Organizations are attempting to harness the power of big data analytics. Enterprise architecture can be used as an instrument to integrate big data analytics into the existing IT landscape and enabling the development of capabilities to create value from these technologies. Yet, there is limited research about the role of enterprise architecture in adopting big data analytics. This paper explores enterprise architecture roles and capabilities for the adoption of big data analytics by conducting a qualitative case study at the Dutch Tax and Customs Administration. The first attempt to adopt big data analytics was focused on integrating analytics into the current complex IT landscape, but this encountered many challenges and resulted in slow progress. To overcome these challenges, a separate department was created to quickly harness the potential of big data analytics. Enterprise architecture was used for impact analysis and to create a transition process. The findings suggest that enterprise architecture was used in different ways at the various stages of adoption and implementation, requiring different roles and a different set of capabilities. Enterprise architecture was found to be contingent on the type of technology and the situation at hand. We recommend more research into the role of the context in enterprise architecture research.

Enterprise Architecture (EA) has been embraced by many organizations to improve the value of their IT. Our systematic literature review (SLR) reveals that EA is a broad concept that is interpreted and used in many different ways. This breadth can be explained by the various starting points taken, and by the content-dependent nature of many EA efforts. Unsurprisingly, the literature presents diverse views on value creation and locates the value of EA in a broad range of areas. Only half of the articles provide empirical evidence supporting the EA value claims. Frequently, values are assumed to be the result of EA efforts, but many alternative explanations are possible. Based on the SLR findings, we identify EA myths that are attributable to an overly simplistic conceptualization of EA. These myths have their basis in the claim that EA is an instrument that can solve almost any kind of enterprise problem. This fails to acknowledge that EA in itself often does not provide value, but is an instrument enabling the creation of value. Based on our findings, we recommend demystifying EA by analysing the context-dependent mechanisms behind EA that result in value creation and developing rigorous evidence-based approaches to better understand EA.