DOI :10.26650/B/ET06.2020.011.05   IUP :10.26650/B/ET06.2020.011.05    Full Text (PDF)

Big Data Governance

Malgorzata Pankowska

Information processing in a traditional way focuses on relatively stable structured data, repeatable processes as well as on operations in Business Intelligence systems. However, nowadays more and more popular, big data, defined as huge volumes of data available in varying degrees of complexity, generated at different velocities, and varying degrees of ambiguity, cannot be processed using traditional methods and technologies. Some people argue that suitable IT (Information Technology) infrastructure for big data processing is not yet widely developed nor implemented to discuss the big data architecture implementation benefits, risks, and opportunities. Nevertheless, this paper is to present the big data governance issues. Particularly, within the proposed theme, the author discusses the big data system architecture and development strategy. The last part of the paper includes a proposal of a big data architecture model as well as a design of balanced scorecard objectives and measures specification to support the big data governance at public services business organizations. As usual, there are two main research methods, i.e., literature review and the analysis of case studies. The first provides an overview of the existing knowledge and the second permits for contextualization of the proposed models. Beyond that, the paper includes definitions of the key concepts and enables to extend the knowledge base in the research area.


  • Anilkumar, R., Deshmukh, R.R., Emmanuel, M. (2017) Big Data Predictive Analysis for Detection of Prostate Cancer on Cloud-Based Platform: Microsoft Azure, Privacy and Security Policies. In Tamane, S., Kumar Solanki, V., Dey, N. (eds.) Privacy and Security Policies in Big Data. IGI Global, Hershey, 259-278 google scholar
  • Azarmi, B. (2016) Scalable Big Data Architecture, A practitioner’s guide to choosing relevant big data architecture. Springer NY google scholar
  • Belcastro, L., Marozzo, F., Talia, D., Trunfio, P. (2017) Big Data Analysis on Clouds. In Zomaya A.Y., Sakr S. (eds.) Handbook of Big Data Technologies. Springer Cham, 101-142. google scholar
  • Chen, M., Mao, S., Zhang, Y., Leung, V.M. (2014) Big Data, Related technologies, challenges and future prospects. Springer, Cham Heidelberg. google scholar
  • Chi, C-H.(2015) Behaviour Informatics: Capturing Value Creation in the Era of Big Data. In Intan, R., Chi, C-H., Palit, H.N., Santoso, L.W. (eds.) Intelligence in the Era of Big Data. Springer Verlag Berlin, XIV-XVI. google scholar
  • Cupoli, P., Earley, S., Henderson, D. (2014) DAMA -DMBOK2 Framework, The Data Management Association. Accessed July 12, 2019. google scholar
  • Dong, X.L., Srivastava, D. (2015) Big Data Integration. Morgan & Claypool Publishers, Waterloo. google scholar
  • Gupta, M., Singla, N. (2017) Evolution of Cloud in Big Data With Hadoop on Docker Platform. In Tamane, S., Kumar Solanki, V., Dey, N. (eds.) Privacy and Security Policies in Big Data. IGI Global, Hershey, 41-64. google scholar
  • Heisterberg, R., Verma, A.(2014) Creating Business Agility, How Convergence of Cloud, Social, Mobile, Video, and Big Data Enables Competitive Advantage. Wiley, Hoboken. google scholar
  • ISO/TS 8000-1:2011, Data quality –Part 1:Overview, Accessed July 12, 2019. google scholar
  • ISO/TS 8000:150:2011, Data quality –Part 150: Master data: Quality management framework, https://www.iso. org/standard/54579.html. Accessed July 12, 2019. google scholar
  • ISO 8000-2:2018 Data quality –Part 2: Vocabulary, Accessed July 12, 2019. google scholar
  • Kaplan, R.S., Norton, D.P. (2004) Strategy Maps, converting intangible assets into tangible outcomes. Harvard Business School Press, Boston. google scholar
  • Krishnan, K.(2013) Data Warehousing in the age of Big Data. Morgan Kaufmann, Elsevier, Amsterdam. google scholar
  • Melgarejo Galvan, A.R., Rocio Clavo Navarro, K. (2017) Big Data Architecture for Predicting Churn Risk in Mobile Phone Companies. In Lossio-Ventura, J.A., Alatrista-Salas, H. (eds.) Information Management and Big Data. Springer Heidelberg, 120-133. google scholar
  • Morabito, V. (2015) Big Data and Analytics, Strategic and Organizational Impacts. Springer Cham. google scholar
  • Plotkin, D. (2014) Data Stewardship, An Actionable Guide to Effective Data Management and Data Governance. Elsevier, Amsterdam. google scholar
  • Quix, Ch., Hai, R. (2019) Data Lake. In Sakr, S., Zomaya, A.Y (eds.) Encyclopedia of Big Data Technologies. Springer Nature, Cham, 552-559. google scholar
  • Schmarzo, B. (2013) Big Data, Understanding How Data Powers Big Business. Wiley, Indianapolis. google scholar
  • Smallwood, R.F. (2014) Information Governance. John Wiley and Sons, Hoboken. google scholar
  • Stimmel, C.L. (2015) Big Data Analytics Strategies for the Smart Grid. CRC Press, Taylor & Francis Group, London. google scholar
  • Unhelkar, B. (2018) Big Data Strategies for Agile Business, Framework, Practices and Transformation Roadmap. CRC Press, Boca Raton, London. google scholar
  • Van Grembergen, W., DeHaes, S. (2008) Implementing Information Technology Governance, Models, Practices, and Cases. IGI Publishing, Hershey, New York. google scholar
  • van Helvoirt, S., Weigand, H. (2015) Operationalizing Data Governance via Multi-level Metadata Management. In Janssen, M., Mäntymäki, M., Hidders, J., Klievink, B., Lamersdorf, W., van Loenen, B., Zuiderwijk, A. (eds.) Open and Big Data Management and Innovation. Springer, Cham, 160-172. google scholar


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