Perspectives of Big Data Quality in Smart Service Ecosystems (Quality of Design and Quality of Conformance)

Document Type : Research Paper


1 Ph.D., Head of Business Informatics Group, Department of Computing, Dublin City University, Dublin, Ireland.

2 Associate Professor, Department of Computer Systems and Communications, Faculty of Informatics, Masaryk University, Brno, Czech Republic.


Despite the increasing importance of data and information quality, current research related to Big Data quality is still limited. It is particularly unknown how to apply previous data quality models to Big Data. In this paper we review Big Data quality research from several perspectives and apply a known quality model with its elements of conformance to specification and design in the context of Big Data. Furthermore, we extend this model and demonstrate it utility by analyzing the impact of three Big Data characteristics such as volume, velocity and variety in the context of smart cities. This paper intends to build a foundation for further empirical research to understand Big Data quality and its implications in the design and execution of smart service ecosystems.


Anttiroiko, A., Valkama, P., & Bailey, S. (2014). Smart Cities in the New Service Economy: Building Platforms for Smart Services, AI & Society, 29(3), 323–334.
Borek, A., Helfert, M., Ge, M., & Parlikad, A.K.N. (2011). An information oriented framework for relating IS/IT resources and business value. In Proceedings of the International Conference on Enterprise Information Systems (ICEIS). Beijing, China.
Chaffey, D., & Wood, S. (2005). Business Information Management: Improving Performance Using Information Systems. Pearson Education Ltd., Upper Saddle River.
Chen, H., Chiang, R.H.L., & Storey, V.C. (2012). Business intelligence and analytics: from Big Data to big impact. MIS Quarterly, 36(4), 1165–1188.
Das, T.K., & Kumar, P.M. (2013). Big data analytics: A framework for unstructured data analysis. International Journal of Engineering Science & Technology, 5(1), 153-156.
Gilmore, H.L. (1974). Product conformance cost. Quality Progress, 7, 16-19.
Gronroos, C. (1983). Strategic management and marketing in the service sector. Marketing Science Institute Massachusetts, USA. Available at:
Giachetti, R. (2010). Design of Enterprise Systems: Theory, Architecture, and Methods. 1st edition. Boca Raton, CRC Press.
Ge, M., & Helfert, M. (2008). Data and Information Quality Assessment in Information Manufacturing Systems. International Conference on Business Information Systems, pp. 380-389.
Ge, M., O’Brien, T., & Helfert, M. (2017). Predicting Data Quality Success - The Bullwhip Effect in Data Quality. Perspectives in Business Informatics Research - 16th International Conference, BIR 2017, Copenhagen, Denmark, August 28-30, 2017.
Grover, V., Chiang, R.H., Liang, T.P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423.
Helfert, M., & Heinrich, B. (2003). Analyzing Data Quality Investments in CRM – A model-based approach. In Eppler, M.J., Helfert, M (eds): Eighth International Conference on Information Quality (IQ 2003), 7th to 9th November, MIT Sloan School of Management, Cambridge, pp. 80-95.
Hoxmeier, J.A. (1998). Typology of database quality factors. Software Quality Journal, 7(3-4), 179-93.
Haug, A., & Arlbjørn, J.S. (2010). Barriers to master data quality. Journal of Enterprise Information Management, 24(3), 288-303.
IEEE Standard (2007). Systems and software engineering. Recommended practice for architectural description of software-intensive systems, IEEE.
Inmon, B. (2006). DW 2.0: Architecture for the Next Generation of Data Warehousing. Information Management, 16(4), p.8.
ITU (2014). Smart sustainable cities: An analysis of definitions, Focus Group Technical Report.
Kim, W., Choi, B.J., Hong, E.K., Kim, SK., & Lee, D. (2003). A taxonomy of dirty data. Data Mining and Knowledge Discovery, 7(1), 81-99.
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage, experience and acquisition intention of Big Data analytics. International Journal of Information Management, 34, 387–394.
Labrinidis, A., & Jagadish, H.V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032-2033.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety, Technical report. META Group.
Miller, H.G., & Mork, P. (2013). From Data to Decisions: A Value Chain for Big Data. IT Professional, 15(1), 57-59.
Pourzolfaghar, Z., Bastidas, V., & Helfert, M. (2019). Standardisation of Enterprise Architecture Development for Smart Cities. Journal of the Knowledge Economy.
Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied artificial intelligence, 17(5-6), 375-381.
Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-34.
Wang, R.Y. (1998). A product perspective on total data quality management. Communications of the ACM, 41(2), 58-65.
Yang, Q., & Helfert, M. (2016). Revisiting arguments for a three layered data warehousing architecture in the context of the Hadoop platform. In: The 6th International Conference on Cloud Computing and Services Science (CLOSER 2016), 23-25 Apr 2016, Roma, Italy. ISBN 978-989-758-182-3.