Conceptual Framework for Implementing Temporal Big Data Analytics in Companies

Abstract

Considering the time dimension in big data analytics allows for a more complete insight into the analyzed phenomena and thus for gaining a competitive advantage on the market. The entrepreneurs also reported the need for temporal big data analytics, when interviewed by the author. Hence, the main goal of this article is to create a conceptual framework for applying temporal big data analytics (TBDA) in businesses. It is determined that a temporal framework is required. Existing big data implementation frameworks are discussed. The requirements for the successful implementation of temporal big data analytics are shown. Finally, the conceptual framework for organizational adoption of temporal big data analytics is offered and verified. The most important findings of this study are: proving that effective implementation of big data analytics in companies requires open consideration of time; demonstrating the usefulness of the leagile approach in the implementation of TBDA in companies; proposing a comprehensive conceptual framework for TBDA implementation; indicating possible success measures of the TBDA implementation in the company. The study has been conducted according to the Design Science Research in Information Systems (DSRIS) methodology. IT, business leaders, and policymakers can use the findings of this article to plan and develop temporal big data analytics in their enterprises. The report provides useful information on how to implement temporal big data in companies.