A Capability Approach for Designing Business Intelligence and Analytics Architectures

Business Intelligence and Analytics (BIA) is subject to an ongoing transformation, both on the technology and the business side. Given the lack of ready-to-use blueprints for the plethora of novel solutions and the ever-increasing variety of available concepts and tools, there is a need for conceptual support for architecture design decisions. After conducting a series of interviews to explore the relevance and direction of an architectural decision support concept, we propose a capability schema that involves actions, expected outcomes, and environmental limitations to identify fitting architecture designs. The applicability of the approach was evaluated with two cases. The results show that the derived framework can support the systematic development of fundamental architecture requirements. The work contributes to research by illustrating how to capture the elusive capability concept and showing its relation to BIA architectures. For further generalization, we created an open online repository to collect BIA capabilities and architectural designs

ArchiCap – A tool for capability-based IT architecture exploration

IT architects often face high uncertainty and a lack of methodical support when it comes to architectural decisions in emerging IT environments. Here, a capability-based approach can help to ensure business-orientation and strategic alignment. However, in practice, it turns out to be difficult to identify relevant capabilities and adequate architectural possibilities. The prototype presented in this proposal constitutes a web-based software tool to support the exploration of capabilities and link them to architectural decisions. For this, the software allows (i) the definition of environmental setups for certain application areas (e.g. capabilities and architectural possibilities for the internet of things) and (ii) the application to certain use cases (i.e. the analysis of the IT landscape and the exploration of relevant capabilities and architectural possibilities). The prototype is part of overarching research and was developed and used to explore the area of distributed analytics systems

Cloud-Based Business Intelligence and Analytics Applications – Business Value and Feasibility

In several application domains, Cloud Computing has been established as an accepted IT sourcing alternative. The usage in more sophisticated areas like Business Intelligence and Analytics (BIA) is, however, still in its infancy. The presented research aims at carving out viable application scenarios for Cloud BIA and at analyzing them regarding their potential business value and feasibility. The scenarios are derived from a case study and are further explored quantitatively with a survey of BIA experts. The results indicate that while there is an interest in Cloud-based BIA solutions, it is mostly directed towards self-contained and simple front-end driven solutions. Furthermore, the study highlights the need for a broader perspective on the subject of Cloud BIA that also considers issues of organizational and technical compatibility. The findings contribute to BIA research by gathering insights into the adaption of Cloud BIA. For business practice, the results support a more differentiated approach towards integrating Cloud technologies into the BIA landscape

FROM DATA WAREHOUSES TO ANALYTICAL ATOMS – THE INTERNET OF THINGS AS A CENTRIFUGAL FORCE IN BUSINESS INTELLIGENCE AND ANALYTICS

For decades, the Data Warehouse (DW) has survived as a central architecture component in Business Intelligence and Analytics (BIA) landscapes despite a number of alternative concepts and continuous forces towards more decentralized structures. However, distributed concepts recently seem to have grown in relevance. This paper investigates causes and consequences of an increasing decentralization in order to derive recommendations for future BIA architectures. We have conducted a literature review for identifying relevant application drivers, challenges and building blocks for BIA solutions. The results suggest that particularly Internet of Things (IoT) applications drive federated analytical “ecosystem” solutions. In two case studies from the realm of Advanced Manufacturing we deepen the respective insights. On the one hand, our results underscore the business potential of integrated BIA solutions for IoT applications. On the other hand, the applications introduce a set of particular challenges that can be mapped to DW characteristics, mainly regarding data history, integration, administration, and governance. Based on the results, we project the identified trends into the future and suggest a federated, platform-based solution with “Analytical Atoms”