Business Intelligence for New-Generation Managers

Business intelligence (BI) supports the decision making according to strategic and operational management tasks. However, there is no systematic classification so that a decision making can be guided by conformable and well understandable BI topics regarding strategic or operational management activities. A literature review is conducted to identify publications on strategic and operational BI. The classification follows a single word and word group analysis supported by text mining. The results present the trends, along with their interpretation, and a discussion of BI in the context of strategic or operational decision making. The findings identify mature BI literature contributing to strategic decision making. The chapter implicates avenues of future research in context of operational management activities.

Business Intelligence has “decision support” as a characterizing element. Decisions are done as a selection process based on alternatives. The choice depends on prospective developments whereby those developments are predicted with uncertainty. Due to this reason, forecasts are getting more into focus of the strategic and tactical level. But forecasts, usually based on Knowledge Discovery in Databases (KDD), are limited, yet. They often produce non-adequate results, which can lead to wrong decisions. Such a forecast quality demands further research in identifying improvements to increase reliability of forecast results and its usage in practice. This chapter modifies the Knowledge Discovery in Databases to improve the forecast quality. The associated process is supplemented by further steps to enhance the analyzed data set with additional future oriented data by using the KDD markup language. First results of an evaluation implementation at a German saving and loans bank shows motivating results.

Managers in today’s trade companies need information management tools that provide them with relevant information in a flexible way. This chapter addresses this need by proposing a performance measurement system particularly suited to managers’ information needs in the German trade sector. Their information needs are determined by applying the success factor method and by analyzing empirical success factor research on the German trade sector. According to the identified information needs, the trade scorecard (TSC) is proposed, a performance measurement system for managers in the German trade sector. The TSC is then implemented as a prototype in a business intelligence environment that supports flexible, real-time access to company data.

The increasing volatility of their companies’ environment is a growing concern for executives. Environmental scanning systems should enable them to focus earlier on emerging threats and opportunities. A lack of applicability means that concepts often go unused in practice. But what does applicability mean for environmental scanning systems design? Adhering to the design science paradigm, this article contributes to better information systems (IS) design by developing a systematic list approach to requirements criteria that specify the applicability of environmental scanning systems. The criteria are derived from the principle of economic efficiency, use findings from the absorptive capacity theory, and can be applied to both evaluate existing environmental scanning systems and develop a new, more applicable generation. The findings should also be applicable to other IS domains.

Management control systems (MCS) facilitate managers’ decision making. At their core, MCS consist of corporate planning and reporting capabilities that rely on IT and non-IT assets. Recently, both researchers and practitioners have paid increasing attention to use maturity models (MM) for designing and using such capabilities effectively and efficiently. Although MMs are well-established and easy to apply, they lack a theoretical foundation and IS research focuses rather on their development process than on using them to create individual IT-enabled capabilities for MCS. We address this research gap by developing a method to systematically adjust MMs from the knowledge base to firm-specific business needs. Findings from the resource-based view (RBV) guide our development process. With an interpretative case study in the chemical sector we demonstrate the applicability of our method. We present a list of IT and non-IT assets that are necessary for our case company to develop individual IT-enabled planning and reporting capabilities. Information systems research benefits from our findings as we translate the RBV into action. Practice benefits from an individual view on their IT-enabled capabilities and we force managers to jointly consider their IT and non-IT assets when they are designing IT-enabled capabilities for their company.

Big Data is an emerging research topic. The term remains fuzzy and jeopardizes to become an umbrella term. Straight forward investigations are inhibited since the research field is not well defined, yet. To identify a common understanding, experts have been interviewed. Hereby, the findings are coded and conceptualized until a descriptive Big Data model is developed by using Grounded Theory. This provides the basis for the model’s deployment. Here, academic publications and practical implementations marked as Big Data are classified. It becomes evident that Big Data is use-case driven and forms an interdisciplinary research field. Even not all papers belong to this research field. The findings become confirmed by the practical implementations. The chapter contributes to the intensive discussion about the term Big Data in illustrating the underlying area of discourse. A classification to set the research area apart from others can be achieved to support a goal oriented research in future.

The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate insights into the multi-facetted phenomenon of ESN use. In order to address this issue, we present a framework of available data dimensions and describe possible methods and insights as well as opportunities and limitations of each data dimension. We then adopt this framework exemplary to comprehensively analyze an empirical ESN case.

New-generation managers are increasingly populating organizations’ management. They consist of digital natives who grew up with Information Systems (IS) and digital immigrants who learned to engage with IS as adults. Today, such managers have to make faster decisions than in the past and find themselves more and more in mobile IS use situations. These requirements combined with managers’ ability to use IS themselves result in the need for self-service Management Support Systems (MSS). This article develops a more business-driven design for such MSS. In doing so, we propose both a rigorous set of requirements and initial design guidelines to start further discussion. The utility of these guidelines is demonstrated with a “mobile-first” prototype on a modern business intelligence platform: the Corporate Navigator app.