Brocke, J.: Design Principles for Reference Modeling: Reusing Information Models by Means of Aggregation, Specialisation, Instantiation, and Analogy. In: Fettke, P. and Loos, P. (eds.) Reference Modeling for Business Systems Analysis. pp. 47–76. IGI Global, Hershey, PA, USA (2007).\n[33]\tMyers, M.D., Newman, M.: The qualitative interview in IS research: Examining the craft. Inf. Organ. 17, 2–26 (2007).\n[34]\tVom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process. In: Proceedings of the 17th European Conference on Information Systems (ECIS 2009). pp. 2206–2217. , Verona (2009).\n[35]\tHoltkamp, B., Steinbuß, S., Gsell, H., Löffeler, T., Springer, U.: Towards a Logistics Cloud. Presented at the 2010 Sixth International Conference on Semantics, Knowledge and Grids November (2010).\n[36]\tBöhmer, M., Schmidt, M., Weissenberg, N.: Seamless Interoperability in Logistics: Narrowing the Business-IT Gap by Logistics Business Objects. In: ten Hompel, M., Rehof, J., and Wolf, O. (eds.) Cloud Computing for Logistics. pp. 77–117. Springer, Berlin, Heidelberg (2015).\n[37]\tCooper, H.M.: Organizing knowledge syntheses: A taxonomy of literature reviews. Knowl. Soc. 1, 104–126 (1988).\n[38]\tRowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33, 163–180 (2007).\n[39]\tZins, C.: Conceptual approaches for defining data, information, and knowledge. J. Am. Soc. Inf. Sci. Technol. 58, 479–493 (2007).\n[40]\tLasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6, 239–242 (2014).\n[41]\tDIN SPEC 91345: Reference Architecture Model Industrie 4.0 (RAMI4.0), (2016).\n[42]\tVDI/VDE-GMA, ZVEI eds: Status Report. Reference Architecture Model Industrie 4.0 (RAMI4.0). , Düsseldorf (2015).\n[43]\tBMWi (German Federal Ministry for Economic Affairs and Energy) ed: Structure of the Administration Shell. (2016).\n[44]\tOtto, B., Abraham, R., Schlosser, S.: Toward a Taxonomy of the Data Resource in the Networked Industry. Presented at the Logistics in the Networked Industry : 7th International Scientific Symposium on Logistics, Cologne June 4 (2014).\n[45]\tHoberman, S.: Data modeling made simple: a practical guide for business and IT professionals. Technics Publications, Bradley Beach, N.J, USA (2009).\n[46]\tÖsterle, H., Höning, F., Osl, P.: Methodenkern des Business Engineering. Ein Lehrbuch. (2011).\n[47]\tThe Open Group: TOGAF Version 9.1. (2011).\n[48]\tThe Essential Project: Essential Meta-Model Reference, http://www.enterprise-architecture.org/documentation/doc-meta-model. Accessed on 01 Mar 2016\n[49]\tMikloš, J.: A meta-model for the spatial capability architecture. J. Theor. Appl. Inf. Technol. 43, 301–305 (2012).\n[50]\tScheer, A.-W.: Architecture of integrated information systems : foundations of enterprise modelling. Springer, Berlin, Heidelberg (1992).\n[51]\tSchmidt, A.: Entwicklung einer Methode zur Stammdatenintegration, (2010).\n[52]\tPäivärinta, T., Tyrväinen, P., Ylimäki, T.: Defining Organizational Document Metadata: A Case Beyond Standards. In: ECIS 2002 Proceedings. , Gdansk, Poland (2002)."]}