The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data.
The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards.
The authors’ model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework.
The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model.
Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework.
The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.
The authors have copyright permission and release for publication of this significantly expanded and revised article from the Journal of Applied Business and Economics and the Production and Operations Management Society Conference Proceedings.
Bumblauskas, D., Nold, H., Bumblauskas, P. and Igou, A. (2017), "Big data analytics: transforming data to action", Business Process Management Journal, Vol. 23 No. 3, pp. 703-720. https://doi.org/10.1108/BPMJ-03-2016-0056Download as .RIS
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