eThesis - School of Business electronic theses

Advanced analytics methods and big data technologies can create a foundation for new competitive advantage and provide significant efficiency gains. Even though underlying technologies and methods have been available for more than a decade, adoption of advanced analytics is still a risky technological investment that does not necessarily lead to widespread organizational transformation. By applying affordance perspective to identify key organizational and technological barriers and prerequisites, and by analyzing relationship and dependencies between them, this research aims to develop a strategic framework for analytics adoption that provides organizations with a holistic view on actions and changes that needs to be done in order to realize full potential of advanced analytics and big data capabilities.

The research of this thesis was designed as an exploratory study that follows the grounded approach methodology. Following a comprehensive literature review, seventeen semi-structured interviews with field experts from Finnish companies were conducted. The collected qualitative data was analyzed using the thematic analysis approach combined with the thematic network technique for better systemization and representation of relationships grounded in the data.

Findings of this research suggest that despite the importance of technological aspects, analytics and big data adoption should be business-driven rather than technological-driven exercises. In order to enable new organizational function offered by advanced analytics technologies and practices, companies would need to conduct organizational and cultural change, and acquire set of rare skills and capabilities in order to extract valuable insights from the data. The framework developed in this thesis comprises the system of interconnected technological and organizational barriers and prerequisites, and provide a set of strategic actions to address these. Hence, by applying the framework organizations can avoid main pitfalls of the analytics adoption process and focus on implementation of required capabilities. Furthermore, this research noted a limitation of the affordance concept in order to explain evolution in offered capabilities in different levels of analytics maturity. To address this limitation, a new concept of acquired affordance was proposed that includes an additional property, i.e. progression. This new property explains how enactment with an object or an artifact actor would develop her characteristics further to enable a new level of affordance that afford a new function.