Towards Bridging the Gap Between BDA Challenges and BDA Capability: A Conceptual Synthesis Based on a Systematic Literature Review

Big data analytics (BDA) and strategies for implementing BDA have received attention among researchers and practitioners alike. However, success stories pertaining to the implementation of BDA remain scarce. The notion of the BDA deployment gap describes the chasm between the attributed value potential of BDA and its actual value realization in organizational practice. Several research articles indicate challenges encountered in implementing BDA but lack a comprehensive systematization of BDA implementation-related challenges. This research article aims to systematize those challenges through a systematic literature review. As a result, we derived five overarching challenge dimensions related to the BDA implementation. Based on this systematization, we adopt the lens of a big data analytics capability and delineate future research avenues through the derivation of propositions on how to overcome the BDA implementation-related challenges, while enhancing our understanding about how to solve the BDA deployment gap

Exploring the Applicability of Test Driven Development in the Big Data Domain

Big data analytics and the according applications have gained huge importance in daily life. This results on the one hand from their versatility and on the other hand from their capability to greatly improve an organization’s performance when utilized appropriately. However, despite their prevalence and the corresponding attention through practitioners as well as the scientific world, the actual implementation still remains a challenging task. Therefore, without the adequate testing, the reliability of the systems and thus the obtained outputs is uncertain. This might reduce their utilization, or even worse, lead to a diminished decision-making quality. The publication at hand explores the adoption of test driven development as a potential approach for addressing this issue. Subsequently, using the design science research methodology, a microservice-based test driven development concept for big data (MBTDD-BD) is proposed. In the end, possible avenues for future research endeavours are indicated

Understanding Issues in Big Data Applications – A Multidimensional Endeavor

The amount of data to be produced and analyzed is increasing year by year. As a result, the concept of big data gained interest among researchers and practitioners. However, a plethora of challenges and potentials require the attention from researchers and practitioners to enhance the future development. Apart from the pure processing of the data and its occurring obstacles, also other dimensions need to be considered in this context. This includes the technical planning of the related systems as well as the human interaction with them. When it comes to the strategic design, development, deployment and use of big data systems, especially the aspect of potential issues is often underestimated and less researched. Hence, in this contribution a comprehensive investigation on the various dimensions of big data under a quality assurance perspective is performed. Consequently an overview about the current state of the art and promising solutions are presented, providing a foundation for the future work of practitioners and researchers