Project risk management has been proposed as an important topic to prevent the failure of large IT projects. But while literature intensively deals with the risk management process, surprisingly little effort has been put into "the last mile", namely the precise, concise, and unambiguous communication of risks to decision makers. Popular misinterpretations of traffic light-based reports confirm this call for action. Hence, we propose a prototype for information-dense IT project risk reporting including a novel risk report design. This prototype has been designed and applied in a large-scale IT project building on the Action Design Research approach. By drawing on design principles derived from both academic literature and practical experience, we show the advantages of this design compared to traditional IT project risk reports used in business practice. We conclude with an outlook on future work such as the prototype's evaluation in an experimental setting.

While it is common practice to use value based decision models for decisions whether to invest in certain projects or not, there is scarce value based decision support for the selection of the most promising project management methodology to be applied in a specific Business Intelligence project. Addressing the lack of a formal yet practical decision model, this paper proposes a risk-adjusted net-present-value-based model to support decision makers in this particular decision situation. Apart from a project’s estimated cash flows, we focus on two decisive risk parameters – the likelihood of environmental changes and the peril of improper system integration. Using an exemplary calculation, the trade-off between those risks and their impact is formalized and made transparent. Therefore, this paper suggests a decision model to improve the understanding of project management methodology types and hereupon the foundation for the selection of the appropriate one in a specific project setting.

Lacking a formal yet practical decision model, nowadays decision makers mostly follow corporate guidelines or their intuition when it comes to the decision between agile and plan-driven project management in Business Intelligence projects. As one size does not fit all, using management methods hyped by temporary fashion or other management methods not adapted to the situation bears the risk of project failure. Thus, this paper proposes a risk-adjusted net present value-based model to support decision makers in the selection of the appropriate management method for Business Intelligence projects. We focus on two decisive risk parameters – the likelihood of environmental changes and the peril of improper system integration – and a project’s estimated cash flows. As a result, the tradeoff between different characteristics of risks and cash flows in a specific project is formalized and made transparent. In summary, this research-in-progress paper sketches the idea of a practical decision model that improves the foundation for the selection of the appropriate management method.

Ensuring adequate information provision continues to be a key challenge of corporate decision making and the usage of business intelligence systems. As a matter of fact, the situation becomes increasingly paradox: Whereas decision makers struggle to specify their information requirements and spend much time on obtaining the information they believe to require, the amount of information supplied by business intelligence systems grows at a speed that makes it hard to keep track. Thus, it is very likely that the required information or suitable alternatives are available, but neither found nor used. Instead, manual searching causes considerable opportunity cost. Existing approaches to information requirements analysis pay attention to incorporate information supply, but do not provide means for leveraging it in a systematic and IT supported manner. As a first step to close this research gap, we propose a metadata-based approach consisting of a procedure model and formalism that help identify a suitable subset of the information supplied by an existing business intelligence system. The formalism is specified using set theory and first-order logic to provide a general foundation that may be integrated into different conceptual modelling approaches.

Despite valuable related work, identifying relevant information requirements of decision makers is still a key issue in developing analytical information systems. Since measures build a major basis for managerial decision making, discovering the objectively most important measures is crucial to reduce information overload and improve decision quality. Therefore, a method is proposed that helps decision makers to identify and to prioritize their measure-based information needs using the system dynamics methodology. As a result, objectively needed and subjectively believed to be needed information requirements are aligned. The applicability is exemplarily demonstrated using an existing system dynamics model.

In dynamic business contexts where knowledge is continually evolving and thus critical for better organizational performance, not only knowledge re-use but also knowledge re-creation becomes more and more important. One of these contexts is the use of non-renewable resources in innovative hightech products. Since media recently spread – often contradictory – news about the increasing scarcity of non-renewable resources, decision makers face a high degree of uncertainty. They struggle to understand and handle the information available. Therefore, it is essential to provide a methodological approach to externalize and combine expert knowledge of a system’s inherent logic. Hence, in this paper we show that mental models of experts can be transformed into an explicit simulation model in order to support decision makers comprehending the shortand long-term dynamic interdependencies of the development of non-renewable resources on demand, supply, and price. For this purpose, we combine known cause-and-effect relationships into an integrated model using the system dynamics methodology. The application of the idea to capture knowledge in a simulation model is exemplarily instantiated with real-world information for the case of indium.