The rise of big data has led to many new opportunities for organizations to create value from data. However, at the same time the increasing dependence on data poses many challenges for organizations in managing data analytics activities. For example, data analytics activities are fragmented across the organization resulting in incompatible outcomes. This inhibits the organization from gaining full potential of their data analytics activities. To overcome these challenges organizations have to implement governance for their data analytics activities. IT and Data Governance literature shows that governance can be implemented through several types of governance mechanisms: structural, procedural and relational mechanisms. However, the literature is not very abundant when it comes to describing these mechanisms. Therefore, there is a need to identify data analytics governance mechanisms to better understand how data analytics governance can be achieved. To this end, a literature review was conducted to identify a preliminary framework. The framework was validated, and extended, in three case studies by identifying practical implementations of governance mechanisms. It resulted in an extended reference framework for data analytics governance describing several structural, process and relational mechanisms. This framework can assists managers in designing data analytics governance mechanisms for their specific organization.
Baijens, Jeroen; Helms, Remko W.; and Velstra, Tjeerd, "Towards a Framework for Data Analytics Governance Mechanisms" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
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