Start Date
11-12-2016 12:00 AM
Description
The business insights that could be generated through the use of data analytics is one of the key motivations for firms to invest in such technologies. However, there is limited understanding of the required conditions for these tools to increase valuable insight generation. This research-in-progress study draws on the gestalt theory of insight and resource-based view of the firm to investigate the role of data analytics in generating valuable insights within organizations and the factors that influence this process. Particularly, this study focuses on how the bigness of data (i.e., volume, variety, and velocity), quality of data, and employees’ competency (i.e., analytical skills, and domain knowledge) impact insight generation (i.e., data hindsight, data insight, and data foresight) through the use of data analytics. A survey-based methodology is outlined to empirically validate the proposed model using structural equation modeling techniques. Potential contributions to theory and practice are also discussed.
Recommended Citation
Ghasemaghaei, Maryam; Ebrahimi, Sepideh; and Hassanein, Khaled, "Generating Valuable Insights through Data Analytics: A Moderating Effects Model" (2016). ICIS 2016 Proceedings. 7.
https://aisel.aisnet.org/icis2016/ITImplementation/Presentations/7
Generating Valuable Insights through Data Analytics: A Moderating Effects Model
The business insights that could be generated through the use of data analytics is one of the key motivations for firms to invest in such technologies. However, there is limited understanding of the required conditions for these tools to increase valuable insight generation. This research-in-progress study draws on the gestalt theory of insight and resource-based view of the firm to investigate the role of data analytics in generating valuable insights within organizations and the factors that influence this process. Particularly, this study focuses on how the bigness of data (i.e., volume, variety, and velocity), quality of data, and employees’ competency (i.e., analytical skills, and domain knowledge) impact insight generation (i.e., data hindsight, data insight, and data foresight) through the use of data analytics. A survey-based methodology is outlined to empirically validate the proposed model using structural equation modeling techniques. Potential contributions to theory and practice are also discussed.