Exploring Mid-Market Strategies for Big Data Governance

Abstract

Many data scientists struggle to adopt effective data governance practices as they transition from traditional data analysis to big data analytics. This qualitative multiple case study explored big data governance strategies used by data scientists employed in 3 mid-market companies in the greater Salt Lake City, Utah area who have strategies to govern big data. Data were collected via 10 semi-structured, in-depth, individual interviews and analysis of 4 organizational process documents. Four major themes emerged from the study: ensuring business centricity, striving for simplicity, establishing data source protocols, and designing for security. One key recommendation from the findings 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 be more profitable by gaining competitive advantages. By implementing strategies relating to the segregation of duties, encryption of data, and personal information, data scientists can mitigate contemporary concerns relating to using private information in big data analytics.