The architectural design of big data for business enhancement in enterprises

Abstract

Increasingly, organisations rely on big data for their business continuity, competitiveness, and sustainability. However, in many organisations, the rapidity at which the big data are generated, retrieved, and used is unprecedented. This contributes to complexity and challenges in many organisations. As a result, some organisations struggle to use big data, to improve business continuity and competitive advantage. Primarily, the complexity and challenges exist because there is no architecture, to govern and manage the big data. In an organisation where there is architecture, it is specifically designed for small data. Big data and small are not the same, hence, the same architecture cannot be used. Thus, organisations need to design an architecture that focuses on big data. This is the motivation for this study. The design of architecture for big data was problematised, where the implications and consequences of lack of architecture are stated. For example, it states, that a lack of architectural design for big data compromises the quality including management and governance of big data in an organisation. Based on the problem, the study aimed to design big data architecture, which enterprises can employ, to enhance business continuity and advance competitiveness. This includes the objectives of the study. In achieving the aim and objectives, research questions were formulated, as presented in Chapter 1. Qualitative methods were employed. Data were extracted from relevant literature, which were gathered from various sources, from both academic and business domains. A total of 201 papers were gathered. Activity theory (AT) was employed to guide the analysis of the data in which the hermeneutics technique was applied. From the analysis, the factors that influence the design of big data architecture were revealed. The factors were interpreted following the subjective reason approach. Based on the interpretation, big data architecture was developed, as presented in Chapter 5 (Figure 5.2.). The study was evaluated, to ensure that the study achieved its aim and objectives. The study is significant and contributes to both business and academics, from technical and non-technical perspectives. This includes the engineering of big data, governance, and management standpoints. The significance and contributions of the study are discussed in Chapter 6.