Jwan Khisro


What makes this digital age so interesting is the prevalence of data and how it could be utilised, processed and made interoperable to create new businesses or to revolutionise old ones. Data quality, or lack thereof, is widely considered one of the most critical problems for achieving interoperability. To achieve high interoperability, such as the Swedish electricity market data hub, data hub development needs to better comprehend the relation between interoperability and data quality. Thus, this study inquired how the relation between interoperability and data quality can be understood. A qualitative study with a deductive approach was chosen for this purpose, as this enabled deeper understanding. The chosen theory formed the basis for the analytical framework. Seven deep interviews with actors in the Swedish electricity market provided empirical data. The results demonstrated that interoperability and data quality possess a make-or-break relationship. Consequently, the understanding is that high data quality is capable of decreasing complexity in a development process and increasing its reliability.

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Ballou and Harold L. Pazer (1995). Designing Information Systems to Optimize the Accuracy-Timeliness Tradeoff. (1995). Information Systems Research, 6(1), 51-72.

Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys (CSUR), 41(3), 1-52.

Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open, 2(C), 8-14.

Blake, R., & Mangiameli, P. (2011). The Effects and Interactions of Data Quality and Problem Complexity on Classification. Journal of Data and Information Quality (JDIQ),2(2), 1-28.

Camarinha-Matos, L.M., 2016. Collaborative smart grids–a survey on trends. Renewable and Sustainable Energy Reviews, 65, pp.283-294.

Chen, D. & Doumeingts, G. (2003). European initiatives to develop interoperability of enterprise applications—basic concepts, framework and roadmap. Annual reviews in control, 27(2), 153-162.

Coronel, C., & Morris, S. (2016). Database systems: design, implementation, & management. Cengage Learning.

Collins dictionary https://www.collinsdictionary.com/dictionary/english Accessed december 2018.

Creswell, J. W., 2014. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4th ed. Los Angeles: Sage Publications.

Daraio, C., Lenzerini, M., Leporelli, C., Naggar, P., Bonaccorsi, A., & Bartolucci, A. (2016). The advantages of an Ontology-Based Data Management approach: Openness, interoperability and data quality. Scientometrics, 108(1), 441-455.

Du, & Zhou. (2012). Improving financial data quality using ontologies. Decision Support Systems, 54(1), 76-86.

Duvier, Neagu, Oltean-Dumbrava, & Dickens. (2018). Data quality challenges in the UK social housing sector. International Journal of Information Management, 38(1), 196-200.

EC, Enterprise Interoperability Research Roadmap, Final Version (V. 4.0), 31 July, 2006.

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115.

Erlingsson, & Brysiewicz. (2017). A hands-on guide to doing content analysis. African Journal of Emergency Medicine, 7(3), 93-99.

Fenton, S., Giannangelo, K., Kallem, C. and Scichilone, R. (2013) "Data Standards, Data Quality, and Interoperability (2013 update)"Journal of AHIMA, 84 (11): 64-69

Ford, T. C., Colombi, J. M., Graham, S. R., & Jacques, D. R. (2007). Survey on Interoperability Measurement. AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH.

Gürdür, & Asplund. (2017). A systematic review to merge discourses: Interoperability, integration and cyber-physical systems. Journal of Industrial Information Integration,Journal of Industrial Information Integration.

Hazen, Weigel, Ezell, Boehmke, & Bradley. (2017). Toward understanding outcomes associated with data quality improvement. International Journal of Production Economics, 193, 737-747.

Herzog, F.J. Scheuren, & W.E. Winkler (2007). Data quality and record linkage techniques. New York: Springer Science+Business Media. 234 pp. US$44.95. ISBN: 978-0-387-69502-0.

IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. (1991). Piscataway, USA: IEEE.

Khisro Jwan & Sundberg Håkan (2018): Enterprise interoperability development in multi relation collaborations: Success factors from the Danish electricity market, Enterprise Information Systems, DOI: 10.1080/17517575.2018.1528633

Kohlbacher Florian. (2006). The Use of Qualitative Content Analysis in Case Study Research. Forum: Qualitative Social Research, 7(1), 1-30.

Lindblad-Gidlund, K. (2005). Techno Therapy: A relation with technology. Research reports in informatics, 2005.

Madnick, S., Wang, R., Lee, Y., & Zhu, H. (2009). Overview and Framework for Data and Information Quality Research. Journal of Data and Information Quality (JDIQ), 1(1), 1-22.

Pandit, H.J., O’Sullivan, D. and Lewis, D., 2018. GDPR data interoperability model. In the23rd EURAS Annual Standardisation Conference, Dublin, Ireland.

McAfee, A. (2005): Will Web Services Really Transform Collaboration? MIT Sloan Management Review, Vol. 46, No. 2, pp. 78-84.

Panetto, H., 2007. Towards a classification framework for interoperability of enterprise applications. International Journal of CIM, 20 (8), 727–740.

Panetto, H., & J. Cecil (2013) Information systems for enterprise integration, interoperability and networking: theory and applications, Enterprise Information Systems, 7:1, 1-6, DOI: 10.1080/17517575.2012.684802

Panetto, H., Zdravkovic, M., Jardim-Goncalves, R., Romero, D., Cecil, J. & Mezgár, I. (2016). New perspectives for the future interoperable enterprise systems. Computers in Industry, 79, 47-63.

Parssian, A. (2006). Managerial decision support with knowledge of accuracy and completeness of the relational aggregate functions. Decision Support Systems, 42(3), 1494-1502.

Ozmen-Ertekin, & Ozbay. (2012). Dynamic data maintenance for quality data, quality research. International Journal of Information Management, 32(3), 282-293.

Tolk, A., & Muguira, J. A. (2003, September). The levels of conceptual interoperability model. In Proceedings of the 2003 fall simulation interoperability workshop (Vol. 7, pp. 1-11). Citeseer.

Turner, S. (2004). Defining and measuring traffic data quality: White paper on recommended approaches. Transportation Research Record No. 1870.

Umar, A., Karabatis, G., Ness, L., Horowitz, B., & Elmagardmid, A. (1999). Enterprise Data Quality: A Pragmatic Approach. Information Systems Frontiers, 1(3), 279-301.

Redman, T. (1996) Data Quality for the Information Age. Artech House, Boston, MA

Redman, T. (2013). Data Quality Management Past, Present, and Future: Towards a Management System for Data. Handbook of Data Quality. S. Sadiq, Springer: 15-40.

Reimers, K. (2001): Standardizing the new e-business platform: Learning from the EDI experience, Electronic Markets, Vol. 11, No. 4, pp. 231-237.

Sciore, E., Siegel, M., & Rosenthal, A. (1994). Using semantic values to facilitate interoperability among heterogeneous information systems. ACM Transactions on Database Systems (TODS), 19(2), 254-290.

Seale Clive, Giampietro Gobo, Jaber F.Gubrium, & Silverman David. (2004). Qualitative Research Practice. London: SAGE Publications.

Shankaranarayanan, & Cai. (2006). Supporting data quality management in decision-making. Decision Support Systems, 42(1), 302-317.

Song, Sun, Wan, & Liang. (2016). Data quality management for service-oriented manufacturing cyber-physical systems. Computers and Electrical Engineering,Computers and Electrical Engineering.

Storey, Dewan, & Freimer. (2012). Data quality: Setting organizational policies. Decision Support Systems, 54(1), 434-442.

Sullivan, H. and Skelcher, C., 2017. Working across boundaries: collaboration in public services. Macmillan International Higher Education.

Swedish Energy Markets Inspectorate (2017). Ny modell för elmarknaden, R2017:05. https://www.ei.se/Documents/Publikationer/rapporter_och_pm/Rapporter% 202017/Ei_R2017_05.pdf Accessed May 07 2018.

Svenska kraftnät (2018). DEN SVENSKA ELMARKNADSHUBBEN - SAMLAD INFORMATION.https://www.svk.se/sok/?searchfield=2018+DEN+SVENSKA+ELMARKNADSHUBBEN+-+SAMLAD+INFORMATION Accessed september 15 2018.

Van Sinderen, Johnson, & Doumeingts. (2013). Computer in Industry Special Issue on "Interoperability and Future Internet for Next-Generation Enterprises" Editorial and state of the art. Computers in Industry, 64(8), 881-886.

Wand, Y., & Wang, R. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39(11), 86-95.

Wang, R., & Strong, D. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33.

Wang, Reddy, & Kon. (1995). Toward quality data: An attribute-based approach. Decision Support Systems, 13(3), 349-372.

Wang, R. Y., Ziad, M., & Lee, Y. W. (2006). Data quality (Vol. 23). Springer Science & Business Media. New York, Boston, Dordrecht, London, Moscow.

Wang, Wenguang, TOLK, Andreas, & Wang, Weiping. (2009). The Levels of Conceptual Interoperability Model: Applying Systems Engineering Principles to M&S.

Webster’s Dictionary http://www.merriam-webster.com/ Accessed december 10 2018.

Yin, R.K., 2017. Case study research and applications: Design and methods. Sage publications.

Zhao, Kexin, & Xia, Mu. (2014). Forming Interoperability Through Interorganizational Systems Standards. Journal of Management Information Systems, 30(4), 269-298.

Zhu, & Wu. (2014). Assessing the quality of large-scale data standards: A case of XBRL GAAP Taxonomy. Decision Support Systems, 59(1), 351-360.

Zhu, H., Lee, Y., & Rosenthal, A. (2016). Data Standards Challenges for Interoperable and Quality Data. Journal of Data and Information Quality (JDIQ), 7(1-2), 1-3.