Big data maturity model: assessing organizational readiness for the new era of data analytics
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Today, in the era of Big Data, many organizations around the world realize that the ability to analyze and use big and complex data sets and data streams is becoming one of the most important sources of competitive advantage. Not surprisingly, Big Data capabilities are becoming essential for the survival and success for both private and public organizations. In order for organizations to take full advantage of opportunities that Big Data offers, they need to reach a new level of maturity with respect to data analytics. Some researchers view Big Data as a natural extension of Business Intelligence (BI). Because of that, the current Big Data maturity models are still grounded in the BI era. But the models that have been used for evaluating organizational maturity in relation to BI cnnot be used to evaluate organization maturity in relation to Big Data due to the difference in the underlying processes, methodologies, and technologies. This dissertation highlights the theoretical differnces between BI and Big Data and empirically explores the key Big Data capabilities via a newly proposed Big Data Maturity Model populated by specific Big Data capabilities at various levels of maturity. The main goal of this research it to develop a practical model that is suitable for evaluating maturity of various organizations in relation to Big Data. The development of the maturity model goes through two main phases. The first iv phase is the literature review phase. The iterature review highlights the limitations of existing maturity models in relation to Big Data and then uses the Socio-Technical Perspective (STP) and the Resource-Based View (RBV) as its primary theoretical lenses to identify the key capabilities required for working with Big Data. These capabilities fall into the people, technology, and organizational domain. The second phase involves using the developed theoretical model to empirically explore Big Data capabilities among various organizations. The preliminary results suggest that the Big Data model can be used to discriminate among organizations at various levels of maturity in relation to Big Data. In addition to proposing a preliminary theoretical model for nderstanding what it takes to build Big Data capabilities, this research also provides practical guidance to organizations wishing to improve their Big Data capabilities for the purpose of improving their organizational erformance and gaining competitive advantage over their rivals.