Date of Conferral



Doctor of Information Technology (D.I.T.)


Information Systems and Technology


Jodine M. Burchell


Many data scientists are struggling to adopt effective data governance practices as they transition from traditional data analysis to big data analytics. Data governance of big data requires new strategies to deal with the volume, variety, and velocity attributes of big data. The purpose of this qualitative multiple case study was to explore big data governance strategies employed by data scientists to provide a holistic perspective of those data for making decisions. The participants were 10 data scientists employed in multiple mid-market companies in the greater Salt Lake City, Utah area who have strategies to govern big data. This study’s data collection included semi-structured in-depth individual interviews (n = 10) and analysis of process documentation relating to big data governance in those organizations (n = 4). Through thematic analysis, 4 major themes emerged from the study: ensuring business centricity, striving for simplicity, establishing data source protocols, and designing for security. One key recommendation for data scientists is to minimize the data noise typically associated with big data. Implementing these strategies can help data scientists’ transition from traditional to big data analytics, which could help those organizations to be more profitable by gaining competitive advantages. The strategies outlined in this study can lead to positive social change by proactively addressing the ethical use of personally identifiable information in big data. By implementing strategies relating to the segregation of duties, encryption of data, and personal information, data scientists can mitigate contemporary concerns relating to the use of private information in big data analytics.